Phylogeny and Biogeography of Gibbons, Genus Hylobates.
by Helen Jane Chatterjee Department of Biology, University College London
A thesis submitted for the fulfilment of the Degree of Doctor of Philosophy University of London, 2000 ProQuest Number: U643055
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This thesis aims to reconstruct the phylogenetic and biogeographic history of gibbons, genus Hylobates. Phylogenetic relationships among gibbons are controversial. This study uses molecular and morphological data to resolve some of these controversies, and provides a new phylogeny for the genus. The estimate of gibbon phylogeny is combined with distribution data to reconstruct the biogeographic history of gibbons. In the first part of the study, original mitochondrial control region sequence data and published cytochrome b gene sequence data are analysed using maximum likelihood, parsimony and bootstrapping methods. Results of these analyses indicate a gibbon phylogeny which shows the following subgeneric relationships: Nomas eus, Symphalangus and Bunopithecus are successively more closely related to Hylobates. A molecular clock is employed which suggests that the gibbon radiation dates to about 10.5 million years ago (Ma). In the second part of the study, craniodental and postcranial metric variables are analysed using multivariate statistical methods to investigate interspecific morphological variability. Multivariate statistical analyses across the gibbon skeleton show that the eleven currently recognised species form five distinct morphological groups. The metric data are further employed to investigate phylogenetic inter relationships among gibbons. These data are converted into discrete character states using segment and range coding. The re-coded data are analysed using parsimony and bootstrapping methods. These analyses indicate that gibbon skeletal metric data is phylogenetically uninformative at the species or subgenus level. In the third part of the study, three alternative methods of biogeographic reconstruction are employed: ancestral area analysis using irreversible parsimony, ancestral area analysis using reversible parsimony and dispersal-vicariance analysis (DIVA). Results of the DIVA analysis are combined with estimates of divergence dates, to propose a new pattern of radiation for the genus Hylobates. This analysis indicates that the gibbon radiation may have begun in Indochina around 10.5 Ma. Between 10.5 and 8.6 Ma gibbons radiated southwards to Sumatra. Subsequently, they differentiated into two types of gibbon on Sumatra, representing the subgenera Symphalangus and Hylobates. A third radiation approximately 7-8 Ma saw the dispersal of Bunopithecus into Burma, Assam, and Bangladesh. At around 3-5 Ma there was a second radiation of subgenus Hylobates, involving dispersal onto the islands of Borneo, Mentawai and Java. Between 0.3 and 1.8 Ma taxa in the subgenus Nomascus differentiated into Cambodia and Hainan Island. This study provides a novel reconstruction of the historical biogeography of gibbons. In addition, parsimony and likelihood analyses of molecular and morphological data sheds light on the controversial topic of hylobatid phylogeny. Acknowledgements
I am indebted to my two supervisors Professor Leslie Aiello and Dr. Adrian Lister, for their unceasing support and encouragement, for their patience and guidance, and their enthusiasm. I am most sincerely grateful to Dr. Mark Collard and Dr. Mark Thomas for their teaching, time, and guidance. I wish to also acknowledge Dr Mark Thomas and Neil Bradman for giving me the opportunity to work in their laboratory. I am grateful to University College London (especially the Department of Biology) not least for their financial support, but also for giving me the opportunity to work in The Grant Museum of Zoology. The following people and institutions were especially generous in providing samples for the genetic part of this study: Dr. Malcolm Hall, formerly of the University of Liverpool. Dr. Ann Oakenfull, Department of Genetics, University of Cambridge, Dr. Volker Sommer, Department of Anthropology University College London, Dr. Andrew Kitchener, National Museums of Scotland, Marwell Zoo, UK, Twycross Zoo, UK, and the Mammals Section, Department of Zoology, British Museum of Natural History, London. I would like to say a special thank you to Malcolm Hall for his generous gift of numerous gibbon hair samples, his encouragement and several interesting discussions. I wish to thank Curators from the following Museums for providing access to their fantastic gibbon collections: the Natural History Museum, London (United Kingdom), the Rijksmuseum van Natuurlijke Histoire, Leiden (The Netherlands), the American Museum of Natural History, New York (United States of America), the Field Museum of Natural History, Chicago (United States of America), and the National Museum of Natural History, Smithsonian Institute, Washington DC. (United States of America), and the Department of Anatomy, UCL (UK). For exchange of correspondence and/or discussions, plus much useful information, I would like to thank Drs. Colin Groves, Nina Jablonski, Ian Barnes, Doug Brandon-Jones and Marcello Ruta. I am especially grateful to Dr. Marcello Ruta for his time, teaching and encouragement. For numerous lively discussions, unceasing support and encouragement I wish to thank the following friends and colleagues: Paul Davies, Sarah Collinge, Margaret Clegg, Julia Day, Marcello Ruta, Sam Cobb, Claire Imber, Helen Wood, Will Harcourt- Smith, Hartley Odwak; plus, Jo, Gillie, Louise, Karen, Maya and everyone in the Department of Biology who has given me support and many kind words. I also wish to thank all of my friends outside of work who have provided fun, support and encouragement. There are many others to whom I am grateful for help, support and discussion, who are too numerous to mention; I am thankful to everyone who has helped me. Finally, I am grateful to my family: Martin, Betty, Nan, Ranjit, Andrew, Ashraf, Kumie, Shakeeb, Leyya, Zoë, Shanta and Ryan. Without their encouragement, commitment, love and moral support, I could not have completed this. Phylogeny and Biogeography of Gibbons, Genus Hylobates, Contents Page Numbers Title 1 A bstract 2 Acknowledgements 3 Contents 5 List of figures and tables 8
Chapter 1: Introduction and Background 15 1.1 Aims and objectives 16 1.2 Structure of the thesis 18 1.3 Gibbon systematics, evolution and geographic distribution 19 1.3.1 Introduction to gibbon systematics 19 1.3.2 Historical background to gibbon systematics 20 1.3.3 Geographic distribution of gibbons 27 1.3.4 Palaeontological and palaeoenvironmental background 36 1.4 Background to previous studies 45 1.4.1 Review of previous molecular studies 48 1.4.2 Review of previous morphological studies 56 1.4.3 Review of previous biogeographic studies 64 1.5 Summary 71
Chapter 2: Molecular Study 72 2.1 Introduction and background to study 73 2.1.1 The mitochondrial control region 75 2.1.2 Mitochondrial cytochrome b gene 77 2.2 Materials and methods 79 2.3 Phylogenetic analysis 85 2.4 Phylogenetic results and discussion 89 2.5 Conclusions 123 Chapter 3: Morphological Study 124 3.1 Introduction 125 3.2 Materials 126 3.3 Measurements 130 3.4 Methods of analysis 148 3.4.1. Multivariate statistical methods 148 3.4.2 Multivariate statistical results and discussion 154 3.4.3 Cladistic methods 192 3.4.4 Cladistic results and discussion 202 3.5 Conclusions 225
Chapter 4: Biogeographic Study 227 4.1 Introduction to Historical Biogeography 228 4.1.1 Dispersal Biogeography 231 4.1.2 Panbiogeography 232 4.1.3 Vicariance Biogeography 233 4.1.4 Cladistic Biogeography 234 4.1.5 Alternative biogeographic models - Réfugia 237 4.2 Materials and Methods 239 4.2.1 Ancestral area analysis using irreversible parsimony 244 4.2.1.1 Introduction to the method 244 4.2.1.2 The method 244 4.2.1.3 Results 247 4.2.2 Ancestral area analysis using reversible parsimony 248 4.2.2.1 Introduction to the method 248 4.2.2.2 The method 248 4.2.2.3 Results 250 4.2.3 Dispersal-vicariance analysis 252 4.2.3.1 Introduction to the method 252 4.2.3.2 The method 253 4.2.3.3 Results 259 4.3 Discussion 261 4.4 Conclusions 268 Chapter 5: Final discussion, conclusions and future research 269 5.1 Final discussion 270 5.1.1 Molecular approach to gibbon phylogeny 271 5.1.2 Morphological study 274 5.1.3 Gibbon biogeography 277 5.2 Concluding remarks 282 5.3 Future research 284
Appendices 286 Appendix I. Pelage Descriptions 287 Appendix II. Control Region sequence data 294 Appendix III. Segment and Range coded metric data matrices 296 Appendix IV. Non size-corrected (raw) cranial, dental and postcranial data 308
References 338 List of figures and tables Figures
Figure 1.1 Map of Southeast Asia ...... 30
Figure 1.2 Distribution of gibbon subgenera, after Geissmann (1995, p. 475) ...... 31
Figure 1.3 Distribution of species in the subgenus Nomascus, after Geissmann (1995, p.
477) ...... 32
Figure 1.4 Distribution of species in the subgenus Hylobates, after Geissmann (1995, p.
476) ...... 33
Figure 1.5 Reconstruction of the distribution of sea and land in SE Asia during the
Middle Miocene, 15 Ma. (Hall, 1998, p. 118) ...... 39
Figure 1.6 Reconstruction of the distribution of sea and land in SE Asia during the Late
Miocene, 10 Ma. (Hall, 1998, p. 119) ...... 40
Figure 1.7 Reconstruction of the distribution of sea and land in SE Asia during the Early
Pliocene, 5 Ma. (Hall, 1998, p. 120) ...... 41
Figure 1.8 Most parsimonious tree based on cytochrome b gene sequence data,
Garza and Woodruff (1992) ...... 53
Figure 1.9 Consensus tree based on partial sequences of mitochondrial NADH
dehydrogenase genes, Hayashi et al. (1995)...... 54
Figure 1.10 Maximum likelihood and maximum parsimony trees, showing bootstrap
values, based on cytochrome b gene sequence data. Hall et al. (1998) ...... 55
Figure 1.11 Groves (1972) phylogeny of gibbons ...... 62
Figure 1.12 Creel and Preuschoft (1984) phylogeny of gibbons ...... 62
Figure 1.13 Haimoff et al. (1984) phylogeny of gibbons ...... 63
Figure 1.14 Groves proposed dispersal of gibbons in the subgenus Hylobates (1972,
p.46) ...... 67
Figure 1.15 Chivers (1977) Model for the evolution of gibbons ...... 68-70
8 Figure 2.1 Schematic diagram of the sections of mtDNA analysed in this study ...... 76
Figure 2.2 Maximum likelihood tree based on cytochrome b gene sequence data ...... 90
Figure 2.3 Maximum likelihood tree constructed under the assumption of a molecular
clock, based on cytochrome b gene sequence data ...... 93
Figure 2.4 Maximum parsimony consensus tree based on cytochrome b gene sequence
data...... 96
Figure 2.5 Bootstrap 50% majority rule consensus tree based on cytochrome b gene
sequence data ...... 98
Figure 2.6 Maximum likelihood tree based on control region sequence data. The same
topology was obtained for each of the control region alignments, CRl and CR2.101
Figure 2.7 Maximum likelihood tree constructed under the assumption of a molecular
clock, based on the control region 1 dataset ...... 103
Figure 2.8 Maximum likelihood tree constructed under the assumption of a molecular
clock, based on the control region 2 dataset ...... 104
Figure 2.9 Maximum parsimony tree based on the control region 1 dataset ...... 107
Figure 2.10 Maximum parsimony tree based on the control region 2 dataset ...... 108
Figure 2.11 Bootstrap 50% majority rule consensus tree constructed using the control
region 1 dataset ...... 111
Figure 2.12 Bootstrap 50% majority rule consensus tree constructed using the control
region 2 dataset ...... 112
Figure 3.1 Measurements of the skull (frontal view) ...... 136
Figure 3.2 Measurements of the skull (lateral view) ...... 137
Figure 3.3 Measurements of the skull (base view) ...... 138
Figure 3.4 Measurements of the mandible ...... 139
Figure 3.5 Measurements of the humerus ...... 140
Figure 3.6 Measurements of the ulna ...... 141
9 Figure 3.7 Measurements of the femur ...... 142
Figure 3.8 Measurements of the tibia ...... 143
Figure 3.9 Measurements of the scapula ...... 144
Figure 3.10 Measurements of the clavicle ...... 144
Figure 3.11 Measurements of the sternum ...... 145
Figure 3.12 Measurements of the pelvis ...... 146
Figure 3.13 Scatter plot of discriminant function 1 against discriminant function 2 for
the analysis of raw craniodental data ...... 157
Figure 3.14 Scatter plot of discriminant function 1 against discriminant function 3 for
the analysis of raw craniodental data ...... 158
Figure 3.15 Scatter plot of discriminant function 1 against discriminant function 2 for
the analysis of size-corrected craniodental data ...... 159
Figure 3.16 Scatter plot of discriminant function 1 against discriminant function 3 for
the analysis of size-corrected craniodental data ...... 160
Figure 3.17a Single-linkage hierarchical cluster analysis based on means of the first
three functions of the discriminant analysis using raw craniodental data 169
Figure 3.17b Single-linkage hierarchical cluster analysis based on means of the first
three functions of the discriminant analysis using size-corrected craniodental
data...... 169
Figure 3.17c Average-linkage hierarchical cluster analysis based on all of the raw
craniodental data ...... 170
Figure 3.17d Average-linkage hierarchical cluster analysis based on all of the size-
corrected craniodental data...... 170
Figure 3.18 Scatter plot of discriminant function 1 against discriminant function 2 for
the analysis of raw postcranial data ...... 175
10 Figure 3.19 Scatter plot of discriminant function 1 against discriminant function 3 for
the analysis of raw postcranial data ...... 176
Figure 3.20 Scatter plot of discriminant function 1 against discriminant function 2 for
the analysis of size-corrected postcranial data ...... 177
Figure 3.21 Scatter plot of discriminant function 1 against discriminant function 3 for
the analysis of size-corrected postcranial data ...... 178
Figure 3.22 Bivariate plot of femur length against tibia length based on size-corrected
postcranial data ...... 182
Figure 3.23 Bivariate plot of pubic length against iliac height based on size-corrected
postcranial data ...... 183
Figure 3.24 Strict consensus of 11 equally parsimonious trees constructed using
segment coded cranial data ...... 204
Figure 3.25 Strict consensus of 50 equally parsimonious trees constructed using
segment coded dental data ...... 205
Figure 3.26 Strict consensus of 17 equally parsimonious trees constructed using
segment coded postcranial data ...... 206
Figure 3.27 Two equally parsimonious trees (T1 and T2) constructed using all.(cranial,
dental and postcranial) segment coded data ...... 207
Figure 3.28 Bootstrap 50% majority rule consensus tree constructed using segment
coded cranial data ...... 209
Figure 3.29 Two equally parsimonious trees (T3 and T4) constructed using range coded
cranial data ...... 212
Figure 3.30 Most parsimonious trees constructed using range coded dental data ...213
Figure 3.31 Most parsimonious trees constructed using range coded postcranial data. 214
Figure 3.32 Most parsimonious trees constructed using all (cranial, dental and
postcranial) range coded data ...... 215
11 Figure 3.33 Strict consensus of two equally parsimonious trees constructed using range
coded cranial and postcranial data ...... 216
Figure 3.34 Most parsimonious tree constructed using range coded cranial and dental
data...... 217
Figure 3.35 Most parsimonious tree constructed using range coded dental and
postcranial data ...... 218
Figure 3.36 Bootstrap 50% majority rule consensus tree constructed using range coded
cranial data ...... 220
Figure 4.1 Taxon area cladogram generated using the gibbon phylogeny estimated using
cytochrome b gene sequence data analysed under maximum likelihood assuming
a molecular clock (Figure 2.3) ...... 243
Figure 4.2 A simple example of ancestral area analysis using irreversible parsimony
(Bremer, 1992)...... 246
Figure 4.3 Expected biogeographic outcomes of spéciation under the DIVA hypothesis
(Ronquist, 1996) ...... 253
Figure 4.4 DIVA optimization of character areas ...... 258
Figure 4.5 Proposed pattern of the gibbon radiation ...... 267
12 Tables
Table 1,1 Schultz (1933) Classification of gibbons and siamangs ...... 20
Table 1.2 Morphological specialisation’s attributed to genus Symphalangus compared
to genus Hylobates, identified by Schultz (1933) ...... 24
Table 1.3 Classifications of the Genus Hylobates...... 25
Table 1.4 Characteristics distinguishing gibbon species identified by Groves (1972)
(adapted from Groves, 1972, p.81) ...... 26
Table 1.5 Geographic distribution of gibbon species ...... 29
Table 1.6 General pelage descriptions and commonly used vernacular terms for the
different gibbon species (Geissmann, 1993, 1995) ...... 47
Table 2.1 Source of cytochrome b gene sequence data ...... 78
Table 2.2 Origin of gibbon hair samples ...... 80-81
Table 2.3 Source of human and gibbon sequence data ...... 84
Table 2.4 Estimates of divergence dates for gibbons ...... 94
Table 3.1 Samples of hylobatid and human skulls measured in this study ...... 128
Table 3.2 Samples of hylobatid and human postcrania measured in this study ...... 129
Table 3.3 List of measurements and definitions, indicating the source of each variable,
the original variable number and the intraobserver error ...... 131-135
Table 3.4 Results of discriminant function analysis based on craniodental data...... 156
Table 3.5 Summary of post-hoc predicted group membership scores based on the results
of the DFA using raw craniodental data ...... 166
Table 3.6 Results of discriminant function analysis based on postcranial data ...... 174
Table 3.7 Summary of post-hoc predicted group membership scores based on the results
of the DFA using raw postcranial data ...... 185
Table 4.1 Area characters of gibbon species used in this analysis ...... 242
13 Table 4.2 Ancestral area analysis using irreversible parsimony (Bremer, 1992) applied
to the taxon area cladogram depicted in Figure 4.1 ...... 247
Table 4.3 Ancestral area analysis using reversible parsimony (Ronquist, 1994) applied
to the taxon area cladogram depicted in Figure 4.1 ...... 250
Table 4.4 Data matrix containing presence(0)/absence(l) information for the 14 area
characters and 11 gibbon species ...... 257
14 Chapter 1: Introduction and Background
Contents
Page numbers
1.1 Aims and obj ectives 16
1.2 Structure of the thesis 18
1.3 Gibbon systematics, evolution and geographic distribution 19
1.3.1 Introduction to gibbon systematics 19
1.3.2 Historical background to gibbon systematics 20
1.3.3 Geographic distribution of gibbons 27
1.3.4 Palaeontological and palaeoenvironmental background 36
1.4 Background to previous studies 45
1.4.1 Review of previous molecular studies 48
1.4.2 Review of previous morphological studies 56
1.4.3 Review of previous biogeographic studies 64
1.5 Summary 71
15 1 Introduction
1.1 Aims and Objectives
The key aim of this study is to reconstruct the phylogenetic and biogeographic
history of gibbons, Genus Hylobates. This will be achieved using a combination of
genetic and morphological data, and phylogenetic and historical biogeographic
reconstruction techniques. The study comprises three intrinsic aims: to assess genetic
variability among hylobatids using original mitochondrial control region sequence data
and published mitochondrial cytochrome b data, to assess morphological variability
among hylobatids using multivariate statistical methods and cladistic analysis, and to use these data to interpret phylogenetic and historical biogeographic relationships among hylobatids, both spatially and temporally.
The study employs well documented multivariate statistical and cladistic techniques, and uses the results of these analyses within an historical biogeographic framework. Historical biogeography aims to reconstruct the evolutionary history of taxa from a spatial and temporal viewpoint. Platnick and Nelson (1978), early architects of historical biogeography, describe the subject as “that field of inquiry which aims to answer the question “Why are taxa distributed where they are today?” (1978, p. 1).
Historical biogeography aims to reconstruct the pattern of radiation of taxa. The resultant patterns of radiation can then be used to interpret processes of spéciation, including competing theories of modes of spéciation, and theories of palaeoenvironmental change, that have influenced distributions.
Over the past two decades the field of historical biogeography has been revolutionised regarding the analytical tools employed to interpret the distribution patterns of organisms. This revolution has largely been the result of incorporating phylogenetic data into biogeographic analyses. This emergent field of historical
16 biogeography has become known as ‘cladistic’ or ‘vicariance’ biogeography. The key aim of cladistic biogeography is to assess whether cladograms of taxa are geographically congruent, by creating a so-called “area cladogram”. Construction of the area cladogram involves replacing the taxonomic names of species at the terminal end of each branch, with the geographic areas occupied by that species.
Three different biogeographic techniques are used to reconstruct the biogeographic history of gibbons: ancestral area analysis using irreversible parsimony
(Bremer, 1992), ancestral area analysis using reversible parsimony (Ronquist, 1994) and dispersal-vicariance analysis (Ronquist, 1997). These techniques differ regarding their treatment of vicariance, dispersal and extinction. Furthermore, in contrast to techniques which rely on testing congruence between two or more area cladograms derived from different groups of organisms occupying the same geographic area, these methods require only one area cladogram. This is particularly relevant in this study since a phylogeny for only one group of organisms, the gibbons, will be generated.
This study advances the current understanding of hylobatid phylogenetics and proposes a new scenario for the radiation of this genus. The gibbons are ideal subjects for both a phylogenetic and historical biogeographic study. There is continued debate regarding interspecific relationships among hylobatids and this study employs both morphological and molecular data in order to assess phylogenetic relationships. In addition, understanding of the biogeographic history which has given rise to the current distribution pattern of gibbons is very limited. Hence, phylogenetic information generated in the first part of this study is employed in three methodological approaches to reconstructing historical biogeography to propose a new scenario for the radiation of hylobatids.
17 1.2 Structure of the thesis
The study is presented in 5 chapters. In the following sections of this chapter, a background to the evolutionary history of gibbons is presented, including gibbon systematics, their geographic distribution, and the palaeo-record. Following this a review of previous molecular, morphological and biogeographic studies is provided.
Chapter 2 outlines the molecular study, including the materials and methods used, and results of the phylogenetic analysis using molecular sequence data. Chapter 3 outlines the morphological study. This comprises the materials and methods of analysis employed, and a discussion of the results of these analyses, including cladograms constructed using the morphological data. In chapter 4 a description of the methodological approaches involved in historical biogeography is provided, including details of the theoretical background to this discipline. This chapter also contains details and results of the biogeographic analyses employed in this study. The final chapter, 5, brings the results of the molecular, morphological and biogeographic studies together.
This chapter discusses the combined results of the analyses and the implications for hylobatid phylogeny and historical biogeography. At the end of chapter 5 the results of the study are summarised, the methods employed are appraised, and final conclusions are drawn.
18 1.3 Gibbon systematics, evolution and geographic distribution
1.3.1 Introduction to gibbon systematics
It is now widely accepted that gibbons and siamangs form the single genus
Hylobates, and that together with great apes and humans they form the monophyletic group Hominoidea (Marshall and Sugardjito, 1986; Groves, 1989; Geissmann, 1993).
However, there are two distinct controversies regarding gibbon systematics. The first is a taxonomic issue and relates to the number of gibbon species. The second area of controversy involves a debate over phylogenetic relationships among gibbons. There have been numerous studies which have attempted to deal with one or both of these issues. The studies of Schultz (1933), Groves (1972, 1984, 1989) and Haimoff et al.
(1982, 1984) were based on various features of internal and external anatomy; Frish
(1965, 1967) analysed hylobatid teeth; Fooden (1969) and Marshall and Sugardjito
(1986) coat colour; Creel and Preuschoft (1976, 1984) cranial variability; Geissmann
(1993, 1995) and Marshall and Sugardjito (1986) vocalisations; and Garza and
Woodruff (1992), Hayashi et al. (1995) Hall et al. (1996, 1998) DNA sequence data.
However, these have provided conflicting results regarding the number of genera, subgenera, species and subspecies and interrelationships among these groups. The situation is further confused by a lack of consistency in the definition of a species. As will be shown, different authors appear to have different definitions of species and species concepts.
19 1.3.2 Historical background to gibbon systematics
The history of hylobatid systematics has seen numerous nomenclatural changes.
The first gibbon to be published was given the name Homo lar in Linneaus’ ‘Systema
Naturae’ in 1771. Over the next two centuries, as new taxa were described, several other names appear in the literature representing different gibbons. The name
Hylobates, meaning “dweller in the trees” first appeared in the early 19th Century
(Nowak, 1991; Groves, 1972). Schultz (1933) and Groves (1972) provide useful reviews of the classification systems published by other authors.
The work of Adolph Schultz in the first part of this century contributed greatly to the understanding of interspecific variability among hylobatids. This was the first large scale study of internal and external morphological differences among gibbons.
Schultz (1933) measured 8 linear variables on the skull, 10 postcranial variables including; sternal, pelvic, and limb lengths and breadths, counted numbers of vertebrae, and made several observations concerning external morphology, such as hair density and interdigital webbing. On the basis of this study Schultz postulated that nine taxa should be split into two genera: Genus Hylobates and Genus Symphalangus (Table 1.1).
Table 1.1 Schultz’ (1933) Classification of gibbons and siamangs
Genus Subgenus Species Hylobates Hylobates lar cinereus (=moloch) agilis pileatus concolor leucogenys hoolock Brachitanytes klossii Symphalangus syndactylus
20 Schultz’s generic division was based on 23 morphological differences, mainly
relating to the large size difference between gibbons (Genus Hylobates) and siamangs
(Genus Symphalangus) (Table 1.2). At the subgeneric level Schultz clearly
distinguishes H. klossii (subgenus Brachitanytes) from members of the subgenus
Hylobates {hoolock, lar, leucogenys, cinereus (= moloch), agilis, pileatus and concolor) on the basis of hair density, frequency of interdigital webbing, comparatively long thumbs and great toes, proportionately longer radius and tibia, proportionately shorter and broader ilium, proportionately shorter and broader corpus sterni, higher average numbers of vertebrae (except cervical), smaller average cranial capacity and cranial
dimensions, relatively higher skull, relatively smaller sized palate and comparatively
smaller degree of postorbital constriction. Schultz proposed that some of these morphological differences (e.g. higher average total number of vertebrae) were
indicative of klossii being more primitive than any of the other hylobatids. On the other hand he points out that in other respects klossii represents an intermediate step between the degrees of specialisation reached by gibbons compared to the siamang.
Subsequent classification systems published during the first part of last century, although differing slightly in their structure and composition, had the same basic form
as Schultz’s. It was not until the 1970’s that major revisions to this taxonomy appeared.
Groves’ monograph (1972) remains one of the most comprehensive studies of gibbon systematics, incorporating cranial and postcranial measurements, observations regarding colour, hair pattern, body proportions, external features, and reproductive anatomy, serology (blood), karyology (chromosomes), distribution patterns, evidence
for hybridization and sympatry, behavioural characteristics, plus data from other published sources. On the basis of these data and observations Groves recognised 6
species all confined to the genus Hylobates (Table 1.3). This was further subdivided
21 into three main subgenera; Hylobates, Nomascus and Symphalangus. The subgeneric divisions were based on the diploid number of chromosomes which showed clear-cut differences between different groups of gibbons. The characteristics identified by
Groves as distinguishing gibbon species are summarised in Table 1.4. Groves also used these data to make several new interpretations regarding phylogenetic relationships among gibbons, this is discussed further in section 1.4.1.
Over the next 20 years or so, several modifications were made to Groves’ (1972) taxonomy as a result of increased understanding of various fields (Creel and Preuschoft,
1976; Chivers, 1977; Chivers and Gittins, 1978; Haimoff et al., 1982; and Groves,
1984, 1989, 1993). The most significant change to Groves’ original classification was the identification of several new species in the subgenus Hylobates. These were formed as a result of raising several lar subspecies to species level. Marshall and Marshall
(1976), Chivers (1977) and Marshall and Sugardjito (1986) agree on the species status of agilis, moloch and muelleri on the basis of differences in colour patterns of fur on the head and around the face, and differences in territorial songs. These authors all recognise a total of 9 gibbon species.
Marshall and Marshall (1976) and Marshall and Sugardjito (1986) amassed data about pelage and vocalisation differences from museum pelts and wild gibbons, respectively. This work remains one of the most comprehensive studies of fur and vocal differences among gibbons. Marshall and Sugardjito (1986) present a new taxonomy
(Table 1.3) which includes Prouty et al's. (1983 a, b) identification of a new subgenus,
Bunopithecus, for the hoolock gibbon on the basis that the diploid number
{Bunopithecus = 38) differed from the other three subgenera.
The most significant recent changes to Marshall and Sugardjito’s (1986) classification relates to the number of species in the subgenus Nomascus. These changes
22 are the result of raising H.c.leucogenys and H.c.gabriellae to species level, to create H. leucogenys (Dao, 1983; Ma and Wang, 1986; Geissmann, 1993) and//, gabriellae
(Geissmann, 1995). These authors provided evidence based on differences in the anatomy of the penis bone, vocalisations and areas of sympatry in support of species status for these taxa.
The resultant taxonomy, incorporating these new species and maintaining
Marshall and Sugardjito’s (1986) basic structure, has been published by Geissmann
(1995) (Table 1.3). It comprises eleven species in total. Since this is the most comprehensive, widely used and up-to-date taxonomy it will be adopted in this study.
23 Table 1.2 Morphological specialisations attributed to genus Symphalangus compared to genus Hylobates, identified by Schultz (1933).
Characteristics of genus Symphalangus, compared to genus Hylobates 1 larger body weight and larger dimensions of nearly all body parts 2 stouter trunk and relatively broader chest 3 shorter legs (in proportion to the trunk) and much longer arms (in proportion to the legs) 4 relatively broader hands 5 proportionately much higher face 6 smaller ears 7 presence of large throat pouch 8 higher frequency of extensive webbing between toes II and III 9 later appearance of ischial callosities 10 lower hair density 11 proportionately broader and longer mouth palate 12 proportionately longer brain case 13 more pronounced postorbital constriction 14 orbits project farther in a vertical and lateral direction 15 greater glabellar thickness of the skull wall 16 relatively larger temporal squama 17 occasional presence of a sagittal crest and generally more marked approximation of the temporal line 18 steeper profile of the mandibular symphysis 19 frequent occurrence of supernumerary molars and lack of signs of suppression of the third molars 20 smaller average number of lumbar and of thoraco-lumbar vertebrae 21 short and broad corpus sterni 22 proportionately longer and broader ilium 23 proportionately longer pubic bone and generally wider pelvis ______Table 1.3 Classifications of the Genus Hylobates.
Groves, 1972 Marshall and Sugardjito, 1986 Geissmann, 1995 Subgenus Diploid Species Subgenus Diploid Species Subgenus Diploid Species number number number Hylobates 44 lar Hylobates 44 lar Hylobates 44 lar pileatus pileatus pileatus klossii agilis agilis moloch moloch muelleri muelleri klossii klossii cyi hoolock Bunopithecus 38 hoolock Bunopithecus 38 hoolock Nomascus 52 concolor Nomascus 52 concolor Nomascus 52 concolor leucogenys gabriellae Symphalangus 50 syndactylus Symphalangus 50 syndactylus Symphalangus 50 syndactylus Table 1.4 Characteristics distinguishing gibbon species identified by Groves (1972) (adapted from Groves, 1972, p.81).
Species Characteristics
lar group {H. lar, H. Small size. Dense fur. Early development of ischial callosities. Genital tuft reduced. Large ears. Loss or pileatus) modification of sexual dichromatism. Laryngeal sac lost. Mandibular symphysis slopes, ‘Dryopithecus’ pattern modified. High brachial index. Short nasal septum, Baculum reduced.
H. klossii Very small size. Loss of sexual dichromatism and of face pattern. Long legs, great toe and thumb. Loss of genital tuft. High number of coccygeal vertebrae. Carotid foramen faces back. No ossification of lateral pterygoid ligament.
H. hoolock Narrow chest and build. Absence of webbing. High number of coccygeal vertebrae. Nasal bones hooked. Os H. concolor Small size. Very narrow chest and build. Cheek whiskers facial pattern. Sexually dimorphic growth on scalp. Pinna fused on head below, Gians penis and clitoris well developed and long. Laryngeal sac absent in female. High number of thoracic vertebrae.
H. syndactylus Loss of sexual dichromatism and (usually) of face pattern. Carotid foramen always faces back. Molar cingula fragmented, ______1.3.3 Geographic distribution of gibbons
The eleven species of hylobatid are distributed in tropical rain forests over
mainland and archipelagic Southeast Asia (Figure 1.1). Geissmann (1995) provides a
detailed outline of the geographic ranges of the species and their subspecies (Table 1.5),
depicted in Figure 1.2.
The hoolock gibbon is distributed to the west in Assam, Burma and Bangladesh
(Figure 1.2). Gibbons from the subgenus Nomascus have a more easterly distribution
over South China, Vietnam, Laos and Cambodia. The three species are distributed in a
north-south continuum with: concolor to the north in the Yunnan province of China,
and northerly parts of Vietnam and Laos; leucogenys in south Yunnan, north and central
Vietnam and Laos; gabriellae in south Laos, south Vietnam and western Cambodia
(Figure 1.3). The siamang is found on Sumatra and mainland Malay Peninsula (Figure
1.2).
The species of the subgenus Hylobates are distributed over the rest of Southeast
Asia; lar over east Burma, Thailand, mainland Malay Peninsula and Southwest Yunnan;
agilis in west and east Sumatra, Southwest Borneo, and island Malay Peninsula;
muelleri over the rest of Borneo from the Northwest to Southeast; moloch on western
Java; pileatus in east Thailand and Cambodia; and klossii on the Mentawai Islands
(Figure 1.4)
In most areas species are separated by rivers or straits (Figure 1.2). These
stretches of water could be barriers to gene exchange since it is well documented that gibbons will not cross water (Marshall and Sugardjito, 1986). There are several areas of contact, however, and these usually occur at the headwaters of the rivers. In such contact areas there is evidence of hybrid zones forming and these will be discussed
27 shortly. The other main areas of overlap occur where species are sympatric with each other.
The siamang is sympatric with other gibbons across the whole of its range; on
Sumatra with agilis, and on mainland Malay Peninsula with lar. These instances of sympatry are presumably made possible by the significant size difference between the siamang and the other gibbons, combined with the fact that the siamangs diet is more folivorous and less frugivorous (Chivers, 1974; MacKinnon, 1977).
Geissmann (1995) reports three areas of sympatry among species in the subgenus Hylobates (numbers 1-3 on Figure 1.4); between lar and pileatus at the headwaters of the Takhon River in Khao Yai National Park, about 120 km Northeast of
Bangkok in Thailand [1]; between lar and agilis at the headwaters of the Muda River in the north-western part of mainland Malay Peninsula [2]; between agilis and muelleri at the headwaters of the Bari to River in Kalimantan, Borneo [3].
At each of these areas of sympatry there have been reports of hybrid zones forming. In Khao Yai National Park the area of overlap between lar and pileatus is about 100 km^ with hybrids constituting approximately 5% of the breeding population
(Brockelman and Gittins, 1984; Marshall and Sugardjito, 1986; Marshall and .
Brockelman, 1986). Brockelman and Gittins (1984) and Gittins (1978) report on a small number of mixed groups and hybrids of lar and agilis on the shores of an artificial lake at the headwaters of the Muda River. On Kalimantan, Mather (1992) reported a zone of at least 3,500 km^ inhabited by an apparently stable hybrid population of agilis and muelleri.
28 Table 1.5 Geographic distributions of gibbon species
Subgenus Species Distribution Hylobates lar Thailand, east Burma, Malay peninsula, north Sumatra, Southwest Yunnan pileatus East Thailand, Cambodia agilis West Sumatra, Southwest Borneo (between Kapuas and Barito rivers), Malay peninsula, east Sumatra moloch West Java muelleri West Borneo (north of Kapuas river), north Borneo, Southeast Borneo (east of Barito river) klossii Mentawai Islands Bunopithecus hoolock Assam, Bangladesh, Burma (east and west of Chindwin river), west Yunnan Nomascus concolor North and Northeast Vietnam (east of Red river and hinterland of Hon Gai), Northwest Laos, central and west Yunnan (between Black and Red rivers and east of Salween river), Hainan Island leucogenys Laos, north and central Vietnam, south Yunnan gabriellae South Laos, south Vietnam, west Cambodia Symphalangus syndactylus Malay peninsula, Sumatra
29 Assam China
.Bangladesh.'
India
Burma Hainan Sunda Shelf Island^
Thailand Philippines
Cambodia
SUNDALAND
Sulawesi Borneo
Mentawai Kalimantan Islands Sumatra
200m Bathymetric line
Java
Figure 1.1 Map of SE Asia showing the boundary of the Sunda Shelf, demarked by the 200m bathymetric line.
30 30 ' m m
10' m wm A
Bunopithecus G=-%r; Hylobates Nomascus i j l I K I I ' j Symphalangus
0 400 800 1200km ■ '-K ^x?c3^ 10' 90' 100' 110' 120'
Figure 1.2 Distribution of gibbon subgenera, after Geissmann (1995, p.475).
31 ii£ Da (Black R.) V Soii£ Hong (Red R.)
20 °
10°
100° 110°
H concolor
I P l i H. leucogenys
0 400 800 km H gabriellae
Figure 1.3 Distribution of species in the subgenus Nomascus^ after Geissmann (1995, p.477).
32 r / j ^ agiAs H. lar
H. klossii
H. moloch
H. muelleri I ! I
Muda R S .fh "'
Banto River
400 800km I I I
100' 110' 120'
Figure 1.4 Distribution of species in the subgenus Hylobates, after Geissmann (1995, p.476). The numbers represent areas of sympatry: [1] between lar and pileatus at the headwaters of the Takhon River in Khao Yai National Park, Thailand; [2] between lar and agilis at the headwaters of the Muda River in the north-western part of mainland Malay Peninsula; [3] between agilis and muelleri at the headwaters of the Barito River in Kalimantan, Borneo.
33 There is also some evidence for the existence of contact zones among species from the subgenus Nomascus (Geissmann, 1995). Dao Van Tien (1983) and Ma and
Wang (1986) reported a small area of sympatry between concolor and leucogenys in southern Yunnan in China and in northern Vietnam. Furthermore, Geissmann (1995) has described a possible wild-born hybrid between concolor and leucogenys. Finally, there is some evidence of hybridisation in the areas of contact between H. gabriellae and H. leucogenys siki in southern Vietnam and Laos (Groves, 1972).
The implications for the species status of the breeding pairs in these hybrid zones is potentially profound. Species definitions vary according to which species concept is favoured. According to the widely held Biological Species Concept (BSC)
(Dobzhansky, 1937; Mayr, 1942; Charlesworth, 1990) species are groups of actually or potentially interbreeding natural populations which are reproductively isolated from other such groups (Mayr, 1942). Paterson (1981, 1985) attempted to deal with the issue of hybridization by modifying the BSC to incorporate autorecognition factors in defining a species. Paterson defined a species as ‘the most inclusive group that shares a fertilisation system’. This fertilisation system is termed the ‘specific-mate recognition system’ (SMRS) and it encompasses all interactive aspects of male-female reproductive physiology. This system is also known as the recognition concept (RC). Most of the above studies regarding gibbon taxonomy and phylogeny (e.g. Groves, 1972; Chivers,
1977; Marshall and Sugardjito, 1986; Geissmann, 1995) are based on the recognition concept, since they rely on auto-recognition factors such as pelage and vocalisations to distinguish species.
Regarding the species status of gibbons from hybrid zones, current information relating to the reproductive success of individuals in such hybrid zones is scarce. Until such data becomes available the species status of those taxa involved can not be fully
34 assessed. Furthermore, it is not within the remit of this thesis to address this question, since this study is looking at morphological, genetic and biogeographic patterning and the data used here is not taken from hybrid zones. Hence, for the purposes of this study the taxonomic assessment presented by Geissmann (1995) is accepted, and by implication, the RC definition of a species.
35 1.3.4 Palaeontological and palaeoenvironmental background
Palaeontological record of gibbons
The evolutionary history of gibbons has been little understood, largely due to a
poor fossil record (Tyler, 1993). Numerous fossil taxa have been nominated as possible
gibbon ancestors on the basis of their small body size and simple molar cusp
morphology, including; Pliopithecus, Laccopithecus, Micropithecus, Dendropithecus,
Limnopithecus, Dionysopithecus and Platodontopithecus. Most of these taxa are now generally regarded as early catarrhines (Tyler, 1993; Fleagle, 1999). For some, however one of these fossils remains a strong contender for the position of gibbon ancestor:
Laccopithecus robustus (e.g. Wu and Pan, 1984, 1985; Tyler, 1993; Jablonski, 1993a).
This late Miocene fossil (c. 8 Ma [million years ago]) is known from the Lufeng deposits in Yunnan, China. Evidence for the close phylogenetic relationship between
Laccopithecus and extant gibbons is largely based on cranial and dental anatomy (Wu and Pan, 1984, 1985). Meldrum and Pan (1988) have also presented evidence that the only identified postcranial remains of Laccopithecus, a proximal fifth phalanx, is
similar to modem siamangs, and hence a brachiator. However, metric and morphological examination of the upper and lower dentition shows a sexual .
dimorphism that far exceeds extant gibbons (Pan et al., 1989). According to Tyler
(1993), ifLaccopithecus is the ancestor of modem gibbons, it should be an arboreal brachiator, and have minimal sexual dimorphism. Clarification of the placement of
Laccopithecus within the Hylobatidae warrants further evidence from the skull, auditory
region and postcranium (Tyler, 1993; Jablonski, 2000).
Wu and Poirier (1995) list mammalian faunas from a site called Hudieliangzi
(Butterfly Hill) in Yuanmou, Yunnan, south China which contains specimens identified
as Hylobates sp.. A more detailed taxonomic description of these specimens in not provided, but Zong et a/. (1991) have dated this site to 5 to 6 Ma.
36 Undisputed gibbons do not appear in the fossil record until the Pleistocene (e.g.
Hooijer, 1960; Gu, 1989; Tyler, 1993; Jablonski 2000). Hooijer (1960) presents evidence of fossil gibbons (mainly teeth) from Sumatra {H. syndactylus, H. agilis), Java
(H. syndactylus, H. moloch), Borneo {H. muelleri) and China {H. hoolock).
Subsequently, several authors have dated the sites which contain these fossil faunas. De
Vos (1983) and Van den Berg et al. (1995) have dated the so-called Punung faunal assemblage on Java and deposits on Sumatra, which contain fossils identified as H. syndactylus, to between 60,000 and 80,000 years old. Deposits containing the remains of H. moloch on Java have been dated as Recent (Van den Berg et a l, 1995). The evidence for syndactylus on Java, between 60,000 and 80,000 years ago indicates this species was present on the island before moloch, Java’s only present day gibbon inhabitant.
Long et al. (1996) discuss additional evidence of fossil gibbons {H. sp.) from the sites of Lang Trang in Vietnam dated to 80,000 years and Niah in Malaysia dated to
50,000 years, although details of taxonomic identification are not provided. Gu (1989) presents evidence of fossil gibbons from Chinese Pleistocene deposits. The fossils, mainly teeth, are identified as representing two species: H. concolor and H. hoolock.
37 Palaeoenvironmental history of SE Asia
The palaeoenvironmental history of Southeast Asia is complex due to a combination of orogenesis, plate tectonics and glacial activities. These factors have affected the palaeogeography of SE Asia, plus its climate, temperature, fauna and flora.
The Indo-Malaysian Islands are part of a complex comprising several structural divisions. At the boundaries of these plates, tectonic activity has created series of volcanoes and areas of submergence, the most dramatic of which can be seen at the margins of the Sunda Shelf (Figure 1.1). The exposed part of the Sunda Shelf is also known as Sundaland, and comprises the Malay Peninsula, Sumatra, Java, Borneo and other smaller island groups (Bellwood, 1997) (Figure 1.1).
Hall (1996, 1998) has reconstructed the palaeoenvironmental history of
Cenozoic SE Asia, including the distribution of land and sea. Since the fossil evidence for gibbons and gibbon ancestors dates to no later than the middle Miocene
(approximately 15 Ma), this date has been used as a benchmark from which to briefly describe the palaeoenvironmental history of SE Asia.
Between 20 and 10 Ma changes in the orientation of several tectonic plate boundaries throughout SE Asia resulted in the tectonic pattern recognisable today (Hall,
1998). These changes, described in detail in Hall (1998) had dramatic effects on the palaeogeography of the area. Marine deposits including fossils and sediments are used to reconstruct the extent of land exposure, the position of former coastlines, and the location of former river systems. However, in many cases depending on the type of depositional environment, the marine record is incomplete and patchy. Despite this,
Hall (1998) has reconstructed the distribution of land and sea in SE Asia at 5 million year intervals from the interpretation of marine records from a variety of sources.
38 Middle Miocene 15 M a
u»
VOLCANOES ^ HIGHLAND E j l a n d CARBONATE PLATFORMS □ SHALLOW SEA □ DEEP SEA
Figure 1.5 Reconstruction of the distribution of sea and land in SE Asia during the Middle Miocene, 15 Ma (Hall, 1998, p.l 18) VXv///.- - Late Miocene % 10 M a
. . . _ A -.
VOLCANOES HIGHLAND L'l LAND PZI3 CARBONATE ' p l a t f o r m s □ SHALLOW SEA n DEEP SEA
Figure 1.6 Reconstruction of the distribution of sea and land in SE Asia during the Late Miocene, 10 Ma (Hall, 1998, p.l 19) ^ Early Pliocene 5 Ma
VOLCANOES HIGHLAND E ] LAND CARBONATE PLATFORMS □ SHALLOW SEA □ DEEP SEA
Figure 1.7 Reconstruction of the distribution of sea and land in SE Asia during the Early Pliocene, 5 Ma (Hall, 1998, p. 120) Between 15 and 5 Ma large parts of Sundaland were exposed. According to
Hall’s distribution map (Figures 1.5-1.7), during the middle Miocene at 15 Ma emergent land persisted from China, Vietnam, Laos, Cambodia, Thailand, and Malay
Peninsula, connecting these areas with large parts of Borneo. During this time only the southern parts of Borneo were covered by shallow seas. A ridge of volcanoes running east to west across central Borneo (on the Sarawak-Kalimantan border) created areas of highland. This situation persisted through the late Miocene, approximately 10 Ma
(Figure 1.6)
Subduction of the Indian plate under the Sunda Shelf also created a ridge of volcanoes across Sumatra and Java, with patches of emergent land appearing at 15 Ma.
By 10 Ma (Figure 1.6) these isolated patches of land joined to form a long, thin strip of land linking Sumatra, Java and Sundaland.
Between 15 and 10 Ma Hainan Island was also joined to mainland China, however by 5 Ma this corridor was covered by shallow seas (Figure 1.7). In Hall’s most recent reconstruction of emergent land, at 5 Ma, the area of emergent land is reduced.
However, even at this time Malay Peninsula, Borneo, Sumatra and Java are linked via emergent land on the Sunda Shelf. Also at this time, patches of emergent land become evident at the position of the present day Mentawai Islands, although these patches are not connected to Sumatra. Throughout this time, east of Borneo deep basins had formed which may have represented barriers to dispersal to islands such as the Philippines,
Sulawesi and New Guinea.
Thus, there is evidence indicating that emergent land probably extended from
Indochina to Borneo in the Miocene, and according to Morley and Flenley (1987) both seasonal and everwet rain forests were present. Other evidence indicates that tropical rain forest extended as far north as southern China, southern Japan, and westward to northern India during the late Miocene (Jablonski, 1993a, Heaney, 1991, and references
42 therein). According to Heaney (1991) by approximately 5 Ma the insular and tropical nature of SE Asia was well established.
The age of the Plio-Pleistocene boundary is controversial (Lowe and Walker,
1997). Some authors favour a relatively young date for the age of this boundary, 1.64
Ma, based on palaeomagnetic evidence. Other suggest that the boundary is much older, around 2.5 Ma, based on ocean core and faunal change evidence (Lowe and Walker,
1997). The view taken here is that the Plio-Pleistocene boundary dates to no later than about 2.5 Ma.
Batchelor (1979) suggests that up until the end of the Pliocene the extent of exposed Sundaland covered some 2,000 kilometres east to west, and incorporated much of Malay Peninsula, Sumatra and Borneo. However, at this time major changes in sea- level began as a result of glacial activity on a global scale. The major world-wide effects of glaciation were to alter sea levels, temperature and the extent of vegetation zones. Evidence from deep sea cores and deeply stratified terrestrial gastropod- and pollen-bearing cores indicates that since about 2.5 Ma there have been a number
(approximately 20) of glacial and interglacial cycles. During glaciations the vast quantities of water trapped in ice sheets across the globe, immobilised large amounts of
'^O and the cold seas were as a result relatively rich in '*0. During interglacials the ratio was reversed. Fluctuations in these ratios have been plotted from deep-sea cores, and indicate the timing and extent of glacial waxing and waning (Shackleton and Opdyke,
1973; Lowe and Walker, 1997).
In SE Asia, as in many parts of the world, the periodicity of glaciations had the effect of altering sea level and hence the extent of exposed land, as well as affecting climate and vegetation. During periods of low sea level large parts of Sundaland were exposed by between 120 and 160m below present sea level (Morley and Flenley, 1987;
43 Heaney, 1991; Bellwood, 1997). This had the effect of linking the islands of Sumatra,
Borneo and Java to the mainland, creating a geography similar to that seen during the
Miocene (Figures 1.5-1.7). Several authors present evidence of the environmental effects of Quaternary glaciations in SE Asia (e.g. De Vos, 1983; Morley and Flenley,
1987; Heaney, 1991; van den Berg et a l, 1995; Jablonski, 1993a; Brandon-Jones, 1996, and references therein). These studies provide evidence of the palaeo-ecological implications of environmental change, plus fossil locality and dating information for numerous sites across SE Asia. However, the exact nature and timing of these variations is still actively debated (Jablonski, 1993b, 1997, and references therein)
In summary, it is apparent that the pre-history of gibbons remains unclear due to a lack of crucial fossil evidence from the late Miocene and Pliocene. However, according to reconstructions by Hall (1996, 1998) and others (e.g. Heaney, 1991;
Jablonski, 1993a) much of the area uniting Sumatra, Borneo and Java to mainland
Malaysia was exposed for long periods during the late Miocene, Pliocene and periodically throughout the Pleistocene. Furthermore, several studies have shown that tropical rain forests were present in these areas, and that these habitats supported a diverse primate fauna (Morley and Flenley, 1987; Heaney, 1991; Jablonski, 1993a; and references therein). Despite evidence that climatic deterioration from the late Miocene onwards affected some primate fauna, this does not appear to be the case with respect to the gibbons (Jablonski, 1993a). Jablonski (1993a, 1998, 1999, 2000) presents evidence from the palaeontological and palaeoenvironmental record of China which indicates that in spite of increased seasonality and habitat fragmentation, gibbons were among the most successful primates. Finally, Jablonski (1998, 2000) suggests that this success was facilitated by a small body size and efficient life history parameters, such as an advanced age for the onset of reproduction and long inter-birth intervals.
44 1.4 Background to previous studies
Previous studies have used a variety of morphological, behavioural and molecular characteristics to address taxonomic issues and infer phylogenetic
interrelationships. These have provided much information regarding general differences among the various gibbon taxa and sub-genera, and it is relevant at this stage to briefly outline these characteristics.
Gibbons are well known for their elaborate forms of communication involving vocal and visual specialisations. All of the gibbon species produce species-specific and
sex-specific vocalisations, or songs (Marshall and Sugardjito, 1986; Geissmann, 1995).
These songs have been shown to vary considerably and have been used by several
authors to investigate taxonomic affinities and phylogenetic interrelationships (e.g.
Groves, 1972; Chivers, 1977; Marshall and Sugardjito, 1986; Geissmann, 1995).
Regarding overall body size, the siamang is the largest of all the gibbons. On
average, the siamang weighs approximately 11 kg. Species in the subgenus Nomascus are the second largest after the siamang, weighing over 7 kg. The hoolock gibbon
averages 6.8 kg. Species in the subgenus Hylobates are the smallest, weighing.between
about 5.5 - 6.5 kg. These values are mean, combined male and female body weights
obtained from documentation relating to wild-shot museum specimens collected by
Geissmann (1993). It is also well known that gibbons exhibit minimal sexual
dimorphism (e.g. Groves, 1972; Creel and Preuschoft, 1976, 1984).
Pelage in gibbons is highly variable interspecifically. Furthermore, coat colour
in gibbons is complex due to phase changes in colour which vary intraspecifically,
during maturation (via colour phases = polymorphism) and differences between the
sexes (sexually dichromatic). Several previous studies have concentrated on pelage
differences among gibbons (e.g. Kloss, 1929; Groves, 1972; Marshall and Sugardjito,
45 1986; Geissmann, 1993, 1995). As a preliminary to the phylogenetic study observations were made on the pelts of museum specimens, during skeletal data collection. These observations were assessed in association with published literature to produce a guide to the inter- and intra-specific differences in pelage among all eleven currently recognised species of gibbon (Appendix 1). Due to a lack of time, however, this was not pursued further in the present study. In the following section only broad differences and general coat colour will be described (Table 1.6).
H. syndactylus, klossii, moloch and muelleri are monochromatic, showing no sharp colour phases; agilis and lar are polymorphic showing dark and light phases; and pileatus, hoolock, concolor, leucogenys and gabriellae are sexually dichromatic, the adult males being black and the adult females being light. There are several vernacular names in use which give an idea of the general colour and/or distinguishing pelage features among the different taxa (Table 1.6).
While pelage and vocalisation differences among gibbons are well understood, detailed morphometric and molecular variability are less so. Thus, these two areas were chosen as the focus for this study. Since one of the key aims of this research is.to use molecular and morphological data to interpret phylogenetic relationships among gibbons, the next two sections of this chapter (1.4.1, 1.4.2) outline previous studies in these areas. Furthermore, since a second key aim of this study is to use the results of phylogenetic investigation to reconstruct the biogeographic history of gibbons, section
1.4.3 provides details of previous research in this area.
46 Table 1.6 General pelage descriptions and commonly used vernacular terms for the
different gibbon species (Geissmann, 1993, 1995).
Species Colour phase / common vernacular terms chromatism Hylobates syndactylus monochromatic siamang Hylobates hoolock sexually dichromatic hoolock, white-browed gibbon Hylobates concolor sexually dichromatic concolor, black (crested) gibbon Hylobates leucogenys sexually dichromatic white-cheeked (crested) gibbon Hylobates gabriellae sexually dichromatic yellow-cheeked (crested) gibbon. red-cheeked (crested) gibbon Hylobates lar polymorphic lar, white-handed gibbon Hylobates agilis polymorphic agile, black-handed gibbon Hylobates muelleri monochromatic Müller’s gibbon, Bornean gibbon. grey gibbon Hylobates moloch monochromatic Javan gibbon, silvery gibbon Hylobates pileatus sexually dichromatic pileated gibbon, capped gibbon Hylobates klossii monochromatic Kloss gibbon, dwarf siamang. dwarf gibbon, beloh
47 1.4.1 Review of previous molecular studies
Cronin, Sarich and Ryder (1984) provide a useful review of molecular studies
prior to the mid 1980’s. Cronin et al. (1984) used a variety of genetic data to construct
evolutionary trees: immunological, electrophoresis and nucleic acid hybridisation, and
had data for six species of gibbon: syndactylus, concolor, lar, moloch, agilis and pileatus. Their results indicated that there was very little genetic divergence among lar,
moloch, agilis and pileatus, and that these species were more closely related to each
other than any of them were to concolor or syndactylus. However, they were unable to
resolve interrelationships among lar, moloch, agilis and pileatus, or among this group
and concolor and syndactylus. The authors were able to calculate genetic distance and provide a date of 4-5 million years for the possible radiation of hylobatids.
Over the next decade or so, molecular biology was revolutionised by the advent
of PCR (Polymersase Chain Reaction) (Kleppe et al., 1971). PCR allowed direct access
to the phylogenetic information content of DNA sequences from both nuclear and
cytoplasmic genes, through in vitro amplification of specific DNA fragments.
Furthermore, PCR afforded the opportunity to quickly, and relatively easily, access phylogenetic information from tiny starting amounts of tissue, such as hair follicles, and
even degraded tissues such as museum specimens.
The first major study to use molecular data and PCR to interpret interspecific
phylogenetic relationships among hylobatids was carried out by Garza and Woodruff
(1992).
Garza and Woodruff (1992) used PCR to amplify a 252 bp segment of the
cytochrome b gene from the mitochondrial DNA of 26 gibbons, comprising nine
species and three subgenera; syndactylus (subgenus Symphalangus), concolor, leucogenys, gabriellae (subgenus Nomascus), agilis, muelleri, klossii, pileatus and lar
(subgenus Hylobates). There is no data from the subgenus Bunopithecus. They
48 extracted DNA from blood and hair samples obtained from zoo animals. The resultant
sequences were analysed using the phylogenetic inference package, PAUP (Swofford,
1991) and the maximum likelihood program PHYLIP (Felsenstein, 1990). Several trees
were built using both parsimony analysis and maximum likelihood. Two equally
parsimonious trees, each sharing the same basic topology were produced. The
maximum likelihood analysis produced a tree which was identical to one of the
parsimony trees (Figure 1.8). The trees indicate that subgenus Symphalangus was the
first group to diverge, however their results are unable to resolve relationships between
the subgenera Nomascus and Hylobates. Another interesting observation relates to the
position of klossii in the subgenus Hylobates. Prior to this study, morphological studies
had considered klossii to be the sister group of the so called /ar-group of gibbons {lar, pileatus, muelleri, moloch and agilis) (Chivers, 1977; Creel and Preuschoft, 1984;
Groves, 1984, 1989). Garza and W oodruffs analysis places klossii as an integral part of
the lar-group, being more closely related to muelleri and pileatus, than to lar or agilis.
These analyses revealed that, although cytochrome b gene provided useful
insights into the evolution of Hylobates, this region was not variable enough to
completely resolve phylogenetic relationships among hylobatids. Garza and Woodruff
conclude that “ a complete resolution of the taxonomy of lesser apes will require data
from other more rapidly evolving regions, such as the mitochondrial D-loop” (1992, p.
209).
Hayashi et al. (1995) analysed partial sequences of mitochondrial NADH
dehydrogenase genes from six species, 896 bp in length. Using the same analytical
techniques as Garza and Woodmff (1992) Hayashi et al. present a consensus tree which
shows the following relationships among gibbon species: concolor, syndactylus, pileatus, agilis, and klossii are successively more closely related to lar. Hence the
49 following subgeneric relationships are seen: Nomascus and Symphalangus are respectively more closely related to Hylobates (Figure 1.9). This is in contrast to Garza
and Woodruff (1992) who place Symphalangus as the most basal group. Again, however, this study does not include samples from subgenus Bunopithecus. Regarding
the distinctiveness of klossii from the other species in the subgenus Hylobates, Hayashi
et aL agreed that no such distinction can be made on the basis of their molecular data. In
contrast to Garza and Woodruff, however, they found klossii was more closely related to lar, than to any of the other lar-group members. In fact, this study revealed that inter relationships among klossii, lar, pileatus, agilis, moloch and muelleri are complicated,
and that no one tree was able to fully resolve the polychotomy.
Hall et al. (1996, 1998) have also used cytochrome b gene sequence data, 1141 bp in length. In their first paper they incorporated one species from each of Hylobates,
Symphalangus and Bunopithecus subgenera {muelleri, syndactylus and hoolock, respectively) and two species from Nomascus (gabriellae and leucogenys) to interpret evolutionary relationships between the four gibbon subgenera. In the later study they added lar to their sample. From these studies. Hall et a l (1996, 1998) concluded that
cytochrome b gene could not resolve the evolutionary relationships between gibbon
subgenera, let alone among different gibbon species (Figure 1.10). Furthermore, Hall et al. (1996) suggest that the gibbons diverged from the great apes about 20 Ma.
Other studies have also suggested a more ancient great ape-gibbon split.
Combined biochemical results based on analysis of blood groups and histocomatibility
antigens, chromosome banding patterns, protein structure and antigenicity, amino acid
sequences of proteins, and DNA endonuclease restriction mapping, sequencing and reassociation kinetics, indicate a great ape-gibbon split at 15 Ma (see Tyler, 1993 and
50 references therein). Analyses based on the complete mitochondrial genome have pushed the split back even further to 36 Ma (Amason et aL, 1996).
This trend for molecular data to indicate more ancient radiation dates is also seen v^ithin the genus Hylobates. Recently, two studies have used molecular sequence data under the assumption of a molecular clock to make predictions regarding divergence dates within the genus. Zehr et al. (1996) suggest, on the basis of cytochrome oxidase subunit II, that there was a rapid radiation of gibbons 6-8 Ma, and that the first subgenus to diverge was Bunopithecus. Porter et al. (1997) analysed sequences of the e-globin locus to investigate phylogenetic relationship among 39 primates, including two gibbon sequences. According to their reconstruction, the gibbon radiation dates to 9.9 Ma. These molecular estimates of the timing of the gibbon radiation are in sharp contrast to studies based on non-molecular data (e.g. Groves,
1972; Chivers, 1977; Creel and Preuschoft, 1984). These authors predict that the gibbons represent a recent radiation (e.g. Pleistocene), on the basis that they are a very homogenous group, both morphologically and biochemically (Creel and Preuschoft,
1984).
Previous molecular studies have provided conflicting results regarding
interrelationships among gibbons, or have been unable to resolve phylogenetic relationships at a subgeneric or species level. This is likely due to the fact that the
genetic regions chosen for study (e.g. cytochrome b gene) do not evolve fast enough to
pick up the spéciation events which have occurred in gibbons. Here, cytochrome b gene
sequence data available on the world wide web is reanalysed to confirm or reject
previous findings. This study will include cytochrome b gene sequence data for all
eleven currently recognised species of gibbon, and as such offers a more comprehensive
taxonomic coverage compared to other studies based on the same gene. Furthermore, a
51 more rapidly evolving area of the mitochondrial genome, the control region, is included in a new genetic analysis. The control region, or D-loop, has been identified as a possibly useful region for elucidating phylogenetic interrelationships among gibbons
(Garza and Woodruff, 1992) but this has not been tested before. It has been used with success at the interspecific level in a variety of other vertebrate groups (Brown et aL,
1986). This study creates novel control region phylogenies, which are analysed in association with published cytochrome b gene data. A molecular clock is also employed to reconstruct divergence dates within the genus Hylobates. These estimates are compared with other molecular and non-molecular divergence dates, and employed along with phylogenetic and distribution data to reconstruct the biogeographic history of gibbons.
52 . Ictr (Hylobates)
■ a^lis (Hylobates)
. muelleri (Hylobates)
pileatus (Hylobates)
. klossii (Hylobates)
leucogenys (Nomascus)
concolor (Nomascus)
gabriellae (Nomascus)
' syndactylus (^mphalang^s)
Figure 1.8 Most parsimonious tree based on cytochrome b gene sequence data,
Garza and Woodruff (1992).
53 lar (Hylobates)
klossii (Hylobates)
a^lis (Hylobates)
pileatus (Hylobates)
syndactylus (^mphalangus)
concolor (Nomascus)
Figure 1.9 Consensus tree based on partial sequences of mitochondrial NADH
dehydrogenase genes, Hayashi et al. (1995).
54 Maximum likelihood
muelleri (Hylobates) 100% lar (Hylobates)
70% hoolock (Bunopithecus)
leucogenys (Nomascus) 100% gabriellae (Nomascus)
syndactylus (^mphalangus)
Maximum parsimony
muelleri (Hylobates) 99% lar (Hylobates)
hoolock (Bunopithecus) 62% leucogenys (Nomascus) 100% gabriellae (Nomascus)
syndactylus (Symphalangus)
Figure 1.10 Maximum likelihood and maximum parsimony trees, showing bootstrap
values, based on cytochrome b gene sequence data. Hall et al. (1998).
55 1.4.2 Review of previous morphological studies
The first large scale skeletal study of interspecific variability among gibbons was done by Adolph Schultz (1933). On the basis of this study Schultz made several new interpretations regarding gibbon taxonomy and phytogeny. These have been discussed is section 1.3.2.
Groves (1972) used a number of morphological characteristics, similar to those adopted by Schultz, but added dentition, pelage, reproductive anatomy, serology
(blood), karyology (chromosomes), distribution, evidence for hybridization and sympatry and behavioural characteristics, as taxonomic indicators. These characteristics have been reviewed previously so will not be discussed further here. However, on the basis of his taxonomic assessment Groves proposed several new interpretations concerning gibbon interrelationships. He arrived at these by tabulating his characteristics, numbering 59 in total, using numerical taxonomy techniques. He estimated degrees of difference between the six species and coded, where possible, the data into two or three character states. For example, for the character ‘zygomatic arch’ two possible states were applied; flat or bowed. From the resultant table. Groves estimated indices of similarity using Sokal and Sneath’s ‘simple matching coefficient’
(1952). The values were used to assess the degree of similarity among the six species.
Groves concluded that the concolor-lar distance was as great as the distance between lar and syndactylus. He was in agreement with Schultz (1933) over the intermediate position of klossii, between lar and syndactylus, but concluded that overall klossii was more similar to lar, hence it should be included in the lar subgenus.
Regarding hoolock. Groves, proposed that it too was intermediate between lar and syndactylus. Groves’ resultant phytogeny placed concolor as the most ancestral species, followed by syndactylus, then klossii, then hoolock, lar and pileatus (Figure 1.11).
56 The methodology employed by Groves (1972) is pre-cladistic and such phenetic approaches to phylogenetic reconstruction are now considered, by many, to be limited in their robustness (e.g. Patterson, 1987). In addition, despite the range of characteristics used by Groves, the actual number of metric variables measured on skeletons was limited to 6 on the skull and teeth, and 6 on the postcranium. Creel and Preuschoft
(1976) were the first authors to publish a large scale morphological study of the gibbons. They analysed cranial measurements using multivariate statistical methods in order to interpret taxonomic and phylogenetic relationships among hylobatids.
Creel and Preuschoft’s particular emphasis was to use tri-dimensional co ordinates of anatomical landmarks on the cranium. These co-ordinates were collected using a specially designed pair of modified co-ordinate callipers made by the authors.
Using this instrument. Creel and Preuschoft took X, Y and Z co-ordinates of 34 anatomical points on the skull which were defined by a total of 102 co-ordinates. These co-ordinates, plus 15 linear variables, were measured on 545 crania of adult, male and female, gibbons from the following taxa; syndactylus, muelleri, agilis, entelloides, hoolock, concolor, klossii, moloch, vestitus, and pileatus. These data were then analysed using principle components analysis, distance and cluster analysis, and multiple discriminant analysis.
On the basis of these analyses. Creel and Preuschoft concluded that their data revealed the presence of five clearly distinguishable morphological groupings among the hylobatids tested. Furthermore, they rely on the morphological definition of a species, such that two groups or populations which are morphologically different from each other constitute two different species (Cain, 1954; King, 1993). Their interpretation of these findings, was that only 5 independent species exist: syndactylus, hoolock, concolor, klossii, and lar. In addition, these five species formed a single genus,
Hylobates, and should be subdivided into four subgenera: [1] Symphalangus for
57 syndactylus, [2] Nomascus for concolor, [3] a subgenus (taxonomie name not given) for
hoolock and [4] Hylobates for lar and klossii. The implication regarding the lar
grouping is that Creel and Preuschoft did not consider muelleri, agilis, entelloides,
moloch, vestitus, and pileatus to be separate species. Creel and Preuschoft refer to these
taxa as the Tar’ complex since they hold closer morphological affinities with lar, than
with any of the other taxa tested.
Creel and Preuschoft (1984) reanalysed the above data along with published
data, to try and resolve phylogenetic relationships among hylobatids, and determine the
number of species within the genus. The data taken from published work comprised
morphological and behavioural characteristics, including: pelage, song, hair density,
postcranial variables (number of saccral vertebrae; number of coocygeal vertebrae;
manubrium length/corpus length; ishium length/pubis length; hindlimb length/trunk
length, [taken from Schultz, 1933]), and other internal and external anatomy features.
These were mainly derived from Groves (1972) and Schultz (1933). All measurements
and observations, other than the co-ordinate data, were recoded as binary variables and
analysed using phenetic and cladistic methods. Where necessary, the co-ordinate data
was recoded as combinations of binary variables so that it could be included the phenetic and cladistic analyses. Despite the much greater detail and methodological robustness of this study, both in terms of the variety of data used and the types of
analytical techniques employed, the results confirmed those of the earlier study. Creel
and Preuschoft (1984) concluded that even with a wider variety of characteristics
employed they could only distinguish 5 species: syndactylus, hoolock, concolor, klossii, and lar. However, it is not stated what criteria they have used to define their species.
Furthermore, they proposed that because of the small number of species within the genus, there was no need for any subgeneric divisions. In addition they suggest that
58 most members of the so called lar-complex {muelleri, agilis, moloch, and pileatus) should be considered subspecies of Hylobates lar.
Regarding interrelationships among these species, Creel and Preuschoft (1984) propose the following: syndactylus, hoolock, concolor, and klossii, are successively more closely related to the lar group. Within the lar group the following relationships are shown: lar moloch, lar muelleri, lar agilis, and lar lar, are successively more closely related to lar pileatus. This phytogeny (Figure 1.12) is based on combined results of all of the morphological analyses.
Haimoff et al. (1982) re-evaluated phylogenetic relationships among hylobatids based on a compatibility analysis of morphological and behavioural characters. The authors collected data on 55 different characteristics: including 6 cranial, 6 dental, 6 postcranial, 4 chromosomal, 7 pelage, 11 general external characteristics (e.g. hair density), and 15 song characters. Included in the analysis were nine gibbon species recognised by Marshall and Marshall (1976) and Chivers (1977): concolor, hoolock, klossii, lar, moloch, muelleri, pileatus, agilis, and syndactylus. Character compatibility analysis, is a means by which “the pattern of agreement and disagreement in a. hypothesised evolutionary pattern among each character in a given data set may be clearly displayed” (Haimoff et al., 1982, p.214). The characters are coded according to whether they are primitive or derived and a series of character state trees constructed. In order to determine the derived and primitive states, Haimoff et al. made several assumptions regarding the gibbon ancestor: the generalised gibbon ancestor was medium sized and exhibited suspensory behaviour, sexual monomorphism, monogamy, territoriality, frugivory and at least a simple vocal repertoire. For example, for the character ‘general body size’ the primitive state was assumed to be medium, and the derived state either large, or small. For the character ‘sexual dimorphism of crown hair’
59 the primitive state was assumed to be no dimorphism, and for the derived state,
dimorphism was present.
Haimoff et a l (1982) present a gibbon phytogeny which shows the following
relationships: concolor^ syndactylus, hoolock, klossii, pileatus and moloch are
successively more closely related to a clade comprising, agilis, lar and muelleri, as
sister taxa (Figure 1.13).
Geissmann (1993, 1995) included several ‘non-communicatory’ characters in his phylogenetic analysis of the evolution of gibbon communication. These were mainly
derived from Schultz (1933) and Groves (1972). Geissmann (1993) analysed several
aspects of vocal communication (e.g. dueting), olfactory communication (e.g. the presence of sternal glands), and visual communication (e.g. pelage characters) among
10 species of gibbon, representing all four subgenera. Geissmann (1993) was generally unable to resolve relationships among gibbon subgenera, although many of the trees derived from cladistic analysis of the combined dataset (i.e. vocal, visual, olfactory and
‘non-communicatory’ characteristics) indicated that the first gibbon to diverge was hoolock (trees not shown).
Despite the apparent breadth of information relating to morphological variability
among gibbon taxa, no studies have incorporated detailed morphometric analyses across
the whole gibbon skeleton. Hence, it was decided that such a study was warranted,
especially in light of the newly recognised species of gibbon published since these previous works (e.g. Geissmann, 1993, 1995). Furthermore, detailed postcranial
characteristics have not previously been analysed using cladistics. For example, Creel
and Preuschoft’s (1984) cladistic and phenetic analyses only included five postcranial
characters. Hence, one of the key aims of this study is to investigate morphometric
variability across the whole gibbon skeleton, using both phenetic and cladistic
60 techniques. Results of the morphological analyses are also interpreted in light of the molecular estimate of gibbon phytogeny, to assess the degree to which the
morphological results correspond to the molecular results.
61 lar + pileatus
hoolock
klossii
syndactylus
concolor
Figure 1.11 Groves’ (1972) phylogeny of gibbons.
lar pileatus lar lar
lar muelleri
lar moloch
klossii
concolor
hoolock
syndactylus
Figure 1.12 Creel and Preuschoft’s (1984) phylogeny of gibbons.
62 lar
agilis
' muelleri
moloch
pileatus
, klossii
. hoolock
, syndactylus
, concolor
Figure 1.13 Haimoff et aL (1982) phylogeny of gibbons.
63 1.4.3 Review of previous biogeographic studies
Few scenarios have been proposed to describe the radiation of hylobatids
(Groves, 1972; Chivers, 1977). Groves (1972) proposes a scenario for the radiation of the /ar-group of gibbons (Figure 1.14). Eustatic lowering of sea level in the Pleistocene is used to explain the dispersal of these gibbons. In Figure 1.14 the dotted lines indicate the approximate location of the valleys of the North and East Sunda Rivers, which
Groves assumed to be barriers to dispersal in the Late Pleistocene. The arrows on the diagram indicate the directions of dispersal and the figures the order. According to
Groves (1972) reconstruction the following dispersal patterns occurred within the subgenus Hylobates: [1] Dispersal of muelleri down the eastern side of the Sunda Shelf into Borneo. According to Groves this probably occurred during the Early Pleistocene, when the later drainage pattern was not yet established. [2] Dispersal down the Bomeo-
Bangka upland bridge. [3] Filtering across the drainage headwaters, into Java and
Sumatra, giving rise to moloch on Java and agilis on Sumatra. [4] First dispersal from
Sumatra into peninsular Thailand possibly by agilis although this is not clear. [5]
Secondary dispersals: from Sumatra into Malaya by agilis, from peninsular Thailand into Malaya and north Sumatra by lar.
Chivers (1977) assimilated data on the sequence of geological, climatic, floral and faunal events during the last few million years. This study provided a chronological history of the palaeoenvironment of Southeast Asia, including possible migration routes for different gibbon taxa. Chivers made several assumptions concerning the radiation of the three gibbon subgenera which he recognised at that time: Hylobates, Symphalangus and Nomascus. He first assumed that the ancestral hylobatid was probably large and black, more like the siamang. This presumptive hylobatid ancestor was, he proposed, distributed throughout much of the south Asian mainland and extended out onto the
Sunda Shelf, less than 3 million years ago (see Figure 1.15). According to Chivers’
64 scenario the siamang, being the first to evolve, would have spread and become isolated on the southern edge of the Sunda Shelf at about 800,000 years ago. The concolor- gibbons radiated north-easterly on the mainland, while subgenus Hylobates spread over the rest of Sundaland about 500,000 years ago. Chivers proposed that the latter radiation occurred as the savannah spread and sea level rose. This had the effect of separating subgenus Hylobates into several populations. By 350,000 years ago the mainland form had given rise to hoolock (which Chivers proposed at that time belonged to the
Hylobates subgenus), and possibly klossii once it was isolated in the west. Chivers suggests that an island form of Hylobates on the eastern edge of the Sunda Shelf gave rise to pileatus, muelleri, moloch, lar and agilis. This was triggered by a rise in sea level at about 100,000 years ago allowing the eastern population to spread westwards and southwards through expanding forests, firstly by ancestors of lar/agilis, then by moloch.
Pileatus arose via individuals crossing the rivers draining into the South China sea to go
Northwest, after the ancestral hoolock had moved further Northwest back into mainland
Asia.
During the last glaciation of the Late Pleistocene Chivers suggests that the lar/agilis ancestor spread north, back onto the Asian mainland. The siamang contracted
slowly northward into the mountains of Sumatra and moloch became isolated in Java.
Palaeoenvironmental changes led to the differentiation of agilis in Sumatra and lar on
the mainland of the Malay Peninsula. Over time lar spread slowly northward between
hoolock and pileatus. When sea level lowered for the last time, the final radiation of
several taxa occurred. The siamang spread across into the Malay Peninsula, along with
agilis, which also migrated east into Borneo. Lar spread into northern Sumatra. When
sea level rose again, Chivers concluded that the resultant distribution we see today was
established.
65 Chivers’ (1977) and Groves’ (1972) schematic illustrations of the radiation of gibbons are useful for visualising the possible migration routes of different taxa, however, they are not congruent with recent advances in molecular estimates of the possible timing of the gibbon radiation. Hence, an up to date reconstruction of the pattern of radiation of gibbons considering new molecular and phylogenetic evidence is warranted. The purpose of this study is to provide a new scenario to describe the pattern of distribution of hylobatids. The estimate of gibbon phylogeny will be used within a cladistic biogeographic framework. Cladistic biogeography incorporates phylogenetic data into a biogeographic analysis in order to interpret the distribution pattern of a group of organisms.
66 carpenter!
pileatus
^esTT: ^ tu s lar
abbotti agiiis muelleri
moloch
Figure 1.14 Groves proposed dispersal of gibbons in the subgenus Hylobates (1972, p.46). See text for an explanation of numbers 1-5.
67 A SOO.OOOyrs B P - 6 0 0 j Nomascus /
HYLOBAfES
B 500.000 yrs B P -300" C 350,000yrs BP - 100'
- 3 0 0D 2 5 0 ,0 0 0 yrs B P - 3 0 0D E 100,000yrs BP \ -100
Figure 1.15 Chivers’ (1977) Model for the evolution of gibbons (parts A-E)
68 F 40,000yrs BP -200 G 2 0 ,0 0 0 yrs B P 5 0
mu
8,000 yrs B P H 12.000 yrs B P 50 -200
ll+s mu mu
J PRESENT 0" s syndactylus c concolor h hoolock k kiossi P pileatus mu muelleri mo moloch a agilis le lar entelloides Il : lar lar mu Iv lar vestitus
Figure 1.15 Chivers’ (1977) Model for the evolution of gibbons (parts F-J)
69 Figure 1.15 Key to Chivers’ (1977) Model for evolution of the gibbons.
(A) about 800,000 years ago during low sea level there was a trivergence of hylobatids across the Sunda Shelf - ancestral concolor to the east, ancestral siamang to the south, ancestral lar in centre; (B) about 500,00 years ago, spread of central population at a time of very low sea level; (C) about 350,000 years ago, break-up of central population by savannah and rise in sea level; (D) about 250,000 years ago hoolock evolves in isolation to the Northwest; (E) about 100,000 years ago, rise in sea level, second break up of central population; (F) about 40,000 years ago, pileatus evolves in isolation prior to lowering of sea level; (G) about 20,000 years ago, high sea level isolated lar, agilis, moloch and muelleri from each other; (H) about 12,000 years ago, low sea level permits the movement of agilis and syndactylus into Malaya, and simultaneously or later, lar into Sumatra and agilis into Borneo; (I) about 8,000 years ago a rise in sea level brought about final evolution in isolation to achieve (J) the present distribution. (± figures indicate deviations of sea level from present, in feet. The distribution of subgenera is indicated for Symphalangus by a solid line, Nomascus with a broken line, and Hylobates with a dotted line.) (Taken from Chivers, 1977, p. 560-561).
70 1.5 Sum m ary
Molecular, morphological and distribution data will be used to reconstruct the phylogenetic and biogeographic history of gibbons. Previous studies are reviewed in order to assess the requirements of this study. Other molecular studies have either failed to resolve relationships among gibbons, or have provided conflicting results. In this study original mitochondrial control region sequence data and published cytochrome b gene sequence data will be analysed phylogenetically to propose a new phylogeny for gibbons. A molecular clock will also be employed in order to calculate estimates of divergence dates within the genus.
Previous morphological studies have covered a wide range of gross morphology
(including skeletal, pelage and vocalisation characteristics) but, have either not included all eleven of the currently recognised species, or have failed to provide detailed assessments of morphologieal variability. This study will use craniodental and postcranial data in multivariate statistical analyses, the results of which will be compared with previous studies of the morphometric variation among gibbons (e.g.
Creel and Preuschoft, 1976, 1984). In addition, the morphological data will be.recoded for cladistic analysis. Results of the morphological study will be interpreted in light of the estimate of gibbon phylogeny based on molecular data. The extent to which gibbon morphology maps the molecular phylogeny, will be assessed.
Previous reconstructions of the biogeographic history of gibbons are reviewed.
The estimate of gibbon phylogeny generated in this study will be combined with distribution data to reconstruct the biogeographic history of gibbons. These data are analysed within a cladistic biogeographic framework to provide a new scenario for the pattern of radiation of gibbons.
71 Chapter 2: Molecular Study
Contents
Page numbers
2.1 Introduction and background to study 73
2.1.1 The mitochondrial control region 75
2.1.2 Mitochondrial cytochrome b gene 77
2.2 Materials and methods 79
2.3 Phylogenetic analysis 85
2.4 Phylogenetic results and discussion 89
2.5 Conclusions 123
72 2.1 Introduction and background to the study
The aim of this chapter is to reconstruct phylogenetic relationships among gibbons using molecular data. The use of molecular data in phylogenetic analyses has dramatically increased over the last two decades. This development has been in parallel with advances made in phylogenetic inference. Central to the development of molecular systematics has been the advent of new applications of the polymerase chain reaction
(PGR) for investigating variation in DNA on a large scale (Kleppe et aL, 1971; Mullis and Faloona, 1987). In conjunction with this, the design of broadly applicable sets of primers, and automated sequencing have revolutionised the study of DNA variation within and between species. The mitochondrial genome has been used extensively for investigating interspecific phylogenetic variability. It is an informative region for several reasons, including the fact that it is more abundant than nuclear DNA, it is inherited maternally, it doesn’t undergo recombination, and it has a high mutation rate.
Several regions of the mitochondrial genome have been used for interspecific analyses including; ribosomal RNA (12S and 16S), cytochrome oxidase I and II, cytochrome b, and the control region. Two main regions of the mitochondrial genome have been used in previous studies to analyse phylogenetic relationships among gibbons: cytochrome b gene, and ND4 and 5 genes (the NADH dehydrogenase complex) (Garza and Woodruff,
1992; Hayashi et aL, 1995; Hall et aL, 1996, 1998). However, as discussed in Chapter
1 (section 1.4.2) these studies have provided conflicting results regarding interrelationships among gibbons. Hence, it was decided further work was warranted in order to interpret gibbon phylogenetics.
Two regions of the mitochondrial genome were chosen for study: the control region and cytochrome b gene. The control region has been shown to be successful for
73 elucidating phylogenetic relationships among a variety of taxa, and has not previously been studied in gibbons (e.g. Zischler et aL, 1998; Gemmell et aL, 1996; Douzcry and
Randi, 1997; Tagliaro et aL, 1997). Previous studies have provided conflicting results regarding the variability of cytochrome b gene among gibbons (Garza and Woodruff,
1992; Hall et aL, 1996, 1998). Garza and Woodruff (1992) suggest that cytochrome b gene is able to resolve certain relationships within the genus Hylobates. Hall et at.
(1996, 1998) on the other hand, suggest that cytochrome b gene is unable to resolve relationships within the genus, arguing that this region evolves too slowly to pick up spéciation events which have occurred in the rapidly evolving gibbons. The cytochrome b gene data from each of these studies, between them covering all eleven currently recognised gibbon species, is available on the world wide web. In light of this, and the contradictory results of Hall et aL (1996, 1998) and Garza and Woodruff (1992), it was decided that a re-analysis of these data was warranted. In the next sections a brief introduction to the control region and cytochrome b gene is provided, followed by the materials, methods and results used and generated in this study.
74 2.1.1 The mitochondrial control region
The control region contains the only major non-coding segment of the animal mitochondrial genome, called the D-loop. Located between tRNA^'° and tRNA*’^®
(Figure 2.1) the length of this non-coding region varies extensively between species, and sometimes, within species (e.g. Mignotte et a l, 1990; Hayasakaet a l, 1991;
Hoelzel et al., 1991; see Gemmell et a i, 1996 for further references). Several studies have shown that the control region is the most rapidly evolving part of the mitochondrial genome (Upholt and Dawid, 1977; Walberg and Clayton, 1981; Chang and Clayton, 1985) and as such it offers a putatively informative area for addressing evolutionary relationships among closely related species and/or subspecies. Studies of the mammalian control region have revealed that it broadly comprises three areas: a conserved central domain, a divergent left domain (adjacent to the tRNA*’'°), also known as hypervariable region I and a right domain (adjacent to the tRNA^^^), also known as hypervariable region II. The central domain exhibits extended nucleotide similarities between species, and diverges no more than do the mitochondrial protein-coding genes
(Brown et al., 1986). The left domain contains termination-associated sequences (TAS elements), and the right several conserved sequence blocks (CSBs), the latter being implicated in the initiation of H strand replication (Gemmell et a l, 1996). These non coding regions flanking the conserved central domain appear to be free to vary and are thought to be more rapidly evolving (Gemmell et al., 1996). They contain many polymorphic sites and the control region has been used to resolve phylogenetic problems for a variety of taxa. The control region has been used with success to elucidate interspecific phylogenetic relationships among birds (e.g. Wenink et al., 1996;
Zink and Blackwell, 1998) fish (e.g. Lee et al., 1995; Alvarado Bremer et al., 1997) and mammals (e.g. Zischler et a i, 1998; Gemmell et al., 1996; Douzery and Randi, 1997;
Tagliaro et al., 1997).
75 The control region has not previously been studied with regard to variability
among gibbons. Furthermore, other regions of the mitochondrial gene which have been
analysed in order to address this question (e.g. cytochrome b gene and NADH complex) have provided incongruent results (e.g. Garza and Woodruff, 1992; Hayashi et a l,
1995; Hall et a l, 1996, 1998). Hence, in light of this and the usefulness of the control region in phylogenetic studies on other mammalian taxa, it was chosen as the target region for study in this analysis of variability among gibbons.
15395 15932
L 15996 H16498 _A Z___ 14167 15308 15444 16472
tRNA tRNA tRNA
Phe Cytochrome b Thr Pro CONTROL REGION
Figure 2.1 Schematic diagram of the sections of mtDNA analysed in this study.
Key\ LI 5996 and HI 6498 refer to the position of primers used in this study. Numbers
14167-16472 refer to the various section of mtDNA analysed in this study.
76 2.1.2 Mitochondrial cytochrome b gene
The cytochrome b gene has commonly been used in phylogenetic analysis of a variety of vertebrate taxa. For example, Irwin et al, (1990) examined the molecular evolution of cytochrome b gene among a variety of mammals. Two molecular studies have used cytochrome b gene to assess phylogenetic relationships among hylobatids
(Garza and Woodruff, 1992; Hall et ah, 1996, 1998). Hall et al., (1996, 1998) concluded that cytochrome b gene does not evolve fast enough to pick up spéciation events that have occurred in hylobatid history. Hence, they were unable to resolve relationships among gibbons using this gene. However, Garza and Woodruff (1992) were able to resolve some relationships among gibbons using cytochrome b gene. In addition, neither study has analysed sequence data for all currently recognised species of gibbons. In light of the contradictory results of Hall et al. (1996, 1998) and Garza and Woodruff (1992) it was decided to re-analyse the published cytochrome b gene sequences available on the EMBL/GenBank (European Bioinformatics Institute)
Nucleotide Sequence Database (http://www.ebi.ac.uk). There are cytochrome b gene sequences, 1141 base pairs in length, available for 10 different species of gibbon, plus one partial cytochrome b gene sequence, 252 base pairs in length representing-one species {H. concolor). The complete human mitochondrial genome was used as an outgroup in phylogenetic analyses. Details of the source references for these sequences can be found in Table 2.1.
77 Table 2.1 Source of Cytochrome b gene sequence data
Species Source reference EMBL Sequence Accession no. length (bp) H. lar Hall et a l, 1998 Y13301 1141 H. muelleri Hall et a l, 1998 Y13300 1141 H. moloch Hall and Wood, 1998 AJO10580 1141 H. agilis Hall and Wood, 1998 AJO10583 1141 H. pileatus Hall and Wood, 1998 AJ010582 1141 H. klossii Hall and Wood, 1998 AJ010581 1141 H. hoolock Hall etal, 1998 Y13304 1141 H. concolor Garza and Woodruff, 1992 L02762 252 H. gabriellae Hall et a l, 1998 Y13307 1141 H. leucogenys Hall etal, 1998 Y13306 1141 H. syndactylus Hall etal, 1998 Y13302 1141 Homo sapiens Anderson et a l, 1981 J01415 16569 complete mt. gene
78 2.2 M aterials and Methods
Hair samples for the molecular part of this study, donated by Dr. Malcolm Hall
formerly of the University of Liverpool, were the primary resource used. These plucked
hairs were obtained non-invasively from captive gibbons housed in zoos. (See Table 2.2
for a list of the origin of each hair sample). Taxonomic identity was confirmed by
Malcolm Hall using the Studbook for gibbons (Da Volls, 1994), and following
Geissmann’s (1995) taxonomy. Hairs were stored in ethanol, and kept at -70°C until used. The collection comprised several individual hairs from both males and females,
from all eleven currently recognised species. These materials are now stored in the
Department of Biology at University College London.
79 Table 2.2 Origin of gibbon hair samples
Sample No. Studbook No. Individual's name SexAge/DOB Species Date Collected Origin no no. Travolta F 18 H. lar U Perth Zoo, Australia no no. U M U H. lar U Perth Zoo, Australia no no. U F U H. lar U Perth Zoo, Australia 80 Jane F 1962 H. lar u Twycross Zoo, UK 127 Mona Lisa F U H. lar u Twycross Zoo, UK 196 Nicholas M 21/12/88 H. lar u Twycross Zoo, UK 79 Susannah F 31/10/76 H. pileatus u Twycross Zoo, UK 191 Rowwen M 12/06/88 H. pileatus u Twycross Zoo, UK No. 6 no no. U M U H. pileatus u Mulhouse Zoo, France 88 Sabina F U H. agilis u Twycross Zoo, UK 92 Smiler M 1963 H. muelleri u Twycross Zoo, UK 102 Bilou M 1967 H. kiossi u Twycross Zoo, UK 00 o 199 Imran M U H. moloch 20/05/94 Howletts Wild Animal Park, UK 200 Omar M U H. moloch 24/06/94 Howletts Wild Animal Park, UK 201 Marlene F U H. moloch 14/04/94 Howletts Wild Animal Park, UK 202 Shewok F U H. moloch 21/05/94 Howletts Wild Animal Park, UK 220 Assini F U H. moloch 24/06/94 Howletts Wild Animal Park, UK 232 Iwok M U H. moloch 20/05/94 Howletts Wild Animal Park, UK 242 Yoni FU H. moloch 24/06/94 Howletts Wild Animal Park, UK 249 L'upau M U H. moloch 25/05/94 Howletts Wild Animal Park, UK 265 Loci F U H. moloch 30/01/94 Howletts Wild Animal Park, UK 266 Hilo M U H. moloch 23/05/94 Howletts Wild Animal Park, UK no no. Liban M 13.5 H. moloch U Perth Zoo, Australia no no. U M U H. moloch U Perth Zoo, Australia no no. Horace ■ M U H. hoolock U Perth Zoo, Australia no no. Flossie F U H. hoolock U Perth Zoo, Australia Table 2.2 continued. Origin of gibbon hair samples
Sample No. Studbook No. Individual's name Sex Age/DOBSpecies Date Collected Origin no no. U M U H. concolor U Perth Zoo, Australia no no. U F U H. concolor U Perth Zoo, Australia No. 1 10 Mimi F U H.c.siki U Mulhouse Zoo, France No. 2 32 Charlotte F u H.c.siki u Mulhouse Zoo, France No. 3 40 Emilia F u H.gabriellae u Mulhouse Zoo, France No. 4 no no. U M Young H.gabriellae u Mulhouse Zoo, France No. 5 no no. U M Young H.gabriellae u Mulhouse Zoo, France no no. 40 Emilia F U H.gabriellae u Mulhouse Zoo, France no no. 40 Emilia F U H.gabriellae u Mulhouse Zoo, France no no. Racqual F 20 H. leucogenys12/03/94 Perth Zoo, Australia 93 Clara F 1974 H. leucogenysU Twycross Zoo, UK 72 Zoa F 1970 H. leucogenysU Twycross Zoo, UK 229 Kana M 03/11/90 H. syndactylus U Twycross Zoo, UK 165 Sorral F 18/03/86 H. syndactylus U Twycross Zoo, UK 96 Kajang M 1964 H. syndactylus U Twycross Zoo, UK 207 Magog F U H. syndactylus 18/01/94 Howletts Wild Animal Park, UK no no. Ringo M U H. syndactylus 17/03/94 Perth Zoo, Australia DNA extraction
DNA was extracted from the hairs following a simple and commonly used protocol outlined by Walsh et al. (1991). One to three hairs from each individual representing all 11 taxa were first washed in chloroform and water. The proximal 5mm
containing the root was then excised using a sterile razor blade. Prior to extraction the hairs were digested in a 1:100 concentration of Proteinase K overnight at 56°C.
Following this, DNA was extracted from the hair roots according to the method of
Walsh et a/. (1991) by boiling, incubating and agitating in Chelex® 100.
DNA amplification
A 537 bp section of hypervariable region 1 of the control region was amplified by PCR using the following two primers (see Figure 2.1 for the position of these primers in relation to the control region):
L15996 5’-CTCCACCATTAGCACCCAAAGC-3’ (Vigilant et a l, 1989)
HI6498 5’-ATGACCCTGAAGTAGGAACCAGATG-3’ (Kocher and Wilson,
1991)
Amplifications were carried out in a 50pl volume containing lOx pH buffer, nucleotides, 2.5pM mix of primers LI 5996 and HI 6498, taq polymerase, water and Ipl
of DNA template. Amplification conditions comprised the following: a preliminary denaturing step of 95°C for 4 minutes, another denaturing step at 94°C for 1 minute,
followed by 40 cycles of primer annealing at 54°C each for 1 minute, extension at 72°C
82 for 1 minute, and a final extension at 72°C for 10 minutes. The final step cooled to 4°C.
An extraction control omitting hair was used as a negative PCR control.
A lOpl aliquot of each PCR product was electrophoresed in a 2% agrose gel stained with ethidium bromide. The DNA fragment was excised from the gel with a sterile razor and washed thoroughly in sterile water, with minor agitation, a minimum of five to six times. The excised band containing DNA was purified according to the
GENECLEAN® (BiolOl) protocol, the purified DNA being stored in pH9 TE buffer. A
Ipl aliquot of this solution was used as a template for PCR reamplification in a 50pl reaction using the same concentration of reaction ingredients as the initial amplification.
The reamplification conditions differed from the initial amplification conditions in that the annealing temperature was increased to 60°C, and the number of cycles decreased to
25. All other amplification conditions remained the same.
The resultant PCR product was purified for a final time in preparation for direct sequencing using centricon® columns. 5pi of the final DNA template was electrophoresed in a 2% agrose gel stained with ethidium bromide, in order to calculate the concentration of DNA. This was then used to calculate the volume of final PCR product to be used in the sequencing reaction.
83 Sequencing
A 537 bp section of the control region was sequenced in both directions using a
Perkin Elmer ABI Prism®, 377 DNA Sequencer. This used a BigDye’^'^ Terminator
Cycle Sequencing Ready Reaction Kit containing dyes which label the nucleotides, A,
G, C, and T. This study generated 10 sequences from each of the following gibbons
(Appendix II): H. muelleri, H. moloch, H. pileatus, H. agilis, H. hoolock (2 sequences from the same individual), H. concolor (2 sequences from the same individual), and 77. concolor siki (2 sequences from the same individual).
This data set was supplemented by three sequences taken from the
EMBL/GenBank Nucleotide Sequence database (Table 2.3)
Table 2.3 Source of human and gibbon sequence data.
Species Source reference EMBL Sequence
Accession no. length (bp)
H. lar Amason et a i, 1996 X99256 16472
complete mt. gene
H. syndactylus Zischler g/ a/., 1998 AF035759 398
partial D-loop
Homo sapiens Anderson et al., 1981 J01415 16569
complete mt. gene
84 2.3 Phylogenetic analysis of hylobatid sequence data
Sequence Alignment
The above analyses resulted in two different sets of sequences: 12 sequences for cytochrome b gene (including human) retrieved from Genbank on the world wide web, and 13 sequences for the control region (including human), 10 of which are new, original sequences, plus three sequences from Genbank. The two datasets were compared with each other and with existing phytogenies for the hylobatids, using both maximum likelihood and maximum parsimony methods, in order to address questions regarding interrelationships among hylobatids.
Prior to phylogenetic analysis the sequences were aligned using the multiple sequence alignment package Clustal X available at: ftp://ftp.ebi.ac.uk/pub/software/dos/ clustalw/clustalx/. This method makes pairwise alignments and then adds the sequences together by inserting additional gaps as needed, hence maximising matches between identical nucleotides. It assigns different scores to different matches so that it can distinguish between substitution events (mismatches) and indels (gaps). Similarity scores are then calculated between taxa. Using these similarity scores a dendogram is constructed using UPGMA (unweighted pair group method using arithmetic averages).
UPGMA is a form of cluster analysis which represents similarity or distance data in the form of a tree (Hillis et al., 1996). The sequences are then aligned following the order specified by the dendogram (Higgins and Sharp, 1988).
In order to determine the optimal alignment the gap penalties were altered in the multiple alignment parameters settings in Clustal X. The resultant alignments were then compared and on visual inspection the best alignment selected. For the cytochrome b dataset no differences were observed between the various alignments, hence the optimal
85 alignment selected was the one based on the default gap penalties. Analysis of the control region dataset resulted in several alignments which differed slightly. In order to ensure that these differences would have no significant effect on the phylogenetic analyses, two alignments using the control region data were used. One control region alignment was based on the default gap penalties, henceforth referred to as the control region (CR) 1 dataset. Another alignment was also chosen based on lower gap penalties, henceforth referred to as the control region (CR) 2 dataset.
Maximum likelihood methods of phylogenetic inference
Maximum likelihood methods of phylogenetic inference evaluate a hypothesis about evolutionary history in terms of the likelihood that a proposed model of the evolutionary process and the hypothesised history would give rise to the observed data
(Swofford et al., 1996). In this study the data are nucleotide sequences, the unknowns are the branching order and branch lengths of the tree. The aim of maximum likelihood is to discover the tree that gives the highest probability of a data set being derived from it. The procedure requires one or more trees, a probabilistic model of evolution and a data set. On generating a tree, or number of trees, the likelihood for a particular site is calculated as the sum of the probabilities of every possible reconstruction of ancestral states given a model of base substitution. Such models of sequence evolution calculate the probability of nucleotide substitution. Kimura’s (1980) two-parameter model (K2P) was employed in this study. K2P is a simple method for estimating the evolutionary rate of base substitutions. It takes into account the common observation that transitions and transversions occur at different rates, but still assumes equal base frequencies. K2P is one of the most commonly used base substitution models. Maximum likelihood
86 analyses were implemented using the phylogenetic inference package PHYLIP
(Felsenstein, 1995).
Sequence data were also analysed under the assumption of a molecular clock.
Molecular clocks assume that the substitution rate is approximately homogeneous across lineages. They work by estimating branching times rather than the lengths of each branch (i.e. all taxa are equidistant from the root). Hence, by assuming a molecular clock, rough estimates of the time of sequence divergence of species can be calculated.
The transition / transversion ratio (Ts/Tv) for the sequences involved in the analysis is required for this type of analysis. In this study the Ts/Tv ratio was calculated using the distance option in PAUP* (Swofford, 2000). Estimation of the Ts/Tv ratio using this method yielded a value of 7.8 for cytochrome b gene, and a value of 1.5 for each of the control region datasets. These estimates were then incorporated into the analyses, implemented using PHYLIP. Maximum likelihood analysis assuming a molecular clock produces a tree in which branch lengths are directly proportional to time. In order to calculate divergence dates at nodes within the tree, a calibration date must be set for the deepest or most proximal node, in this case the great ape-gibbon split (Tyler, 1.993;
Amason et al., 1996). Divergence dates for successively more distal nodes can then be estimated as a measure relative to the calibration point.
87 Alternative methods for analysing nucleotide sequence data - Maximum parsimony
The parsimony approach to phylogenetic analysis will be discussed in section
3.4.3, so here only its application to sequence data will be outlined. In general, parsimony methods operate by selecting trees that minimise the total tree length, i.e. the number of evolutionary steps (transformations from one character state to another) required to explain a given set of data. Regarding nucleotide sequence data, these steps are base substitutions. The four nucleotides are treated as unordered multistate characters. Parsimony and bootstrap analyses were implemented using PAUP Version
3.1.1 (Swofford, 1993). Cladograms were constructed using the branch and bound method. This method was employed since other search mechanisms (e.g. exhaustive) are too slow, while others (e.g. heuristic) do not guarantee to find the shortest tree
(Kitching etal., 1998).
88 2.4 Phylogenetic results and discussion
Cytochrome b gene
Maximum Likelihood
Maximum likelihood analysis yielded a tree (Figure 2.2) which shows each of
the four gibbon subgenera Nomascus, Symphalangus, Bunopithecus and Hylobates as monophyletic. This is in agreement with previous studies based on molecular, morphological and behavioural data (e.g. Chivers, 1977; Haimoff et al., 1982; Cronin et a l, 1984; Marshall and Sugardjito, 1986; Geissmann, 1993, 1995; Garza and Woodruff,
1992; Hayashi et al., 1995; Hall, et a l, 1996, 1998). The tree shows the following relationships: the subgenera Symphalangus and Bunopithecus are successively more closely related to subgenus Hylobates. Interrelationships among taxa in the subgenus Nomascus could not be resolved using this method. Hence, the tree shows a polytomy between concolor, gabriellae and leucogenys. Within subgenus
Hylobates, pileatus and klossii are sister taxa, and agilis is sister taxon to {lar + muelleri).
89 human
concolor {Nomascus)
gabriellae {Nomascus)
leucogenys {Nomascus)
syndactylus {Symphalangus)
hoolock {Bunopithecus)
moloch {Hylobates)
agilis {Hylobates)
lar {Hylobates)
muelleri {Hylobates)
klossii {Hylobates)
pileatus {Hylobates)
Figure 2.2 Maximum likelihood tree based on cytochrome b gene sequence data. (Subgenera are indicated in brackets).
90 Maximum likelihood assuming a molecular clock
The maximum likelihood tree constructed under the assumption of a molecular clock (Figure 2.3) is similar to the tree constructed using maximum likelihood analysis.
The tree shows that the subgenera Nomascus, Symphalangus and Bunopithecus are successively more closely related to subgenus Hylobates. Relationships within subgenera are different in the two analyses. The tree depicted in Figure 2.3, assuming a molecular clock, shows that within the subgenus Nomascus, leucogenys is sister taxon to {concolor + gabriellae). Within subgenus Hylobates, moloch and agilis are successively more closely related to a clade comprising {lar + muelleri) and {klossii + pileatus).
Under the assumption of a molecular clock it is possible to give estimates of the dates of divergence at different nodes on the tree (Figure 2.3, Table 2.4). These estimates were calibrated using the great ape-gibbon split. Dates for this divergence, however, are controversial, ranging from 12 Ma (million years ago) to 36 Ma, based on a variety of data. Since fossil hylobatids are scarce (Tyler, 1993) evidence for their divergence from the other apes has mainly originated from the field of molecular biology. Combined biochemical results based on analysis of blood groups and histocompatibility antigens, chromosome banding patterns, protein structure and antigenicity, amino acid sequences of proteins, and DNA endonuclease restriction mapping, sequencing and reassociation kinetics, indicate a great ape-gibbon split at no more that 15 Ma (see Tyler, 1993 and references therein). However, more recent molecular analyses based on the complete mitochondrial genome have pushed back the great ape-gibbon split to 36 Ma (Amason et a l, 1996). In light of this controversy the two alternative hypotheses for the date of this split (36 Ma and 15 Ma) are employed here to calibrate possible dates of divergence within the genus Hylobates (Table 2.4).
91 If the great ape-gibbon split is more ancient than previously believed this would
suggest that gibbon radiation began over 20 Ma (node A, Figure 2.3). According to
cytochrome b gene, this radiation involved the divergence of taxa in the subgenus
Nomascus, from the rest of the gibbons. A fairly prolific radiation followed between
about 20 and 10 Ma, involving syndactylus, hoolock and gibbons in the subgenus
Hylobates. Taxa in the subgenus Hylobates radiated between about 11 and 7 Ma.
According to this reconstruction, taxa in the subgenus Nomascus are a relatively more
recent radiation, diverging about 4 to 1 Ma (nodes I and J, Figure 2.3).
Using the more recent date for the divergence of great apes and gibbons, based
on combined evidence, it appears that the gibbon radiation dates to approximately 10
Ma (node A, Figure 2.3). According to this reconstruction, the clade comprising syndactylus, hoolock, and taxa in the subgenus Hylobates radiated between about 8 and
3 Ma. These estimates also indicate that taxa in the subgenus Nomascus represent a
recent radiation between about 1.7 and 0.3 Ma (nodes 1 and J, Figure 2.3).These results
are in agreement with previous findings based on molecular data which indicate that
there was a rapid radiation of hylobatids between approximately 10-6 Ma (Zehr et al.,
1996; Porter et al., 1997). Zehr et al. (1996) analysed cytochrome oxidase subunit 11
sequence data for various species of gibbon and estimated that there was a rapid
radiation of gibbons 6-8 Ma. Porter et al. (1997) analysed sequences of the e-globin
locus from a variety of primate taxa including two species of gibbon and estimated that
the gibbon radiation dates to approximately 9.9 Ma. Thus, the estimated divergence
dates presented in this study, based on cytochrome b gene, are compatible with previous
estimates.
92 human
— leucogenys {Nomascus)
’“‘concolor {Nomascus)
— gabriellae {Nomascus)
syndactylus {Symphalangus)
hoolock {Bunopithecus)
moloch {Hylobates)
agilis {Hylobates)
lar {Hylobates)
muelleri {Hylobates)
klossii {Hylobates)
pileatus {Hylobates)
Figure 2.3 Maximum likelihood tree constructed under the assumption of a molecular clock, based on cytochrome b gene sequence data. (Subgenera are indicated in brackets).
93 Table 2.4 Estimates of divergence dates for hylobatids based on maximum likelihood analysis of cytochrome b gene, assuming a molecular clock. Calibrated using two estimates of the great ape-gibbon split
Node * Estimates of divergence dates Estimates of divergence dates based on based on 36 Ma great ape-gibbon split 15 Ma great ape-gibbon split (Arnason et at., 1996) (Tyler, 1993)
Ma Ma A 24.7 10.5 B 20.3 8.6 C 18.2 7.7 D 11.9 5.1 E 11.4 4.8 F 10.9 4.6 G 8.8 3.7 H 7.2 3.1 I 4.2 1.8 J 0.8 0.3
* Nodes are depicted in Figure 2.3, maximum likelihood tree constructed under the assumption of a molecular clock using cytochrome b sequence data. Representations are as follows; A = split between the clade comprising taxa in the subgenus Nomascus and the clade comprising the rest of the hylobatids (subgenera Symphalangus, Bunopithecus, and Hylobates), B = split between syndactylus and hoolock, C = split between hoolock and clade comprising taxa in the subgenus Hylobates, D = split between moloch and clade comprising agilis, {lar + muelleri), and (klossii + pileatus), E = split between agilis and clade comprising (lar + muelleri) and (klossii + pileatus), F = split between (lar + muelleri) and (klossii + pileatus), G = split between klossii and pileatus, H = split between lar and muelleri, I = split between leucogenys and (concolor + gabriellae), J = split between concolor and gabriellae.
94 Parsimony analysis
Parsimony analysis of the cytochrome b dataset produced nine equally parsimonious trees. Figure 2.4 shows a strict consensus tree derived from the parsimony analysis. The topology of this tree is almost identical to the maximum likelihood analysis and maximum likelihood assuming a molecular clock. Again all of the subgenera are monophyletic and the tree shows the following relationships: the subgenera Nomascus, Symphalangus and Bunopithecus are successively more closely related to taxa in the subgenus Hylobates. The strict consensus tree can provide no resolution to relationships among taxa in the subgenus Nomascus. Within the subgenus
Hylobates, moloch forms a separate clade to a clade comprising agilis, {lar + muelleri) and {pileatus + klossii).
95 human
concolor {Nomascus)
gabriellae {Nomascus)
leucogenys {Nomascus)
syndactylus {Symphalangus)
hoolock {Bunopithecus)
moloch {Hylobates)
agilis {Hylobates)
lar {Hylobates)
muelleri {Hylobates)
klossii {Hylobates)
pileatus {Hylobates)
Figure 2.4 Maximum parsimony consensus tree based on cytochrome b gene sequence data. (Subgenera are indicated in brackets).
96 Bootstrap analysis
The bootstrap 50% majority rule consensus tree (Figure 2.5) has a similar topology to the parsimony and likelihood trees, showing the subgenera Nomascus,
Symphalangus and Bunopithecus are successively more closely related to subgenus
Hylobates. The tree shows that taxa in the subgenus Hylobates form a polytomy, including {lar + muelleri) as sister taxa. Bootstrap support for this clade is high, 97%, with 78% support for the {lar + muelleri) group. Taxa in the subgenus Nomascus also form a polytomy, with 100% bootstrap support. Support for the clades including syndactylus and hoolock, however, is lower at 67% and 50% respectively.
97 human
concolor {Nomascus)
gabriellae {Nomascus) 100%
leucogenys {Nomascus)
syndactylus {Symphalangus)
hoolock {Bunopithecus) 67%
- moloch {Hylobates)
50%
agilis {Hylobates)
lar {Hylobates)
97% 78% muelleri {Hylobates)
klossii {Hylobates)
pileatus {Hylobates)
Figure 2.5 Bootstrap 50% majority rule consensus tree based on cytochrome b gene sequence data. (Subgenera are indicated in brackets).
98 Control Region
Maximum Likelihood
Maximum likelihood analyses using the two control region datasets resulted in identical trees (Figure 2.6). The analysis yielded some unexpected results which contradict other findings (e.g. Chivers, 1977; Haimoff et a l, 1982; Cronin et al., 1984;
Marshall and Sugardjito, 1986; Geissmann, 1993, 1995; Garza and Woodruff, 1992;
Hayashi et al., 1995; Hall, et al., 1996, 1998). The trees indicate that moloch, agilis and pileatus form a separate clade, not only from the rest of subgenus Hylobates, but from all of the other gibbons. Possible explanations for this anomaly are discussed in section
2.5. Aside from this anomaly, the clade comprising the rest of the four subgenera indicates that lar, muelleri, hoolock, and syndactylus are successively more closely related to a clade comprising {concolor + concolor siki). Interrelationships between lar, muelleri and the other gibbons are not resolved. Hence, a polytomy between lar and muelleri is depicted in Figure 2.6.
Maximum likelihood (ML) analyses using control region data contradict findings of the ML using cytochrome b gene data (compare Figure 2.6 with Figure 2.2).
ML analysis of the control region datasets implies that the subgenus Hylobates is not monophyletic. This is on contrast to the ML analysis of cytochrome b gene data, plus many other published studies, which suggest that subgenus Hylobates is monophyletic.
(e.g. Chivers, 1977; Haimoff et a l, 1982; Cronin et a l, 1984; Marshall and Sugardjito,
1986; Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et al., 1995; Hall, et a l, 1996, 1998).
Aside from this anomaly, relationships among the four gibbon subgenera also differ between the ML analysis of cytochrome b gene and control region data. Analyses based on cytochrome b gene sequences result in a tree indicating that Nomascus,
99 Symphalangus, Bunopithecus are successively more closely related to taxa in the subgenus Hylobates. The analysis based on control region sequences (aside from the moloch {pileatus + agilis) anomaly) suggests Hylobates, Bunopithecus, and
Symphalangus are successively more closely related to Nomascus. In other words the sequence of evolution in the tree constructed from control region data is completely the reverse of the sequence observed in the cytochrome b dataset.
100 human
muelleri {Hylobates)
lar {Hylobates)
hoolock {Bunopithecus)
hoolock* {Bunopithecus)
syndactylus {Symphalangus)
concolor siki {Nomascus)
concolor siki* {Nomascus)
concolor {Nomascus)
concolor* {Nomascus)
moloch {Hylobates)
pileatus {Hylobates)
agilis {Hylobates)
Figure 2.6 Maximum likelihood tree based on control region sequence data. The same topology was obtained for each of the control region alignments, CRl and CR2. (Subgenera are indicated in brackets). * indicates a duplicate sample.
101 Maximum likelihood assuming a molecular clock
Maximum likelihood analyses assuming a molecular clock using the two control region datasets, resulted in two trees with different topologies (Figures 2.7 and 2.8).
Both trees show moloch and {pileatus + agilis) as a separate clade to all the other gibbons. Interrelationships between the other hylobatid taxa, however, differ between the two trees. Figure 2.7 (based on the control region 1 dataset) shows a tree in which hoolock and {muelleri + lar) form a separate clade to syndactylus and concolor. In
Figure 2.8 (based on the control region 2 dataset) concolor and concolor siki form sister taxa, and form a separate clade to {syndactylus + hoolock) and {muelleri + lar).
Divergence times were calculated using an early (36 Ma) and more recent (15
Ma) calibration date of the great ape-gibbon split, as before. In each case the results were similar for both trees. Assuming an early great ape-gibbon split of 36 Ma, divergence time was estimated to be between 20 and 22 Ma for the clade excluding moloch, pileatus and agilis (node A, Figure 2.8). Divergence time, assuming a great ape-gibbon split of 15 Ma, was estimated to be between 8 and 9 Ma for the clade excluding moloch, pileatus and agilis (node A, Figure 2.8). These estimates should, however, be treated with great caution since a significant departure from a molecular clock has been shown for the control region in this study, based on the significant differences in the sequences of certain closely related species of gibbon in the subgenus
Hylobates (compared to previous molecular estimates, and the cytochrome b gene data analysed here).
102 human
hoolock {Bunopithecus)
hoolock'^ {Bunopithecus)
muelleri {Hylobates)
lar {Hylobates)
syndactylus {Symphalangus)
concolor siki {Nomascus)
concolor siki* {Nomascus)
concolor {Nomascus)
concolor* {Nomascus)
moloch {Hylobates)
pileatus {Hylobates)
agilis {Hylobates)
Figure 2.7 Maximum likelihood tree constructed under the assumption of a molecular clock, based on the control region 1 dataset. Branch lengths are not shown. (Subgenera are indicated in brackets). * indicates a duplicate sample.
103 human
— concolor siki {Nomascus)
concolor siki*' {Nomascus)
— concolor {Nomascus)
concolor* {Nomascus)
syndactylus {Symphalangus)
— hoolock {Bunopithecus)
hoolock* {Bunopithecus)
muelleri {Hylobates)
lar {Hylobates)
moloch {Hylobates)
— pileatus {Hylobates)
— agilis {Hylobates)
Figure 2.8 Maximum likelihood tree constructed under the assumption of a molecular clock, based on the control region 2 dataset. (Subgenera are indicated in brackets). * indicates a duplicate sample.
104 Parsimony analysis
Parsimony analyses of the two control region datasets resulted in only one most parsimonious tree per analysis. The two trees have slightly different topologies (Figures
2.9, 2.10). Both trees agree with maximum likelihood analysis and maximum likelihood analysis assuming a molecular clock, as regards the anomalous grouping of moloch, agilis and pileatus. The trees differ in their grouping of hoolock. In the tree based on the control region 1 dataset, hoolock forms a sister group with {lar + muelleri), and together they form a separate clade to syndactylus as sister taxa to {concolor + concolor siki).
The tree based on the control region 2 dataset shows {lar + muelleri), hoolock and syndactylus are successively more closely related to a clade comprising {concolor and concolor siki).
The tree constructed using the control region 1 (CRl) dataset differs slightly from the tree constructed using the same dataset in the maximum likelihood analysis
(compare Figure 2.9 with Figure 2.6). The parsimony analysis of CRl produced a tree in which {lar + muelleri) form sister taxa to hoolock, and together these form a separate clade from syndactylus and {concolor + concolor siki). The tree constructed from maximum likelihood analysis using CRl shows hoolock forms a separate clade to {lar + muelleri), and that hoolock is intermediate in position between {lar + muelleri) and syndactylus.
Maximum likelihood analysis assuming a molecular clock based on the CRl dataset produced a tree which is similar to the parsimony analysis using the same dataset (compare Figure 2.9 with Figure 2.7)
The tree depicted in Figure 2.10 (based on the control region (CR) 2 dataset) has the same topology as the tree constructed using the same data in a maximum likelihood
105 analysis (compare with Figure 2.6). The trees show that, in the clade excluding moloch, pileatus and agilis, lar and muelleri form sister taxa, and that {lar + muelleri), hoolock and syndactylus are successively more closely related to a clade comprising {concolor + concolor siki).
The tree depicted in Figure 2.10 is different, however, from the tree constructed using the same dataset in a maximum likelihood analysis assuming a molecular clock
(compare with Figure 2.8). In the parsimony tree (in the clade excluding moloch, pileatus and agilis) {lar + muelleri) form a separate clade to a clade comprising syndactylus as sister taxon to {concolor + concolor siki) and forming a sister group to hoolock. In the maximum likelihood tree based on the assumption of a molecular clock
(in the clade excluding moloch, pileatus and agilis), {concolor + concolor siki) form a separate clade to a clade comprising syndactylus as sister taxon to hoolock, and forming a sister group to {lar + muelleri).
106 human
muelleri {Hylobates)
lar {Hylobates)
hoolock {Bunopithecus)
hoolock* {Bunopithecus)
syndactylus {Symphalangus)
concolor siki {Nomascus)
concolor siki* {Nomascus)
concolor {Nomascus)
concolor* {Nomascus)
moloch {Hylobates)
pileatus {Hylobates)
agilis {Hylobates)
Figure 2.9 Maximum parsimony tree based on the control region 1 dataset. (Subgenera are indicated in brackets).* indicates a duplicate sample.
107 human
muelleri {Hylobates)
lar {Hylobates)
hoolock {Bunopithecus)
hoolock'^ {Bunopithecus)
syndactylus {Symphalangus)
concolor siki {Nomascus)
concolor siki* {Nomascus)
concolor {Nomascus)
concolor* {Nomascus)
moloch {Hylobates)
pileatus {Hylobates)
agilis {Hylobates)
Figure 2.10 Maximum parsimony tree based on the control region 2 dataset. (Subgenera are indicated in brackets). * indicates a duplicate sample.
108 Bootstrap analysis
Bootstrap analysis of CRl produced a tree which has the same topology as the tree constructed from parsimony analysis of CRl (Figure 2.11). Bootstrap support for clades was typically high. There is 100% bootstrap support for the anomalous clade comprising moloch and {agilis and pileatus), and 99% support for the larger clade comprising the rest of the gibbons. There is 100% support for the following pairs of duplicate samples: {hoolock-^ hoolock*), {concolor + concolor*) and {concolor siki + concolor siki*). There is also 100% support for {concolor + concolor siki) as sister taxa.
There is high support, 99%, for the {lar + muelleri) sister group. Finally, there are low bootstrap values for the clade grouping {lar + muelleri) and hoolock (38%), and for the clade grouping syndactylus and {concolor + concolor siki) (44%).
Bootstrap analysis of CR2 produced similarly high bootstrap values for the same clades as CRl. The bootstrap 50% majority rule consensus tree (Figure 2.12) has the same topology as the parsimony tree constructed using CR2. Again there is 100% bootstrap support for the clade which separates moloch and {agilis + pileatus) from the rest of the gibbons. The tree shows there is 97% support for the clade separating the other gibbons. As with the CRl analysis, there is 100% support for the clades comprising duplicate samples, and for the clade which groups {concolor + concolor siki) as sister taxa. There is 98% support for the grouping of {lar + muelleri) as sister taxa. Again there is low bootstrap support for the clades which depict relationships between hoolock, syndactylus, {lar + muelleri) and {concolor + concolor siki); 52% for the clade that separates hoolock from syndactylus, and {concolor + concolor siki), and
60% for the clade that separates syndactylus from {concolor + concolor siki).
These bootstrap values indicate that for the control region there is very high support for the anomalous separation of moloch, agilis and pileatus from the rest of the
109 gibbons. In addition, there is high support for all of the duplicate samples indicating that systematic sequencing error was not a problem. Furthermore, consistently high bootstrap support for the grouping of {concolor + concolor siki) and {lar + muelleri) indicates these taxa are closely related, and provides evidence to suggest that sequencing errors were minimal. The low bootstrap values for clades comprising hoolock, syndactylus, {lar + muelleri) and {concolor + concolor siki) indicate that these control region analyses were unable to resolve interrelationships among these taxa.
110 Discussion
Maximum likelihood analysis, maximum likelihood analysis assuming a molecular clock, parsimony and bootstrap analyses of cytochrome b sequence data agree on the general relationship among hylobatid subgenera. Trees produced from all these types of phylogenetic analysis indicate that Nomascus, Symphalangus, and
Bunopithecus are successively more closely related to Hylobates, and that the subgenera are monophyletic. Interrelationships among taxa within subgenera differed between trees. Maximum likelihood analysis, parsimony and bootstrap analysis could not resolve relationships among taxa in the subgenus Nomascus. All the analyses based on cytochrome b sequence data indicate that lar and muelleri form sister taxa, and this is supported by a high bootstrap value. Maximum likelihood and parsimony analyses also indicate a sister taxon relationship between klossii and pileatus. The relationships of these two sister groups, (Jar + muelleri) and (klossii + pileatus), to agilis and moloch varied between the four analyses, although most trees indicate that moloch forms a separate clade to the other taxa in subgenus Hylobates.
Re-analysis of 11 published cytochrome b gene sequences is able to resolve relationships among the four gibbon subgenera. This is contrary to Hall et al. (1996,
1998) who concluded that they could not resolve relationships among gibbon subgenera using cytochrome b gene. One explanation for this could be that in each of Hall et aPs
(1996, 1998) studies only five and six species, respectively, of the 11 gibbon species are analysed.
Regarding interrelationships among the four gibbon subgenera, this study is broadly in agreement with Hayashi et al. (1995). As in this study, Hayashi et al. suggest that Nomascus and Symphalangus are respectively more closely related to Hylobates, although they do not include Bunopithecus in their study. The results of the above
113 analyses disagree with the findings of Garza and Woodruff (1992), who suggest that
Nomascus is more closely related to Hylobates, than Symphalangus is. Again this study does not include Bunopithecus.
Maximum likelihood and parsimony analyses of both control region datasets provide unexpected results which contradict other findings, including behavioural and morphological studies (e.g. Chivers, 1977; Haimoff et al., 1982; Cronin et al., 1984;
Marshall and Sugardjito, 1986; Geissmann, 1993, 1995; Garza and Woodruff, 1992;
Hayashi et al., 1995; Hall, et a l, 1996, 1998). The control region data generated and analysed in this study implies that subgenus Hylobates is not monophyletic. There could be several explanations for this contradiction: [1] systematic sequencing errors have occurred; [2] unusual evolutionary processes have occurred in the control region of gibbons; [3] these results are due to the effects of saturation; [4] nuclear inserts of the control region have been amplified; [5] subgenus Hylobates is not monophyletic and all other indicators are wrong.
Sequencing error seems an unlikely explanation for the unusual findings, since all three sequences consistently group together in all of the phylogenetic analyses. In addition, there is high bootstrap support for the clade separating moloch and (pileatus + agilis) from the rest of the gibbons.
Some authors have noted that the hypervariable parts of the control region exhibit an unusual evolutionary pattern (e.g. Pesole et a l, 1999, and references therein).
This peculiarity has manifest itself in a difficulty to align sequences from the hypervariable regions in organisms belonging to different taxa, and in some cases even the same taxon (Saccone et a l, 1991; Sbisà et a l, 1997). Aligning the control region sequences generated in this study proved problematic as discussed earlier in section 2.3.
114 As the structure, function and evolution of the control region becomes better understood, some of these problems may be overcome.
The anomalous results of these analysis may be due to the effects of saturation, which rapidly evolving regions of the mitochondrial genome have been shown to exhibit (Swofford et a i, 1996). Stoneking et al. (1992) have proposed that the control region evolves much more rapidly than other parts of the mitochondrial genome. They have estimated an average rate of control region sequence evolution of approximately
11.8% per Ma for human mitochondrial DNA. Transition saturation as shown by the low transition: transversion ratio (1.5 for the control region sequences produced in this study) calculated in this study, provides evidence to suggest that the control region of gibbon is also very rapidly evolving. Bearing this in mind, it is not surprising that the control region provides anomalous results regarding certain gibbon species, since it may be evolving to fast to have picked up spéciation events which have occurred. For instance, spéciation later than about 1 Ma may not be detected by control region sequence data. The divergence dates for gibbons estimated in the present study indicating spéciation in the order of a few million years rather than a few hundred thousand years, substantiate the proposal that the control region has evolved top rapidly to provide information regarding inter-subgeneric and inter-specific phylogenetic relationships.
Several studies have shown that mitochondrial DNA sequences are frequently inserted into the nuclear genome, where they evolve as nuclear pseudogene sequences
(e.g. Zullo et al., 1991; Zischler et al., 1995). Such sequences are characterised by a considerably slower rate of evolution than mitochondrial DNA sequences (Zischler et a l, 1995). Zischler et al. (1995, 1998) present evidence to suggest that a nuclear insertion of the mitochondrial control region occurred in the Hominoidea after the Old
World Monkey-Hominoidea split. This study has isolated the nuclear insertion of
115 control regions in the following hominoids: gibbon, orangutan, gorilla, chimpanzee, and human. Another possible explanation for the anomalous grouping of certain gibbons in the analyses presented here, could be due to the accidental amplification of a nuclear pseudogene. To test this hypothesis all of the control region sequences used in this study (13 in total) were analysed using a sequence similarity tool called BLAST
(Altschul et al., 1997) (available at http://www.ncbi.nlm.nih.gov/BLAST). This programme is part of the NCBI (National Centre for Biological Information) which comprises databases (e.g. Genbank) and analytical tools (e.g. BLAST). BLAST searches all of the NCBI’s databases for parts of a sequence that are similar to the sequence (in this case the gibbon sequences) under scrutiny. A BLAST search was carried out on all of the gibbon sequences individually. None of the nuclear insertions amplified by Zischler et al. (1995, 1998) were identified by the BLAST search. This provides evidence to suggest that the nuclear pseudogene has not been amplified in the control regions analysed in this study.
Finally, there is the possibility that these results accurately reflect relationships among gibbons. However, this seems highly unlikely considering the overwhelming amount of evidence for the monophyly of gibbon subgenera, based on morphological, molecular (also corroborated here with cytochrome b data) and behavioural data (e.g.
Chivers, 1977; Haimoff et al., 1982; Cronin et al., 1984; Marshall and Sugardjito, 1986;
Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et al., 1995; Hall, et a i,
1996, 1998).
In light of the evidence discussed above is seems likely that sequencing errors and nuclear insertions are not responsible for causing the anomalous separation of certain closely related species in the subgenus Hylobates. However, saturation provides a likely explanation for the unexpected tree topologies derived from phylogenetic analyses of control region data. A secondary additional cause of these findings may also
116 be explained by unusual evolutionary processes which have occurred in the control region, as substantiated by the alignment evidence.
117 Estimate of gibbon phylogeny
In light of the above, the results of the phylogenetic analyses based on the control region indicate that these data are unable to resolve relationships among the main groups of gibbon, one of the key aims of this study. Not only are the various trees, derived from parsimony and likelihood methods, based on the control region datasets incongruent with each other, they also show low bootstrap support (< 70%) for splits between the main subgenera. Bootstrap supports within species and subgenera are, however, generally high. For example, there is 98/99% support for the sister group
{muelleri + lar) (Figures 2.11, 2.12). This provides evidence to suggest that the control region may be useful for investigating relationships among closely related species that have speciated relatively recently (e.g. less than about 1 Ma). Furthermore, the control region may prove a useful tool for determining relationships among gibbon subspecies, and for example, the more recently speciated (according to the estimates calculated in this study) Bunopithecus gibbons.
Phylogenetic analysis of cytochrome b gene sequence data produced trees that are congruent both in terms of the different methods of analysis employed (i.e., likelihood and parsimony), and with regard to the monophyletic grouping of gibbon subgenera which is consistent with other studies (e.g. Chivers, 1977; Haimoff et al.,
1982; Cronin et a l, 1984; Marshall and Sugardjito, 1986; Geissmann, 1993, 1995;
Garza and Woodruff, 1992; Hayashi et al., 1995; Hall, et al., 1996, 1998). Furthermore, the monophyly of Nomascus and Hylobates are supported by high bootstrap values,
100% and 97%, respectively. The cytochrome b gene trees generated in this study all agree on the general relationships among the gibbon subgenera, and provide a gibbon phylogeny which shows the following relationships: Nomascus, Symphalangus, and
Bunopithecus are successively more closely related to subgenus Hylobates. Out of all of
118 the datasets analysed in this study, the phylogeny demonstrated by cytochrome b gene sequence data represents the most likely estimate of gibbon phylogeny.
At the species level, however, cytochrome b gene was unable to resolve relationships within subgenus Nomascus. Most tree produced a polytomy between concolor, gabriellae and leucogenys. This is probably due to the fact that these species are very closely related, and the recent estimate of divergence time for concolor, gabriellae and leucogenys (0.3-1.8 Ma) substantiates this. Cytochrome b gene was also unable to resolve relationships within the subgenus Hylobates. Parsimony and likelihood analysis provided slightly different results regarding the relationships between agilis and moloch and the other members of subgenus Hylobates {lar, muelleri, klossii, and pileatus). Both analyses, however, agree on the sister group relationships of
{lar + muelleri) and {klossii + pileatus).
The relationships between gibbon subgenera revealed in this study are in partial agreement with several other studies. Haimoff et a/.(1982) propose a gibbon phylogeny which shows the same relationships between gibbon subgenera: Nomascus,
Symphalangus and Bunopithecus are successively more closely related to Hylobates.
Hayashi et al. (1995) do not include Bunopithecus in their study but agree on the order of relationships between the other three subgenera, showing that Nomascus and
Symphalangus respectively are more closely related to Hylobates. Chivers (1977) also agrees that Bunopithecus is intermediate between either Nomascus!Symphalangus (this relationship is unresolved) and subgenus Hylobates.
In summary, the results of the analyses generated using cytochrome b gene can be regarded as a working estimate of gibbon phylogeny, which shows the following subgeneric relationships: Nomascus, Symphalangus and Bunopithecus are successively more closely related to Hylobates.
119 Divergence Dates
The estimates of divergence dates for gibbons, based on a calibration of 36 Ma for the great ape-gibbon split, pushes back the dates for hylobatid radiation by about 10
Ma compared to all previous estimates (e.g. Groves, 1972; Chivers, 1977; Zehr et al.,
1996; Porter et a l, 1997). Using a more recent calibration date (15 Ma), based on combined evidence, provides divergence dates in agreement with the previous molecular studies of Zehr et al. (1996) and Porter et al. (1997). These other molecular studies are based on different genetic loci: cytochrome oxidase subunit II and the 8- globin gene. Furthermore, Porter et al. (1997) have used a different calibration date than this study, namely the Old World Monkey-Hominoid split, dated at 25 Ma based on fossil evidence (Gingerich, 1984; Fleagle, 1999). Since dating evidence is available for the split of the most recent common ancestors of gibbons (e.g. Tyler, 1993) it was decided this was a more appropriate calibration point in the present study. Despite the different choice of the date of calibration, the results of this study and that of Porter et al. (1997), are congruent in terms of indicating a more ancient gibbon radiation: this study indicates a 10.5 Ma gibbon radiation (based on a 15 Ma calibration), and Porter et al., (1997) propose a gibbon radiation which dates to 9.9 Ma.
It is proposed that the estimate of 15 Ma for the great ape-gibbon split is more likely to be accurate than the 36 Ma estimate, based on several pieces of evidence.
Firstly, the date of 36 Ma for the great ape-gibbon split may be rejected on the basis that fossil evidence for the earlier Old World Monkey-Hominoid split is dated to 25 Ma.
This renders the Amason et al. (1996) estimate unlikely if the more recent fossil evidence is accepted. Secondly, if the fossils of Laccopithecus robustus are accepted as a likely gibbon ancestor, a Miocene date for the great ape-gibbon divergence seems more likely than an Oligocene/Eocene data, since Laccopithecus dates from the
120 Miocene. Thirdly, the 15 Ma estimate for the great ape-gibbon split is based on numerous independent lines of evidence including analysis of blood groups and histocomatibility antigens, chromosome banding patterns, protein structure and antigenicity, amino acid sequences of proteins, DNA endonuclease restriction mapping, sequencing and reassociation kinetics, derived from at least nine separate studies (See
Tyler, 1993, and references therein). Hence, more robustness may be applied to this combined estimate in terms of the congruence between nine separate analyses. In addition, the Amason et a l (1996) estimate of 36 Ma for the great ape-gibbon split is based on evidence from the mitochondrial genome. Thus, using this calibration point for estimating divergence dates from mitochondrial sequences introduces an element of circularity. Finally, these results are congruent with other molecular studies which provide evidence of a 6-10 Ma gibbon radiation on the basis of different genetic loci
(Zehr et a l, 1996; Porter et a l, 1997). On the basis of the above, the estimates of divergence dates for gibbons based on the 36 Ma great ape-gibbon split proposed by
Amason et a l, (1996) have been rejected. Hence, it is proposed that using a calibration point of 15 Ma, the gibbon radiation dates to 10.5 Ma.
However, there are several other factors which must be taken into consideration regarding estimates of divergence time. The accuracy in calculating estimates of divergence times is dependant on several factors including: the validity of a molecular clock for the dataset in question, the estimate of the divergence time of the most recent common ancestor (e.g. great ape-gibbon) being accurate, and the gene tree matching the tme species tree. The variability in the estimates of divergence times of different mammalian lineages (e.g. Amason et a l, 1996) has highlighted the problem that using different genetic markers can provide incongment phylogenetic information. As this study has shown, sequencing different parts of the mitochondrial gene can provide
121 different information regarding phylogenetic relationships. The fact that gene trees can differ from the species tree was first pointed out by Hudson (1983) and reiterated more recently by Nei (1987), Avise (1994) and Ruvolo (1997). These mismatches can be due to two factors, [1] sequencing a finite number of nucleotides at a locus produces stochastic sampling error, and potentially, an incorrectly inferred gene tree. This factor can be reduced by sequencing a sufficiently large DNA region from each locus. [2] gene tree-species tree incongruencies can be caused by genetic polymorphisms in an ancestral species followed by lineage sorting. This can be overcome by employing several independent loci in phylogenetic analyses (Saitou and Nei, 1986). For this reason estimates of divergence dates must be treated with caution.
122 2.5 Conclusions
A reassessment of cytochrome b gene sequence data provides evidence to suggest that cytochrome b can resolve relationships among hylobatid subgenera, although not at the inter-specific level. This is contrary to previous findings which suggest cytochrome b gene is unable to resolve relationships among gibbons (Hall et a l, 1996, 1998). Analysis of original control region sequence data provides anomalous results which contradict all other findings. The surprising variability found in the control region of hylobatids is likely due to the effects of saturation, with possible secondary effects being caused by unusual evolutionary processes which may have occurred during the evolution of the control region.
A new estimate of gibbon phylogeny is proposed based on cytochrome b gene sequence data, which agrees with previous studies on the monophyly of the subgenera.
The phylogeny shows the following relationships: Nomascus, Symphalangus, and
Bunopithecus are successively more closely related to subgenus Hylobates.
Furthermore, molecular clock estimates indicate that the gibbon radiation dates to 10.5
Ma, and this is congruent with other studies which have used different genetic.loci to estimate the timing of the gibbon radiation (Zehr et al., 1996; Porter et al., 1997).
123 Chapter 3: Morphological Study
Contents
Page numbers
3.1 Introduction 125
3.2 Materials 126
3.3 Measurements 130
3.4 Methods of analysis 148
3.4.1. Multivariate statistical methods 148
3.4.2 Multivariate statistical results and discussion 154
3.4.3 Cladistic methods 192
3.4.4 Cladistic results and discussion 202
3.5 Conclusions 225
124 3.1 Introduction
This chapter has two key aims: to investigate morphological diversity among gibbons using metric measurements from the whole skeleton, and to assess the usefulness of using cladistic re-coding techniques in relation to reconstructing gibbon phylogeny. In the first part of the chapter, metric (linear) data and multivariate statistical techniques are used to explore multivariate shape variation among gibbon taxa. The results of these analyses are used to test whether the variation maps the taxonomic divisions established by other characteristics (e.g. size, pelage, vocalisations, etc.). The second part of the chapter aims to test whether cladistic re-coding of univariate metric measurements can be used in phylogenetic analysis, and to test whether such data gives a meaningful phylogenetic signal in comparison with the estimate of gibbon phylogeny based on molecular data.
Previous morphological studies on taxa in the Genus Hylobates have not incorporated all currently recognised species and have been restricted to only five or six species (see section 1.2.1 for a detailed review of these studies). Although much is known about the gross morphological differences among these taxa, the morphological affinities of the other species in the genus have not been fully assessed. Detailed morphometric data from the cranium, dentition and postcranium is used in multivariate statistical analyses to assess size and shape differences among the eleven gibbon taxa.
Results of these analyses are compared with published interpretations of the morphological variability among gibbons (e.g. Creel and Preuschoft, 1976, 1984).
These data are also re-coded and used in cladistic analysis to interpret phylogenetic relationships.
125 3.2 M aterials
Standard linear measurements were collected from hylobatid skeletons housed in European and North American museums. Only adults were measured, their adult status being assessed by completed dental eruption and fusion of the cranial sutures.
Sex identification was made according to information on the museum labels, and recorded as male, female or unknown. Missing data was recorded as ‘nd’, no data.
All eleven species recognised by Geissmann (1995) were included in the study.
These are listed in Tables 3.1 (skulls) and 3.2 (postcrania) along with the number of individuals measured and the location of specimens. Museum collections tended to house their crania separately to the postcrania, hence, the number of individuals for each species is shown separately. The museums visited were: the Natural History
Museum, London (United Kingdom), the Rijksmuseum van Natuurlijke Histoire,
Leiden (The Netherlands), the American Museum of Natural History, New York
(United States of America), the Field Museum of Natural History, Chicago (United
States of America), and the National Museum of Natural History, Smithsonian Institute,
Washington DC. (United States of America). In addition, several skulls were sent for study on short-term loan from the Swedish Museum of Natural History, Stockholm
(Sweden). Material from University College London’s Grant Museum of Zoology and
Comparative Anatomy was also measured. In addition, a number of human skulls and postcrania were measured for comparative purposes for use in cladistic analysis. These are housed in University College London’s Department of Anatomy and Developmental
Biology.
Where possible, wild caught specimens of gibbons were measured in preference to animals kept in captivity This was not always possible, however, for those species for
126 which there were only small numbers. Also, matching skulls and postcrania were measured when possible, but in order to maintain sample sizes this was not always viable. Regarding H. gabriellae and H. leucogenys, many museums have not yet adopted the newly acquired species status of these taxa. In those cases where collections still catalogued such specimens as subspecies of H. concolor, the location data on all available specimens (including associated pelts) were checked to confirm their taxonomic status.
127 Table 3.1 Samples of hylobatid and human skulls measured in this study
Species Number of skulls Location of specimens measured
Hylobates lar 35 BMNH(35) Hylobates agilis 25 AMNH(16), BMNH(9) Hylobates moloch 14 AMNH(9), BMNH(5) Hylobates muelleri 31 BMNH(12), FMNH(19) Hylobates pileatus 11 AMNH(8), BMNH(3) Hylobates klossii 14 AMNH(6), BMNH(2), NMNH(6) Hylobates hoolock 16 BMNH(16) Hylobates concolor 3 BMNH(l), FMNH(2) Hylobates gabriellae 14 AMNH(2), BMNH(l), FMNH(7), SMNH(5) Hylobates leucogenys 12 AMNH(4), BMNH(4), FMNH(4) Hylobates syndactylus 21 AMNH(7), BMNH(14)
Total 196
Homo sapiens 11 UCLAnat.(ll)
Abbreviations for the museum collections which house hylobatid material, are as follows: AMNH (American Museum of Natural History, New York, USA), BMNH (Natural History Museum, London, UK), FMNH (Field Museum of Natural History, Chicago, USA), GMZ (Grant Museum of Zoology and Comparative Anatomy, University College London, UK), NMNH (National Museum of Natural History, Smithsonian Institute, Washington DC, USA), SMNH (Swedish Museum of Natural History, Stockholm, Sweden). Human material was measured at UCLAnat, (University College London, Department of Anatomy and Developmental Biology, UK)
128 Table 3.2 Samples of hylobatid and human postcrania measured in this study
Species Number of postcrania Location of specimens measured
Hylobates lar 15 AMNH(4), FMNH(6), MNH(5) Hylobates agilis 13 AMNH(4), NMNH(7), RNHL(2) Hylobates moloch 7 AMNH(l), MNH(2), RNHL(4) Hylobates muelleri 19 AMNH(2), FMNH(6), NMNH(IO), RNHL(l) Hylobates pileatus 2 AMNH(l), NMNH(l) Hylobates klossii 13 AMNH(4), BMNH(l), NMNH(8) Hylobates hoolock 8 AMNH(7), BMNH(l) Hylobates concolor 4 BMNH(2), NMNH(2) Hylobates gabriellae 13 AMNH(2), FMNH(IO), NMNH(l) Hylobates leucogenys 4 AMNH(l), NMNH(3) Hylobates syndactylus 17 AMNH(3), FMNH(4), NMNH(IO)
Total 115
Homo sapiens 11 U C L A n atd l)
Abbreviations for museum collections follow Table 3.1
129 3.3 Measurements
Measurements were chosen to reflect interspecific morphological variation.
Forty one standard cranial variables and thirty four standard postcranial variables were measured whenever possible, depending on completeness of the skeletons. Definitions of these measurements are given in Table 3.3 (including the source of each measurement), and illustrated in Figures 3.1 - 3.12. Measurements were selected from three main sources: Bass (1995), Wood (1975 and 1991) and Chamberlain (1987). The composite list was designed to represent as many anatomical parts or areas of the skeleton as possible.
Data were collected using two main types of sliding callipers. For measurements under 150mm (for example, most craniodental measurements), Mitutoyo^"^ digital callipers were used. For longer measurements (for example, long bone lengths), specially extended 500mm callipers, with extendible jaws, were utilised. A specially adapted set of sliding callipers was used to measure variable number 19 (Palate depth at
M'). Attached to the posterior end of these callipers is a horizontal perspex bar, at 90° to the sliding scale. This bar was placed on the occlusal surfaces of the left and right M', and the sliding scale moved vertically away from the roof of the palate. Variable number 65, the midshaft circumference of the clavicle, was measured using a piece of dental floss. The circumference was measured by wrapping the length of floss around the midshaft, then placed on the sliding scale of the callipers to determine the length.
130 Table 3.3 List of measurements and definitions, indicating the source of each variable, the original variable number, and the intraobserver error calculated in the present study.
V ariable Measurement Definition Source & Original Intraobserver No. measurement No. error
1 I* buccolingual diameter I' breadth, buccal to lingual Wood (1975)# 1 0.009 (0.9%) 2 I' mesiodistal diameter l‘ width, mesial to distal Wood (1975) #2 0.022 (2.2%) 3 buccolingual diameter I^ breadth, buccal to lingual Wood (1975) #3 0.028 (2.8%) 4 mesiodistal diameter L width, mesial to distal Wood (1975) #4 0.021 (2.1%) 5 C’ buccolingual diameter C* breadth, buccal to lingual Wood (1975) #5 0.020 (2.0%) 6 C‘ mesiodistal diameter C' width, mesial to distal Wood (1975) #6 0.013(1.3%) 7 C‘ labial height Height from the neck to the tip of the crown Wood (1975) #7 0.033 (3.3%) 8 Right orbit breadth Distance between dacryon and ectocochion Wood (1975) #52 0.011 (1.1%) 9 Right orbit height Maximum internal height of the orbit in a plane Wood (1975) #53 0.007 (0.7%) perpendicular to variable 8 10 Interorbital breadth Dacryon to dacryon Wood (1975) #54 0.035 (3.5%) 11 Nasion-nasospinale Chord distance between nasion and nasospinale Wood (1975) #57 0.010(1.0%) 12 Maximum nasal width Maximum width at the anterior edges of the nasal Wood (1975) #58 0.024 (2.4%) (pyriform) aperture 13 Bizygomatic breadth Maximum breadth across the zygomatic arches Wood (1975) #62 0.001 (0.1%) 14 Upper facial prognathism Chord distance between Porion and Glabella Chamberlain (1987) 0.001 (0.1%) #F15 15 Lower facial prognathism Chord distance between Porion and Alveolare Chamberlain (1987) 0.007 (0.7%) #F16 16 Palate length Prosthion to Staphylion Wood (1975) #64 0.049 (4.9%) 17 Inner alveolar breadth Minimum distance between the inner aspects of the Wood (1975) #65 0.007 (0.7%) alveolar processes at the level of M^ 18 Canine interalveolar distance Minimum distance between the upper canine alveoli Wood (1991) 0.007 (0.7%) Variable Measurement Definition Source & Original Intraobserver No. variable No. error
19 Palate depth at M The depth at the midpoint of M' to the roof of the Wood (1975) # 6 6 0.011 (1.1%) palate is measured using specially adapted sliding callipers with a horizontal perspex bar attached to the posterior end of the callipers at 90°. The horizontal bar is placed on the occlusal surfaces of each M' and the callipers extended as normal 20 Bregma - basion Chord distance between bregma and basion Wood 1975) #71 0.009 (0.9%) 21 Glabella - bregma Chord distance between glabella and bregma Wood 1991) 0.002 (0.2%) 22 Glabella - opisthocranion Chord distance between glabella and opisthocranion Wood 1991) 0.006 (0.6%) 23 Biporionic breadth Chord distance between left and right porion Wood 1991) 0.008 (0.8%) 24 Lambda - inion Chord distance between lambda and inion Wood 1991) 0.018 (1.8 %) 25 Lambda - opistbion Chord distance between lambda and opistbion 0.007 (0.7%)
26 ? 3 buccolingual diameter ? 3 breadth, buccal to lingual Wood 1975)# 25 0.014 (1.4%)
27 ? 3 mesiodistal diameter ? 3 width, mesial to distal Wood 1975) #26 0.029 (2.9%)
28 ? 4 buccolingual diameter ? 4 breadth, buccal to lingual Wood 1975) #27 0.009 (0.9%)
29 ? 4 mesiodistal diameter ? 4 width, mesial to distal Wood 1975) #28 0.026 (2.6%) 30 M, buccolingual diameter Ml breadth, buccal to lingual Wood 1975) #29 0.007 (0.7%) 31 M; mesiodistal diameter M, width, mesial to distal Wood 1975) #30 0.009 (0.9%)
32 Mj buccolingual diameter M 2 breadth, buccal to lingual Wood 1975) #31 0.018 ( 1.8 %)
33 Mj mesiodistal diameter M 2 width, mesial to distal Wood 1975)# 32 0.006 (0.6%)
34 M 3 buccolingual diameter M 3 breadth, buccal to lingual Wood 1975) #33 0.022 (2.2%)
35 M 3 mesiodistal diameter M 3 width, mesial to distal Wood 1975) #34 0.012 ( 1.2%) Variable Measurement Defînition Source & Original Intraobserver No. variable No. error
36 Bicondylar breadth Maximum distance between the outer aspects of the Wood (1975) #37 0.004 (0.4%) two condylar heads 37 Coronoid height Maximum vertical height of the right coronoid Wood (1975) #38 0.009 (0.9%) process from the plane of the base of the mandible 38 Bicoronoid breadth Maximum breadth between the outside aspects of the Wood (1975) #39 0.002 (0.2%) tips of the coronoid processes 39 Bigonial width Maximum distance between the outer aspects of the Wood (1975) #44 0.004 (0.4%) two gonia 40 Symphyseal height Oblique height from the base of the symphysis to Wood (1975) #47 0.005 (0.5%) infradentale 41 Symphyseal fossae Chord distance between the left and right symphyseal 0.006 (0.6%) fossae 42 Maximum length of humerus Bone is placed on a horizontal surface facing Bass (1995) 0.005 (0.5%) upwards (medially). The sliding callipers are extended to fit the maximum length of the bone 43 Humeral head diameter Maximum vertical diameter of the humeral head Wood (1975) 0.014 (1.4%) 44 A-P diameter at midshaft of Antero-posterior diameter of the humerus taken Wood (1975) #83 0.018 (1.8%) humerus midshaft 45 M-L diameter at midshaft of Medio-lateral diameter of the humerus taken Wood (1975) #84 0.013 (13%,) humerus midshaft 46 Biepicondylar breadth of humerus Distance between the lateral and medial epicondyles Wood 1975) #81 0.003 (0.3%) 47 Maximum length of Ulna Bone is placed on a horizontal surface facing Bass (1995) 0.008 (0.8%) upwards (medially). The sliding callipers are extended to fit the maximum length of the bone 48 Olecranon length Taken from the most proximal point on the olecranon 0.023 (2.3%) to the midpoint of the joint Variable Measurement Defînition Source & Original Intraobserver No. variable No. error
49 A-P diameter at midshaft of ulna Antero-posterior diameter of the ulna taken midshaft Adapted from Wood 0.014 (1.4%) (1975) #83 50 M-L diameter at midshaft of ulna Medio-lateral diameter of the ulna taken midshaft Adapted from Wood 0.021 (2.1 %) (1975) #84 51 Maximum length of femur Bone is placed on a horizontal surface facing Bass (1995) 0.006 (0.6%) upwards (medially). The sliding callipers are extended to fit the maximum length of the bone 52 Femur head diameter Maximum vertical diameter of the femoral head Wood (1975) #89 0.009 (0.9%) 53 Biepicondylar breadth of femur Maximum distance between the lateral and medial Wood (1975) #86 0.005 (0.5%) epicondyles 54 A-P diameter at midshaft of femur Antero-posterior diameter of the femur taken Wood (1975) # 87 0.017 (1.7%) midshaft 55 M-L diameter at midshaft of femur Medio-lateral diameter of the femur taken midshaft Wood (1975) #88 0.0 1 2 ( 1.2%) 56 Maximum length of tibia Bone is placed on a horizontal surface facing Bass (1995) 0.008 (0.8%) upwards (medially). The sliding callipers are extended to fit the maximum length of the bone 57 Proximal end breadth of tibia Greatest breadth of proximal end from the outsides 0.006 (0.6%) of the medial and the lateral condyles 58 Distal end breadth of tibia Greatest breadth at distal end 0.0 1 0 ( 1.0%) 59 Maximum midshaft breadth of tibia Maximum breadth of the tibia taken at midshaft 0.034 (3.4%) 60 Minimum midshaft breadth of tibia Minimum breadth of the tibia taken at midshaft 0.029 (2.9%) 61 Supraspinous fossa Taken from the superior angle to the point where the Aiello and Dean 0.016(1.6%) scapula spine bisects the medial border (1990) 62 Infraspinous fossa Taken from the inferior angle to the point where the Aiello and Dean 0.011 (1.1%) scapular spine bisects the medial border (1990) Variable Measurement Definition Source & Original Intraobserver No. variable No. erro r
63 Scapular Breadth Maximum breadth from the middle of the dorsal border Aiello and Dean 0.025 (2.5%) of the glenoid fossa, to the end of the spinal axis on the (1990) vertebral border 64 Clavicle maximum length Bone is placed on a horizontal surface facing upwards Bass (1995) 0.009 (0.9%) (medially). The sliding callipers are extended to fit the maximum length of the bone 65 Clavicle midshaft circumference Taken using strips of dental floss Bass (1995) 0.024 (2.4%)
66 Manubrium length Taken from jugular notch to attachment to body of Bass (1995) 0.047 (4.7%) sternum 67 Manubrium breadth Measured at the base of the attachment site of the 1st rib Bass (1995) 0.038 (3.8%) w 68 Body length Measured from the attachment of the manubrium to the Bass (1995) 0.019(1.9%) attachment of the xiphoid process 69 Body breadth Measured below the attachment site of the 3rd rib Bass (1995) 0.025 (2.5%) 70 Xiphoid length Maximum length of the xiphoid process Bass (1995) 0.034 (3.4%) 71 Iliac height Greatest length from centre of acetabulum to tubercle of Henry (1975) 0.013 (1.3%) the iliac crest 72 Iliac width Taken from posterior superior iliac spine to anterior Henry (1975) 0.008 (0.8%) superior iliac spine 73 Pubic length The shortest distance between the rim of the acetabular Wood (1975) 0.016(1.6%) fossa and the symphyseal aspect of the pubic bone 74 Ischial length The shortest distance between the rim of the acetabular Wood (1975) 0.018(1.8%) fossa and the furthest point on the ischial tuberosity 75 Acetabulum height ■ Height anteriorly-posteriorly Henry (1975) 0.009 (0.9%) Figure 3.1 Measurements of the skull (frontal view).
136 Figure 3.2 Measurements of the skull (lateral view).
137 Figure 3.3 Measurements of the skull (base view).
138 36
40
39
Figure 3.4 Measurements of the mandible (front and lateral view).
139 45
\/
46
Figure 3.5 Measurements of the humerus.
140 7 K
Figure 3.6 Measurements of the ulna.
141 /\
\/
53
Figure 3.7 Measurements of the femur.
142 7 \
\/
Figure 3.8 Measurements of the tibia.
143 Figure 3.9 Measurements of the scapula.
Figure 3.10 Measurements of the clavicle.
144 \/
.69
70
Figure 3.11 Measurements of the sternum.
145 Figure 3.12 Measurements of the pelvis (posterior and lateral views).
146 Intraobserver error was tested using the method outlined in White (1991):
• Repeat measurement, a (e.g. orbit breadth), three times and find the mean (x)
3~
• Calculate the deviation of a,, aj, a^ from a: to give y a,-x = y, ^2-x = Y2 a3-jc = y3
• Find the sum of y,, y 2, y 2 and divide by the number of measurements taken (i.e. 3) to give z =Z 3 • Calculate the percentage that z differs from x to give the intraobserver error, z = c % Intraobserver error = c x 1 = 1E% 100
Error measurements for each of the craniodental and postcranial variables were calculated to estimate repeatability. The majority of variables gave error measurements of between 0.1 and 2.5%, and all were less than 5%, indicating that they are all highly repeatable (Table 3.3).
147 3.4 Methods of Analysis
3.4.1 Multivariate statistical methods
The morphological data were analysed using discriminant function analysis, implemented using SPSS fo r Windows (version 7.0). The craniodental data were also analysed using hierarchical cluster analysis, implemented using the same software.
These forms of multivariate statistical analysis have been employed by numerous authors, to assess morphological variability among a variety of primate groups (e.g.
Masters and Lubinsky, 1988; Froehlich et a i, 1991; Kobayashi, 1995; Ravosa, 1998;
Burity et al., 1999). The techniques were selected since they can be used to identify groups of morphological similarly in a given set of observed measurements (Sokal and
Rohlf, 1995).
Discriminant function analysis
The aim of discriminant function analysis (DFA) is to predict group membership from a set of independent variables having outlined a set of grouping variables
(Tabachnick and Fidell, 1996). The independent variables are selected (in this study the metrical measurements collected from gibbon postcrania, crania and teeth), as well as the grouping variables (the eleven gibbon species) and a DFA computed to give a set of discriminant functions. These are derived in such as way as to maximise the difference between groups relative to the variation within groups.
There are three types of DFA: direct, sequential (also known as hierarchical) and stepwise. In direct DFA all the variables enter the analysis at once; in sequential DFA they enter according to a schedule set by the analyser; and in stepwise DFA statistical criteria determine the order of entry. The stepwise method is the most generally
148 applicable (Kinnear and Gray, 1995) since in most cases, including this study, there is no reason to give some predictors higher priorities than others. The effect of including a particular independent variable (IV) in the DFA is assessed by a statistical test, the results of which are used as a basis for the inclusion of that IV in the final analysis. A variety of statistical tests are available for weighing up the inclusion, or not, of variables; Wilks’ lambda is the most commonly used. Wilks’ lambda is a multivariate test of significance, sometimes called the U statistic. Lambda ranges between 0 and 1,
with values close to 0 indicating the group means are different and values close to 1 indicating the group means are not different. When this procedure is complete a summary table is produced indicating which variables were added or removed at each step. The variables remaining in the analysis are those used to compute discriminant functions (Kinnear and Gray, 1995).
In a DFA those discriminant functions with eigenvalues above 1 are considered the most significant. The first discriminant function accounts for the highest percentage of variance, and has the highest eigenvalue. The higher functions account for successively lower percentages of the overall variance and have successively smaller eigenvalues. IV’s that load highly on particular functions indicate that those particular variables are important in discriminating groups. IV which are particularly important
(i.e. have loading greater than 0.5) or may be meaningful, are deduced from the standardised canonical discriminant function coefficients table and the structure matrix in the output listing.
The post-hoc predicted groups membership scores provide an indication of the success of predicting group membership based on the discriminant functions developed in the analysis. The higher the percentage for each group, the greater the success in predicting group membership (species groupings, in this case) based on the discriminant
149 functions. An overall percentage is given which provides an indication of the general success in predicting group membership.
150 Hierarchical cluster analysis
Hierarchical cluster analysis groups together those cases which are most similar, in a sequential fashion. In order to cluster cases it is necessary to have some numerical similarity measurement to characterise the relationships among the cases (Anderberg,
1973). This is achieved by computing a measure of association for every pairwise combination of the cases using an algorithm. The algorithm starts with each case in a separate cluster and combines clusters until only one is left. The basic assumption of all cluster analysis methods is that these numerical measures of association are comparable to each other. In hierarchical cluster analysis, such association measures are used to construct a similarity matrix. This matrix describes the strength of all pairwise relationships among the cases. The methods of hierarchical cluster analysis
(implemented using the software SPSS for Windows [version 7.0]) operate on this similarity matrix to construct a tree, or dendogram, depicting specified relationships among pre-selected groups (in this case the different species of gibbon).
Once the similarity matrix has been created there are a variety of tree building techniques available. The simplest method is single-linkage cluster analysis (Anderberg,
1973). In this method, clusters are joined at each stage by the single shortest or strongest link between them. The single linkage method has been adopted here since it makes the least number of assumptions about the data.
151 Size correction
The question of size and shape in comparative biology is an ongoing issue
(Mosimann, 1970; Corruccini, 1973; Darroch and Mosimann, 1985; Jungers et a i,
1995; Sokal and Rohlf, 1995; and references therein). It is widely accepted that, in order to find genuine shape differences among a suite of linear measurements representing interspecific variability among a set of taxa, the effects of overall size must be controlled (e.g. Jungers, et a i, 1995). Indeed, shape differences themselves may depend on size differences (Gould, 1966). The view taken in this study is that for phylogenetic purposes, both size and shape may play an important role in distinguishing taxa, however, in order to assess shape differences it is necessary to remove the confounding effects of body size. Hence, is was decided to create two datasets, one comprising raw data and the other with the effects of body size removed. In this way the results of multivariate analyses can be compared, taking into account both size and shape differences.
Numerous size adjustment techniques have been proposed and a useful review of the main methods can be found in Jungers et al. (1995). This paper reviews eleven size adjustment techniques. The authors applied the various size adjustment techniques to a craniometric dataset comprising several measurements from different species of guenons. This was designed to test for successful size adjustment with reference to interspecific craniometric analysis. The procedure was also applied to a dataset comprising native American anthropometries to test the success of size correction in relation to intraspecific analyses. Jungers et a l set the criterion that organisms of identical shape should have no measurable distance between them after differences in size are removed. Using this criterion they attempted to identify different sized individuals of the same shape, after the various size correction techniques had been
152 applied. One of the Mosimann family of shape ratios (Mosimann, 1970), was one of three identified by Jungers et al. (1995) as being successful based on the criterion set out above. This method has been adopted in this study. It involves dividing each variable by the geometric mean of all variables. One advantage of using the geometric mean is that the standard against which the individuals are scaled is not dependant on the composition of the sample. Furthermore, this technique allows for the effects of size-related shape, i.e. it corrects for geometric shape differences, hence allometric shape differences remain.
153 3.4.1 Results
Discriminant Function Analysis
Craniodental data
Size and shape differences among the eleven gibbon species were assessed by performing two DFA, one using the raw craniodental data (reflecting both size and shape), another using the size-corrected data (emphasising shape). Each analysis produced 10 discriminant functions. The first three functions had eigenvalues over one and accounted for over 85% of the variance in each of the analyses (raw and size- corrected) (Table 3.4a).
The DFA using raw craniodental data produced several clear morphological groupings (Figure 3.13). Function 1 reflects size, and separates the large {syndactylus), medium {hoolock, concolor, gabriellae, and leucogenys) and small (lar, agilis, moloch, muelleri, pileatus, and klossii) sized gibbons. These size separations are in agreement with previous findings (Geissmann, 1993). Function 1 accounts for over 58% of overall variance (Table 3.4a) and is described by orbit breadth (Table 3.4b). Function 2 reflects a shape difference and separates syndactylus from the subgenus Nomascus {concolor, gabriellae and leucogenys), and to a lesser extent klossii from lar, agilis, moloch, muelleri, and pileatus (Figure 3.13). Function 2 accounts for a further 21% of the
variance (Table 3.4a), and is described by the breadth of T, the width of PM 3 , and the coronoid height of the mandible (Table 3.4b). Function 3 separates hoolock from species in the subgenus Nomascus {concolor, gabriellae, and leucogenys) (Figure 3.14).
It is described by the height of the nose from nasion to nasospinale, lower facial prognathism, palate length, height of the skull between bregma and basion, and the
width of M 2 (Table 3.4b).
154 The DFA using size-corrected craniodental data produced similar grouping patterns as the analysis using raw data. Function 1 again separated syndactylus, the larger sized gibbon, hoolock, concolor, gabriellae, and leucogenys, the medium sized gibbons, and lar, agilis, moloch, muelleri, pileatus, and klossii, the smallest gibbons
(Figure 3,15). This result suggests that since size has been removed, these separations are based on shape differences of orbit breadth and lower facial prognathism, the variables which have high standardised canonical function coefficients scores (Table
3.4c). Function 2 again separates syndactylus from the subgenus Nomascus {concolor, gabriellae, and leucogenys), and to a lesser extent klossii from lar, agilis, moloch, muelleri, and pileatus (Figure 3.15). This function is described by the relative breadth of I^, the relative height of the skull between bregma and basion, the relative width of
PM], and the relative coronoid height of the mandible (Table 3.4c). Function 3 for the size-corrected DFA, is described by the relative height of the nose from nasion to nasospinale (Table 3.4c), and separates hoolock from species in the subgenus Nomascus
{concolor, gabriellae, and leucogenys) (Figure 3.16). Again this is in agreement with the analysis using raw data.
155 Table 3.4a Results of discriminant function analyses based on craniodental data
Function Eigenvalue % of Variance Cumulative % Canonical correlation Analysis based on raw data
1 10.18 58.18 58.18 0.95
2 3.77 21.52 79.69 0.89 3 1.59 9.06 88.75 0.78 Analysis based on size-corrected data
1 &32 54.49 54.49 0.94
2 3.41 2Z32 76.81 0 . 8 8 3 1.40 9.18 85.99 0.76
Table 3.4b Standardised canonical discriminant function coefficients for raw craniodental data
Function 1 Function 2 Function 3 Orbit breadth -.54 .49 .09 f breadth .03 .73 . 2 1
PM 3 width .26 - . 6 6 .32
Coronoid height of mandible - . 0 0 .65 -.16
Nasion to nasospinale .37 . 1 2 . 6 8 Lower facial prognathism .42 .17 -.91 Palate length .37 -.07 .54
Bregma to basion . 2 2 -.32 .63 M, width .30 -.32 -.54
Table 3.4c Standardised canonical discriminant function coefficients for size-corrected craniodental data
Function 1 Function 2 Function 3
Orbit breadth -.58 .35 - . 2 2 Lower facial prognathism .62 .34 -.40 f breadth .04 .65 .29
PM 3 width .28 - . 6 6 .13 Bregma to basion .04 -.50 .45
Coronoid height of mandible . 1 2 .65 . 0 1
Nasion to nasospinale .37 . 1 2 . 6 8
156 Species
+ syndactylus[1]
Q ^ leucogenys[2]
• gabriellae [2]
CP ^ concolor [2]
hoolock [3]
■ Wosai [4]
^ pileatus[4]
^ muelleri [4]
^ moloch [4]
O agilis[4]
° lar [4]
Function 1
Figure 3.13 Scatter plot of discriminant function 1 against discriminant function 2 for the analysis of raw craniodental data. [1] subgenus Symphalangus, [2] subgenus Nomascus, [3] subgenus Bunopithecus, [4] subgenus Hylobates
157 Species
+ syndactylus [1]
^ leucogenys[2]
• gabriellae [2]
^ concolor [2] o ° o hoolock [3]
■ Wosai [4]
^ pileatus[4]
^ muelleri [4]
^ moloch [4]
O agilis[4]
° lar [4]
Function 1
Figure 3.14 Scatter plot of discriminant function 1 against discriminant function 3 for the analysis of raw craniodental data. [1] subgenus Symphalangus, [2] subgenus Nomascus, [3] subgenus Bunopithecus, [4] subgenus Hylobates
158 Species
+ syndactylus[1]
^ leucogenys[2]
6 • gabriellae [2]
^ concolor [2]
hoolock [3]
■ kosai [4]
^ pileatus[4]
^ muelleri [4]
^ moloch [4]
O agilis[4]
° lar [4]
Function 1
Figure 3.15 Scatter plot of discriminant function 1 against discriminant function 2 for the analysis of size-corrected craniodental data. [1] subgenus Symphalangus, [2] subgenus Nomascus, [3] subgenus Bunopithecus, [4] subgenus Hylobates
159 Species
+ syndactylus[1] * ▼ ,
Function 1
Figure 3.16 Scatter plot of discriminant function 1 against discriminant function 3 for the analysis of size-corrected craniodental data. [1] subgenus Symphalangus, [2] subgenus Nomascus, [3] subgenus Bunopithecus, [4] subgenus Hylobates
160 The analyses using raw and size-corrected craniodental data resulted in five distinct morphological groupings:
(1) syndactylus (subgenus Symphalangus) - distinguished by function 1 in the raw and size-corrected DFA (Figures 3.13 and 3.15).
(2) lar, agilis, moloch, muelleri, and pileatus (subgenus Hylobates) (often referred to as
the lar-group/complex) - distinguished by functions 1 and 2 in the raw and size- corrected DFA (Figures 3.13 and 3.15).
(3) klossii (subgenus Hylobates) - distinguished by function 2 in the raw and size- corrected DFA (Figures 3.13 and 3.15).
(4) concolor, gabriellae, and leucogenys (subgenus Nomascus) - distinguished by function 3 in the raw and size-corrected DFA (Figures 3.14 and 3.16).
(5) hoolock (subgenus Bunopithecus) - distinguished by function 3 in the raw and size- corrected DFA (Figures 3.14 and 3.16).
These results agree with the findings of Creel and Preuschoft (1976,1984) who have also used gibbon craniodental data in a DFA. Both this study and that of Creel and
Preuschoft, identified the same five morphological groupings (see 1 - 5 above).
Furthermore, in each analysis the same discriminant functions separated the same groups. For example, in both this analysis and that of Creel and Preuschoft,
discriminant functions 1 and 2 produced the same groupings: ( 1 ) syndactylus, (2 ) lar.
161 agilis, moloch, muelleri, and pileatus (the lar-group), (3) klossii, and (4) concolor, gabriellae, and leucogenys with (5) hoolock (Figure 3.13). It is not possible, however, to make direct comparisons between morphological features since Creel and Preuschoft used 3-D co-ordinate data as opposed to the linear measurements employed in this study
(see section 1.4.1 for details of Creel and Preuschoft’s data collection technique).
On the basis of these results it is possible to identify certain morphological features of the skull and dentition which characterise the five different groupings. These are as follows:
(1) syndactylus (subgenus Symphalangus) is characterised by: - large absolute skull size - narrower orbits relative to the other gibbons - large incisor size (I^) relative to the other gibbons
- small premolar size (PM3) relative to the other gibbons - higher mandibles relative to the other gibbons - a higher face, particularly in the region below the orbits (nasion to nasospinale) relative to hoolock - a less prognathic lower face relative to hoolock - a longer palate, relative to hoolock - a higher skull (bregma to basion) relative to hoolock - narrower Mj relative to hoolock
162 (2) lar, agilis, moloch, muelleri, and pileatus (subgenus Hylobates) are characterised by: - small absolute skull size - broader orbits relative to the other gibbons - large incisor size (I^) relative to klossii
- small premolar size (PM 3 ) relative to klossii - higher mandibles relative to klossii - a higher face, particularly in the region below the orbits, relative to hoolock - a less prognathic lower face relative to hoolock - a longer palate, relative to hoolock - a higher skull (bregma to basion) relative to hoolock - narrower Mj relative to hoolock
(3) klossii (subgenus Hylobates) is characterised by: - small absolute skull size - broader orbits relative to the other gibbons - small incisor size (I^) relative to lar, agilis, moloch, muelleri, and pileatus
- large premolar size (PM 3) relative to lar, agilis, moloch, muelleri, and pileatus - lower mandible relative to lar, agilis, moloch, muelleri, and pileatus - a higher face, particularly in the region below the orbits, relative to hoolock - a less prognathic lower face relative to hoolock - a longer palate, relative to hoolock - a higher skull (bregma to basion) relative to hoolock
- narrower M 2 relative to hoolock
163 (4) concolor, gabriellae, and leucogenys (subgenus Nomascus) are characterised by: - medium absolute skull size
- orbit breadth as an intermediate between the large gibbon ( 1 ) syndactylus and the small gibbons ( 2 ) lar, agilis, moloch, muelleri, pileatus and (3) klossii - small incisor size (I^) relative to lar, agilis, moloch, muelleri, and pileatus, and syndactylus - large premolar size (PM3) relative to lar, agilis, moloch, muelleri, and pileatus, and syndactylus - lower mandible relative to lar, agilis, moloch, muelleri, and pileatus, and syndactylus - a higher face, particularly in the region below the orbits, relative to hoolock - a longer palate, relative to hoolock - a less prognathic face, relative to hoolock - a higher skull (bregma to basion) relative to hoolock - narrower Mj relative to hoolock
(5) hoolock (subgenus Bunopithecus) is characterised by: - medium absolute skull size
- orbit breadth as an intermediate between the large gibbon ( 1 ) syndactylus and the small gibbons (2) lar, agilis, moloch, muelleri, pileatus and (3) klossii - small incisor size (P) relative to lar, agilis, moloch, muelleri, and pileatus, and syndactylus
- large premolar size (PM3) relative to lar, agilis, moloch, muelleri, and pileatus, and syndactylus - lower mandible relative to lar, agilis, moloch, muelleri, and pileatus, and syndactylus - a lower face, particularly in the region below the orbits, relative to the other gibbons - a shorter palate, relative to the other gibbons - a more prognathic face relative to the other gibbons - a lower skull (bregma to basion) relative to the other gibbons - wider Mj relative to the other gibbons
164 The post-hoc predicted groups membership summary (Table 3.5) provides an indication of the success of predicting group membership based on the discriminant functions developed in the analysis. Using the raw craniodental data 79.6% of cases,
overall, were correctly classified; 1 0 0 % of cases were correctly allocated to subgenus
Symphalangus (syndactylus)’, an average of 85% of cases were correctly allocated to the subgenus Nomascus (concolor, gabriellae, and leucogenys)', 100% of cases were correctly allocated to H. hoolock', and an average of 87.9% of cases were correctly allocated to subgenus Hylobates (lar, agilis, moloch, muelleri, pileatus, and klossii).
The results of the predicted group membership classification also showed that even for those subgenera which had lower success rates for case allocation (e.g.
Hylobates), individual species within the groups were always incorrectly classified as another member of their group. For example, while only 72.7% of cases for pileatus were correctly classified, 27.3% were incorrectly classified moloch. The predicted group membership scores for the size-corrected data were not quite as high, 77.3% of cases overall being correctly classified, however, the patterns of classification were the same as for the raw group membership test.
165 Table 3.5 Summary of post-hoc predicted group membership scores based on the results of the DFA using raw craniodental data
Species Cases lar"* agilis"* moloch"* muelleri* pileatus* klossii* hoolocld concolor^ gabriellae^ leucogenys^ syndactylus*
lar* 29 79.3% 6.9% 3.4% 10.3% 0 % 0 % 0 % 0 % 0 % 0 % 0 %
agilis"* 19 10..5% 63.2% 0 % 15.8% 10.5% 0 % 0 % 0 % 0 % 0 % 0 %
moloch!* 8 0 % 0 % 62.5% 25.0% 0 % 12.5% 0 % 0 % 0 % 0 % 0 %
muelleri* 23 4.3% 8.7% 0 % 78.3% 4.3% 4.3% 0 % 0 % 0 % 0 % 0 %
pileatus"* 1 1 0 % 0 % 27.3% 0 % 72.7% 0 % 0 % 0 % 0 % 0 % 0 %
klossii* 1 2 0 % 0 % 0 % 0 % 16.7% 83.3% 0 % 0 % 0 % 0 % 0 % o\ hoolocJ^ 1 0 0 % 0 % 0 % 0 % 0 % 0 % 1 0 0 % 0 % 0 % 0 % 0 % ON
concolor^ 2 0 % 0 % 0 % 0 % 0 % 0 % 0 % 1 0 0 % 0 % 0 % 0 %
gabriellae^ 1 1 0 % 0 % 0 % 12.5% 0 % 0 % 0 % 0 % 75.0% 12.5% 0 %
leucogenys^ 1 0 0 % 0 % 1 0 .0 % 0 % 0 % 0 % 0 % 0 % 1 0 .0 % 80.0% 0 %
syndactylus* 15 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 1 0 0 %
Percent of “grouped” cases correctly classified: 79.6% subgenus Symphalangus^ ^ subgenus Nomascus; ^ subgenus Bunopithecus; 4, subgenus Hylobates Hierarchical Cluster Analysis
Hierarchical cluster analysis (HCA) was used on two datasets: [1] means of the
first three discriminant function scores, and [ 2 ] using all of the original data, both raw
and size corrected. Hierarchical cluster analysis allows dendograms to be created which
show the phenetic closeness of the taxa involved. Figure 3.17a shows a dendogram
created from a hierarchical cluster analysis using means of the first three discriminant
function from the raw craniodental DFA. The dendogram was constructed using the
single-linkage method.
The hierarchical cluster analysis has grouped the different species according to their taxonomic subgeneric groupings (Geissmann, 1995); for example, all of the members of subgenus Hylobates, have been grouped more closely to each other than to any of the other hylobatids. The same applies to species in the subgenus Nomascus.
Regarding interrelationships among the four subgenera, the dendogram groups the subgenera Hylobates, Nomascus and Bunopithecus more closely to each other than any of them are to Symphalangus. Since this analysis involves raw data, it is possible that the observed groupings reflect the absolute size differences between the gibbons and the siamang. Hence the same analysis was performed using the size-corrected data.
The hierarchical cluster analysis using means of the first three functions from the DFA using size-corrected data produced the same phenetic patterns as the analysis based on raw craniodental data (Figure 3.17b). The exception is the grouping of klossii within the subgenus Nomascus. This provides further evidence to suggest that the morphological groupings which separate the subgenera of gibbons (and klossii) are based on size and shape differences.
Figures 3.17c and d show HCA using all of the original raw and size corrected craniodental data, respectively. This time the average linkage method was employed.
167 although single linkage provided similar results. The average linkage method clusters groups according to their means. In each analysis the dendograms show clusters which are not grouped entirely according to taxonomic groupings. For example, in the raw data HCA while all the members of subgenus Hylobates are grouped together, the group also includes gabriellae. However, broadly speaking both HCA’s using the original raw and size-corrected data provide dendograms which are similar to the taxonomic groupings with the exception of certain taxa, including: gabriellae and klossii.
The usefulness of employing size-corrected data in hierarchical cluster analysis, however, has been questioned by Masters and Lubinsky (1988). These authors found that when the effects of size were removed from their skeletal dataset of variability among galagos, the clustering phenomenon was reduced. This could explain the spurious grouping of klossii within the subgenus Nomascus, in this study. Thus, the results of the hierarchical cluster analysis using raw and size-corrected craniodental data suggest that, while klossii is more similar in size to the other members of the subgenus
Hylobates, there are some interesting shape differences which separate it from these taxa. These differences are also reflected in the results of the DFA.
168 — moloch (Hylobates)
— pileatus (Hylobates)
- ûgi/ïs (Hylobates)
- muelleri (Hylobates)
— lar (Hylobates)
_ klos^i (Hylobates)
gabriellae (Nomascus)
leucogenys (Nomascus)
- concolor (Nomascus)
- hoolock (Bunopithecus)
- syndactylus (Symphalangus)
Figure 3.17a Single-linkage hierarchical cluster analysis based on mean of the first three functions of the discriminant function analysis using the raw craniodental data. (Subgenera are indicated in brackets).
— lar (Hylobates)
— agilis (Hylobates)
- moloch (Hylobates) - muelleri (Hylobates)
pileatus (Hylobates)
- gabriellae (Nomascus)
leucogenys (Nomascus)
- concolor (Nomascus)
- klossii (Hylobates)
■ hoolock (Bunopithecus)
- syndactylus (^mphalangus)
Figure 3.17b Single-linkage hierarchical cluster analysis based on mean of the first three functions of the discriminant function analysis using the size-corrected craniodental data. (Subgenera are indicated in brackets).
169 moloch (Hylobates)
lar (Hylobates)
gabriellae (Nomascus)
klossii (Hylobates)
concolor (Nomascus)
leucogenys (Nomascus)
hoolock (Bunopithecus)
syndactylus (^mphalangus)
Figure 3.17c Average-linkage hierarchical cluster analysis based on all of the raw craniodental data. (Subgenera are indicated in brackets).
— agilis (Hylobates)
— moloch (Hylobates)
- muelleri (Hylobates)
- pileatus (Hylobates)
- lar (Hylobates)
- gabriellae (Nomascus)
— concolor (Nomascus)
■ leucogenys (Nomascus)
- klossii (Hylobates)
- hoolock (Bunopithecus)
— syndactylus (Symphalangus)
Figure 3.17d Average-linkage hierarchical cluster analysis based on all of the size- corrected craniodental data. (Subgenera are indicated in brackets).
170 Discriminant Function Analysis
Postcranial data
The DFA using the raw and size-corrected postcranial datasets were hampered by a large amount of missing data. In an effort to reduce these effects, those individuals and variables which had missing data for more than ten variables were removed. This procedure reduced each dataset down from 115 individuals to 91, and from 34 postcranial variables to 29 (this involved removing all the sternal measurements, since many skeletons were lacking part or all of the sternal bones).
Complete dataset Reduced dataset
115 individuals / 34 variables 91 individuals / 29
variables
No. of cases included in DFA 28 64
(raw and size-corrected)
When all 115 individuals and 34 variables from the raw and size-corrected datasets were included in the analysis only 28 individuals out of 115 were used in the analysis; 87 individuals having been dropped due to missing data. When the reduced dataset (those individuals and variables which had a large amount of missing data were removed) was analysed, however, 64 out of 91 individuals were included in the analyses.
171 The breakdown of individuals included in the analyses, per species, is as follows:
Species No. of individuals included in analyses syndactylus 13 leucogenys 1 gabriellae 1 0 concolor 2 hoolock 2 klossii 5 pileatus 2 muelleri 7 moloch 3 agilis 7 lar 1 2 Total 64
Although it is recognised this is a fairly small number of individuals to work with considering the number of species, the results of the DFA provided some interesting patterns which warrant some discussion.
Each DFA (using raw and size-corrected reduced datasets) produced 5 discriminant functions, the first three of which had eigenvalues above 1. These accounted for 92.2% (raw) and 91.6% (size-corrected) of the total variance (Table
3.6a).
The DFA using raw postcranial data produced several morphological groupings, although these were not as distinct as the DFA using craniodental data. Function 1 reflects size and separates syndactylus from the rest of the gibbons (Figure 3.18). This is in agreement with the results of the DFA using craniodental data, and with previous findings (Geissmann, 1993). Function 1 accounts for 60% of the overall variance (Table
3.6a) and is described by femoral head diameter, the maximum midshaft breadth of the tibia, and by humerus length (Table 3.6b). Function 2 reflects a shape difference and
172 separates klossii, concolor, gabriellae, and leucogenys from lar, agilis, moloch, muelleri, and pileatus and syndactylus (Figure 3.18). Function 2 accounts for a further
19% of variance (Table 3.6a) and is described by tibia length and scapular breadth
(Table 3.6b). Function 3 does not produce any clear separations (Figure 3.19). Variables which scored highly on function 3 also scored highly on functions 1 and 2, and hence these loadings must be treated with caution (Table 3.6b).
The DFA using size-corrected postcranial data produced less distinct
morphological groupings, although functions 1 and 2 distinguished three groups (Figure
3.20). Function 1 separated concolor, gabriellae, and leucogenys, from hoolock and subgenus Hylobates (Figure 3.20). Function 1 accounts for 59% of the overall variance (Table 3.6a) and is described by tibia length, the maximum midshaft breadth of the tibia, ulna length and ischial length (Table 3.6c). Function 2 separates syndactylus from concolor, gabriellae, and leucogenys (Figure 3.20), and is described by scapular breadth (Table 3.6c). Again function 3 did not discriminate well between groups (Figure 3.21). The variables which scored highly on function 3 also scored highly on function 1 and 2 (Table 3.6c), so the results must be treated cautiously.
173 Table 3.6a Results of discriminant function analyses based on postcranial data
Function Eigenvalue % of Variance Cumulative % Canonical correlation Analysis based on raw data
1 7.77 60.09 60.09 0.94
2 2.45 18.99 79.08 0.84 3 1.70 13.16 92.24 0.79 Analysis based on size-corrected data
1 6.33 59.15 59.15 0.93
2 2.41 22.53 81.68 0.84 3 1.06 9.92 91.60 0.72
Table 3.6b Standardised canonical discriminant function coefficients for raw postcranial data.
Function 1 Function 2 Function 3 Femoral head diameter 1.22 -.35 -.52
Tibia max. midshaft -1.26 -.34 . 1 1 breadth
Tibia length - . 2 0 .77 .69 Scapular breadth -.06 1.10 -1.19 Humerus length .81 -.57 1.04
Table 3.6c Standardised canonical discriminant function coefficients for size-corrected postcranial data.
Function 1 Function 2 Function 3
Tibia length 1.02 . 2 2 . 2 2
Tibia max. midshaft .70 . 2 0 -.04 breadth Ischial length .56 .50 -.16 Scapular breadth .27 -1.12 .15 Ulna length -.72 .43 .80
174 6
Species
4 + syndactylus[1]
leucogenys [2]
gabriellae [2] 2 concolor [2]
hoolock [3]
0 kosai [4] + + pileatus [4]
muelleri [4]
CvJ ■2 moloch [4] C o ü agilis [4] c 3 -4 lar [4] -8 -6 4 ■2 0 2 4 6 8 10
Function 1
Figure 3.18 Scatter plot of discriminant function 1 against discriminant function 2 for the analysis of raw postcranial data. [1] subgcnus Symphalangus, [2] subgcnus Nomascus, [3] subgcnus Bunopithecus, [4] subgcnus Hylobates
175 Species
+ syndactylus [1]
leucogenys [2]
gabriellae [2]
concolor [2] AO V hoolock [3]
++ Wossii [4] - 2 "
pileatus [4]
muelleri [4]
CO c moloch [4] o o agilis [4] c lar[4] -8 -6 -4 2 0 2 4 6 8 10
Function 1
Figure 3.19 Scatter plot of discriminant function 1 against discriminant function 3 for the analysis of raw postcranial data. [1] subgenus Symphalangus, [2] subgenus Nomascus, [3] subgenus Bunopithecus, [4] subgenus Hylobates
176 leuœ genys [2]
gabriellae [2]
œncolor [2]
hoolock [3]
kossii [4]
++ pileatus[4] □ O muelleri [4]
moloch [4]
o agilis[4] c 3 LL lar[4] -6 -4 ■2 0 2 4 6
Function 1
Figure 3.20 Scatter plot of discriminant function 1 against discriminant function 2 for the analysis of size-corrected postcranial data. [1] subgenus Symphalangus, [2] subgenus [3] subgenus Bimopithecus, [4] subgenus Hylobates
177 Species
+ syndactylus[1]
leuœ genys [2]
gabriellae [2]
œ n œ lor [2] □O hoolock [3]
kl osa I [4]
pileatus[4]
muelleri [4]
CO moloch [4] o -2" o agi lis [4] c 3 LL lar[4] -6 -4 •2 0 2 4 6
Function 1
Figure 3.21 Scatter plot of discriminant function 1 against discriminant function 3 for the analysis of size-corrected postcranial data. [1] subgenus Symphalangus, [2] subgenus Nomascus, [3] subgenus Bimopithecus, [4] subgenus Hylobates
178 The analyses using raw and size-corrected postcranial data resulted in four distinct morphological groupings:
(1) syndaciylus (subgenus Symphalangus) - distinguished by function 1 in the raw and size-corrected DFA (Figures 3.18 and 3.20).
(2) lar, agilis, moloch, muelleri, and pileatus (subgenus Hylobates) (often referred to as the lar-group/complex) - distinguished by function 2 in the raw and size-corrected DFA
(Figures 3.18 and 3.20).
(3) klossii (subgenus Hylobates) - distinguished by function 2 in the raw DFA (Figure
3.18).
(4) concolor, gabriellae, and leucogenys (subgenus Nomascus) - distinguished by function 1 in the raw and size-corrected DFA (Figures 3.18 and 3.20).
The results of DFA using raw and size-corrected postcranial data broadly agree with the results of the DFA using raw and size-corrected craniodental data, in that the postcranial data were able to distinguish among the same groups of gibbons. The morphological groupings, however, were less distinct using the postcranial data, and there was more evidence of overlap among the four groupings. However, on the basis of these results it is possible to identify certain features of the postcranium which characterise the four different groupings.
179 These are as follows:
(1) syndactylus (subgenus Symphalangus) is characterised by: - larger absolute skeleton size - a long humerus - large femoral head - narrower tibial breadth relative to the other gibbons - shorter lower limb bones relative to the other gibbons
(2) lary agilis, nioloch, muelleri, and pileatus (subgenus Hylobates) are characterised by: - smaller absolute skeleton size - a short humerus - small femoral head - broader tibial breadth relative to syndactylus - broader scapula compared to klossii - longer tibiae relative to klossii - longer lower limb bones relative to syndactylus
(3) klossii (subgenus Hylobates) is characterised by: - smaller absolute skeleton size - a short humerus - small femoral head - broader tibial breadth relative to syndactylus - a narrower scapula compared to the lar-group gibbons {lar, agilis, moloch, muelleri, and pileatus), and syndactylus - longer lower limb bones relative to syndactylus
180 (4) concolor, gabriellae, and leucogenys (subgenus Nomascus) are characterised by: - medium absolute skeleton size, intermediate between syndactylus and subgenus Hylobates - a humerus intermediate in length between syndactylus and the subgenus Hylobates - a femoral head intermediate in breadth between syndactylus and the subgenus Hylobates - a tibia intermediate in breadth between syndactylus and the subgenus Hylobates - narrower scapulae compared to the lar-group gibbons {lar, agilis, moloch, muelleri, and pileatus), and syndactylus
These aspects of size and shape which serve to distinguish the postcrania of certain gibbons are in agreement with previous findings (Schultz, 1933; lungers and
Cole, 1992). For example, Schultz (1933) and Jungers and Cole (1992) determined that the gibbons (excluding H. syndactylus) had relatively longer elements of the lower limb when compared to the siamang, and that klossii had relatively the longest legs of all the gibbons. This finding has been corroborated here (Figure 3.22). On comparing tibia length with femur length using size-corrected data, syndactylus occupies the lower end of the range, while klossii is distributed towards the top end of the range. Furthermore, the data presented in this study support previous findings regarding variation in pelvic proportions among gibbons. Schultz (1933) and Jungers and Cole (1992) present data indicating that syndactylus is characterised by relatively longer ilia and pubes. A comparison of iliac height (this measurement is equivalent to Schultz’ iliac length) against pubic length using size-corrected data shows that, although there is some overlap, the siamang occupies the top end of the range when compared to the other gibbons (Figure 3.23).
181 10 Species
syndactylus (1 ) 9 leucogenys ( 2 )
8 concolor ( 2 )
hoolock (3)
klossii (4) 7 pileatus (4)
muelleri (4) 6 CJ) moloch (4) c JJ a agilis (4)
5 lar (4) 6 7 8 9 10 11 12
Femur length
Figure 3.22 Bivariate plot of femur length against tibia length based on size-corrected postcranial data. (1) subgenus Symphalangus, (2) subgenus Nomascus, (3) subgenus Bimopithecus, (4) subgenus Hylobates
182 4.5 Species
syndactylus (1 ) 4.0" leucogenys (2 )
3.5 concolor ( 2 ) >□ hoolock (3)
klossii (4) 3.0" O pileatus (4)
muelleri (4) 2.5" moloch (4)
agilis (4)
lar (4) 8 9 1.0 1.1 1.2 1.3 1.4 1.5 1.6
Pubic length
Figure 3.23 Bivariate plot of pubic length against iliac height based on size-corrected postcranial data. (1) subgenus Symphalangus, (2) subgenus Nomascus, (3) subgenus Bimopithecus, (4) subgenus Hylobates
183 The post-hoc predicted group membership scores based on the results of the
DFA using raw and size-corrected postcranial datasets were similar in grouping success as the prediction scores based on craniodental data (Table 3.7). The overall percent of cases correctly classified using the results of the DFA based on raw postcranial data was high at 78.5%. The result was slightly lower for the size-corrected dataset, at
74.2%. The predicted group membership scores, however, for species in the subgenus
Hylobates using the raw and size-corrected DFA results showed more evidence of overlap with species in the other three subgenera. For example, only 50% of cases were correctly allocated to muelleri, 25% of cases being incorrectly classified as hoolock and gabriellae.
184 Table 3.7 Summary of post-hoc predicted group membership scores based on the results of the DFA using raw postcranial data
Species Cases lar* agilis* moloch* muelleri* pileatus* klossii* hoolocJc' concolor^ gabriellae^leucogenys^ syndactylus^
lar" 29 75.0% 8.3% 0 % 8.3% 0 % 0 % 8.3% 0 % 0 % 0 % 0 %
agilis'^ 19 0 % 85.7% 0 % 14.3% 0 % 0 % 0 % 0 % 0 % 0 % 0 %
moloch!^ 8 33.3% 33.3% 0 % 0 % 0 % 33.3% 0 % 0 % 0 % 0 % 0 %
muelleri* 23 12.5% 0 % 12.5% 50% 0 % 0 % 12.5% 0 % 12.5% 0 % 0 % pileatus* 1 1 0 % 0 % 0 % 0 % 1 0 0 % 0 % 0 % 0 % 0 % 0 % 0 %
klossii* 1 2 0 % 0 % 0 % 0 % 0 % 1 0 0 % 0 % 0 % 0 % 0 % 0 %
hoolock^ 1 0 0 % 0 % 0 % 50% 0 % 0 % 50% 0 % 0 % 0 % 0 %
concolor^ 2 0 % 0 % 0 % 0 % 0 % 0 % 0 % 1 0 0 % 0 % 0 % 0 %
gabriellae^ 1 1 0 % 0 % 2 0 % 0 % 0 % 9.1% 0 % 0 % 80% 9.1% 0 %
leucogenys^ 1 0 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 1 0 0 % 0 %
syndactylus^ 15 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 0 % 1 0 0 %
Percent of “grouped” cases correctly classified: 78.5% ’ subgenus Symphalangus', ^ subgenus Nomascus', ^ subgenus Bunopithecus', ^subgenus Hylobates One factor which warrants some brief discussion is the ratio of the number of variables compared with the number of individuals, with respect to both the craniodental and postcranial datasets. These analyses are based on datasets which comprise a large number of variables in relation to a relatively small number of individuals. Furthermore, these individuals are subdivided into a large number of groups (11 different species) each comprising a small number of individuals. For example, included in the postcranial analysis are 29 variables, but some species groups
(e.g. concolor, hoolock and pileatus) only include one or two individuals. In order to assess if this ratio would skew the results of the DFA, the robust groupings identified by the DFA were further substantiated by performing a DFA based on the factors produced from a principal components analysis. This analysis was executed in order to reduce the ratio of the number of variables against the number of individuals used. Since this ratio is low (there being a large number of variables compared to a relatively small number of individuals) a principal components analyses was used to reduce the number of variables against sample size. The resultant factors were then included in a DFA. This analysis produce the same five morphological groupings as the DFA using raw and size-corrected data. This indicates that despite the ratio of variables: individuals being low, the data are successful in discriminating among groups of gibbons.
186 Discussion
The results of the multivariate statistical analyses show evidence of five distinct morphological groupings among hylobatids:
( 1 ) syndactylus
(2 ) lar, agilis, moloch, muelleri, and pileatus
(3) klossii
(4) concolor, gabriellae, and leucogenys
(5) hoolock
These analyses have differentiated the various species of gibbon in accordance with the widely accepted subgeneric groupings, namely; [1] Symphalangus’,
[2] Nomascus', [3] Bunopithecus’, [4] Hylobates. The exception to this is the grouping of klossii.
This study has included newly described species not incorporated in previous analyses (H. gabriellae, H. leucogenys). These taxa consistently grouped with the other member {H. concolor) of their subgenus, Nomascus. This highlights the close . morphological affinities that these three taxa share.
Furthermore, the five morphological groups of hylobatid can be distinguished by a suite of craniodental and postcranial features. These analyses confirm the results of previous works (e.g. Schultz, 1933; Creel and Preuschoft, 1976, 1984; Jungers and
Cole, 1992) and have added to these studies in identifying a number of newly recognised characteristics, distinguished by discriminant function analysis (DFA).
The robust groupings identified by the DFA were further substantiated by performing a DFA based on the factors produced from a principal components analysis.
187 This analysis produce the same five morphological groupings as the DFA using raw and size-corrected data.
The question remains, how can these morphological groupings be explained?
They are either the result of adaptive variability, or they reflect phylogenetic differences. Adaptively gibbons are relatively similar in that they have similar dietary and locomotory requirements, and occupy similar habitats. The exception to this might be the siamang, syndactylus, which has a more folivorous diet (Chivers, 1974;
MacKinnon, 1977). Also, its larger overall body size might have implications for the locomotory requirements of its skeleton. Interpretation of such adaptive differences is not within the scope of this study, and such analyses would warrant further research involving a variety of data not explored here, such as; field observation, biomechanical and dietary.
The results of the multivariate statistical study have, however, highlighted several interesting issues relating to gibbon taxonomy. The multivariate shape differences identified above are congruent with the taxonomic divisions established using other characteristics including pelage, vocalisations, size, etc. The exception to this is klossii. Several aspects of shape serve to distinguish klossii from the other members of subgenus Hylobates including: relative incisor and premolar size, coronoid height of the mandible and scapular breadth. This result is in agreement with the findings of other authors, who have also noted the morphological distinction of klossii from the other members of subgenus Hylobates (Creel and Preuschoft, 1976, 1984;
Groves, 1972). This study has added to the previous works in identifying which aspects of klossiVs skeleton distinguish it from the rest of subgenus Hylobates. Thus, while the similar size of klossii and lar, pileatus, agilis, moloch and muelleri hold it taxonomically within subgenus Hylobates, several shape differences serve to exclude it.
188 Most authors agree, however, that these features are not different enough to warrant the exclusion of klossii from the subgenus Hylobates, in light of other characteristics such as diploid number, vocalisations and size which serve to include it (e.g. Groves, 1972;
Marshall and Sugardjito, 1986; Geissmann, 1995). The recent molecular evidence for the similarities between klossii and lar, pileatus, agilis, moloch and muelleri on the basis of cytochrome b gene, provide further evidence for its correct taxonomic inclusion in subgenus Hylobates (Garza and Woodruff, 1992).
The morphological uniqueness of klossii compared to the other members of subgenus Hylobates, may be the result of a longer separation time. Cytochrome b gene data analysed here, however, does not indicate a more ancient divergence for klossii compared to the rest of subgenus Hylobates. Other molecular studies have also not found klossii to be the first member of subgenus Hylobates to diverge (Garza and
Woodruff, 1992; Hayashi et al., 1995). Several previous studies, however, based on a variety of morphological data have indicated that klossii was the first member of subgenus Hylobates to diverge (Groves, 1972; Creel and Preuschoft, 1984; Haimoff et al., 1982). Further work involving molecular data is warranted to establish the phylogenetic relationships between klossii and the other members of subgenus.
Hylobates, especially in light of the fact that cytochrome b gene data has already been shown to be uninformative at the species level, only resolving relationships among gibbons at the subgeneric level.
Alternatively, discrete adaptive differences may explain the morphological separation of klossii from the rest of subgenus Hylobates. Again, data derived from biomechanical, dietary and field studies is required to investigate this hypothesis fully.
The results of the Discriminant function analysis showed a large amount of overlap between species within the four subgenera. This indicates that within subgenera
189 gibbons are relatively morphologically homogeneous. This homogeneity has also been highlighted by Creel and Preuschoft (1984). This is interesting when compared to the molecular phylogeny of gibbons established in the previous chapter. Cytochrome b gene was also largely unable to resolve relationships within subgenera, indicating that at the species level cytochrome b gene in not highly variable among gibbons. This highlights the phenomenon that, at the species level, gibbons are unusual in being relatively homogeneous in some respects (such as gross morphology and certain mitochondrial genes), while showing extreme diversity in other respects (e.g. vocalisations and pelage). Other authors have also drawn attention to this phenomenon (Brockelman and
Gittins, 1984; Marshall and Sugardjito, 1986).
It is proposed that the homogeneity of certain gibbon characteristics in contrast to the extreme diversity of other features indicates that the differences highlighted in this study and in previous works, arose at the earliest stage of divergence, after the accumulation of significant genetic change, and that there has been minimal change since that time (except perhaps in auto-recognition factors, such as pelage and vocalisations).
Thus, subgeneric differences arose at or near the time of initial separation, and subsequently there has been minimal change at the species level. This is substantiated by the results of the molecular study presented in the previous chapter. While cytochrome b gene was able to resolve relationships among gibbon subgenera (i.e. the deeper, older nodes of the phylogeny) it was unable to resolve relationships towards the more recent, distal nodes of the tree. It must be noted, however, that this is only one gene of the gibbon mitochondrial genome and other genes which have evolved more rapidly may be able to resolve relationships among gibbon species.
The implications of the above for further work are, that in order to investigate
190 species level differences among gibbons, analyses should include pelage and vocalisation data, plus genetic loci which are variable at the species/subspecies level and have evolved more rapidly (for example, cytochrome oxidase II gene).
Finally, the results of the multivariate statistical analysis provide a useful tool for identifying gibbon morphotypes. Furthermore, such techniques may be used with accuracy to identify, to subgenus level, unidentified skeletal material, both fossil and recent.
191 3.4.3 Cladistic methods
Introduction to cladistics
Cladistics is a method of phylogenetic analysis formulated by the German entomologist Willi Hennig (1950, 1966). The method is also known as phylogenetic systematics, as it is used to reconstruct phylogenetic relationships among organisms and to construct classifications. Useful introductions to the principles and practice of cladistics can be found in a variety of texts, including Eldredge and Cracraft (1980),
Nelson and Platnick (1981), Forey et al. (1992) and Kitching et al. (1998). In addition to its use in biology, cladistics can also be used for organising any comparative information, hence its independent development in linguistics (Bonheim, 1990) and biogeography (Humphries and Parenti, 1998) (see Chapter 4).
Hennig defined phylogenetic relationships in terms of recency of common ancestry, and outlined a protocol for reconstructing relationships. Hennig recognised that similarities were an important part of any taxonomic study. He reasoned that features, or characters, change their status relative to the taxonomic problem under investigation. Hennig defined three different sorts of similarity: [1] shared derived characters inherited from the most recent common ancestor, termed ‘synapomorphies’,
[2 ] shared primitive characters, termed ‘symplesiomorphies’, which are inherited from more distant ancestors, and [3] ‘convergences’ or ‘homoplasy’ in which identical character states have evolved independently in separate lineages. According to cladistic principles, phylogenetic relationships are supported only by character states that can be inferred to be synapomorphic. Groups defined by such character states are known as
‘monophyletic’ groups. Hennig deemed symplesiomorphies and convergences to be.
192 respectively, irrelevant and misleading, for inferring phylogenetic relationship.
Paraphyletic groups are recognised by symplesiomorphies. Such groups include a most recent common ancestor plus only some of its descendants, in other words one or more of the components of a monophyletic group are excluded. Polyphyletic groups are defined by convergent characters and such groups do not include the most recent common ancestor of all its members.
Hennig proposed that phylogenetic relationship can be depicted as a rooted, branching diagram called a cladogram. Hennig saw cladograms as phylogenetic trees in which the nodes, or branching points, denoted divisions of ancestral species and in which a time axis implied from the most inclusive node to the terminal taxa. Others, however, have viewed cladograms with a relative timescale where the nodes represent synapomorphies without the notion of a particular pattern of ancestry (Platnick, 1979;
Patterson, 1982). Platnick (1979) and Patterson (1982) argued that Hennig’s view was problematic because theories about characters (synapomorphies) and theories about groups (monophyly) both appealed to common ancestry for justification. Hence, they removed ancestry from the analysis and shifted it to become part of the explanation of patterns. In short, monophyletic groups are discovered and paraphyletic and polyphyletic groups resolved in terms of character distribution and hierarchy, but all are free from a priori notions of common ancestry (Scotland, 1992).
Once a monophyletic group has been recognised, the next task is to search for its sister group. Sister groups are defined as species groups that arise from the stem species of a monophyletic group by one and the same splitting process. Sister groups are discovered by identifying synapomorphic characters inferred to have originated in their most recent common ancestor and shared by its descendants. These synapomorphies can
193 be considered as evolutionary homologies, in other words, as structures inherited from the most recent common ancestor. As such, sister groups are established by plotting the distribution of synapomorphies.
Another important premise of cladistics is the relationship that characters have to each other. On a given cladogram characters may be congruent, consistent or homoplastic. A congruent character is one that specifies the same set of relationships as another character. Consistent characters specify a subset of the group of taxa specified by another character. Homoplasy describes similarity between different taxa without the notion of common descent, and causes character conflict.
As stated previously monophyletic groups are discovered by identifying synapomorphies. However, Patterson (1982) synonymized homology with synapomorphy. In this scheme, homology is defined as the same structure inherited from a common ancestor, e.g. the wing of a bird in relation to the forelimb of another tetrapod. Patterson (1982) proposed that homologies be treated as hypotheses, such that they should be proposed and tested. This testing of homologies is central to cladistic analysis (Kitching et al., 1998). Furthermore, Patterson proposed a new way of testing homology, namely congruence. Congruence testing is an empirical procedure, and in order to pass a congruence test a character must specify the same group on cladogram as another character (Patterson, 1982). Patterson (1982) also proposed two other tests of primary homology: similarity test, and conjunction test. To pass the similarity test, two characters must be compatible in morphology, anatomy and topological position. To pass the conjunction test, two characters hypothesised to be homologous must not coexist in an organism at the same time. The final determinants of homology, however, are the characters and character states themselves, and these will be discussed in the next section.
194 Characters and character coding
Cladistic analysis consists of three steps: selection of characters and taxa, coding of characters, and determination of cladograms that best explain the distribution of characters states across the taxa (Kitching et a l, 1998). A character is an observable feature of an organism that may be used to distinguish it from another organism, i.e. characters are independent features. The term character state describes one of two or more alternative manifestations of a character (Kitching et al., 1998). Character states, as used in cladistic analysis, are frequency distributions of attribute values across a sample of individuals of a taxon (Thiele, 1993).
Morphological characters can be of two kinds, either discrete (qualitative) or continuous (quantitative). Discrete morphological data are represented by a subset of all values, generally integers. These include absence/presence data (0/1 being the only
values allowed), multistate data ( 0 /l/ 2 /...«) and meristitic data (counts of structures expressed as integers, directly scored into the matrix or rescaled). Continuous data, by contrast, are those where the potential values are so close to each other that there are no disallowable real numbers (Thiele, 1993). Qualitative and quantitative are modes of expression of data, while continuous or discrete refer to properties of the set of numbers that express the data (Thiele, 1993).
Some authors argue that continuous characters should not be included in cladistic analysis since they do not vary cladistically (e.g. Crowe, 1994; Pimentai and
Riggins, 1987). Others, however, suggest that data themselves cannot be cladistic or phenetic, and that these terms are only applicable to the analysis of the data (Rae,
1998). Those against the use of continuous data in cladistic analysis suggest these data
195 represent overlapping values (Crisp and Weston, 1987). As such, the data must be grouped into meaningful subsets using taxon means and/or statistical tests (this will be discussed in greater detail below). Crisp and Weston (1987) argue that there is no cladistic significance of a mean of a taxon. However, advocates of the use of continuous data in cladistic analysis suggest that these data do contain grouping homologies when identified through discrete coding (Kitching et al., 1998; Rae, 1998; Thiele, 1993).
Many authors agree that continuous data, including metric morphological data as presented here, fulfil the sole criterion for inclusion in phylogenetic analysis, namely the presence of homologous character states (e.g. Rae, 1998; Kitching et al., 1998;
Thiele, 1993; Baum, 1988). Furthermore, several studies have successfully employed continuous, metric data from a variety of primates to investigate phylogenetic inter relationships (e.g. Corruccini and McHenry, 1984; Stringer, 1987; Chamberlain and
Wood, 1987; Collard and Wood, 2000).
Thiele suggests that the only reason for rejecting characters from a cladistic analysis is if it can be demonstrated that they show no phylogenetic signal. He proves his point with a cladistic analysis of morphometric data from the angiosperm genus,
Banksia. The results indicate that with these taxa, they map phylogeny almost as well as qualitative morphological data. Kitching et al. (1998) are in agreement that in many cases morphometric and qualitative characters are found to map similar phylogenies and be informative about those phylogenies.
196 Coding morphometric data
Thus many cladists suggest that continuous data can be useful for cladistic analysis, since they may still contain grouping homologies and a phylogenetic signal
(e.g. Thiele, 1993). In order to identify these groups it is necessary to re-code the variables as discrete characters. This is necessary, since in order to determine if the variables hold any phylogenetic signal, they must be scorable into data matrices that contain some pattern for relationships of taxa to be discovered (Kitching et al., 1998).
Furthermore, the computer programs available for phylogenetic reconstruction (e.g.
PAUP) cannot cope with quantitative data. There are several methods of re-coding, which are generally described as gap-coding methods. These include: simple gap- coding (Mickevich and Johnson, 1976), segment coding (Thorpe, 1984), generalised gap-coding (Goldman, 1988), range-coding (Baum, 1988), and gap-weighting (Thiele,
1993). All these methods have one factor in common: they use a simple algorithm to create gaps in order to produce discrete codes for continuous or overlapping variables
(Kitching et al., 1998). Essentially, these methods rank the taxa along a scaled attribute axis, then divide the attribute axis into states. They differ in the degree to which they divide the attribute axis.
There is some discussion regarding the appropriate coding procedure of morphological metric data (Thorpe, 1984). One of the most common coding methods is segment coding (Rae, 1998). In this method the number of codes, their distribution and membership depends entirely on a priori decisions, in terms of the number of segments chosen. Colless (1980) argues that these types of coding procedure are arbitrary and suggests range coding as an alternative. Evidence for the success of range coding, and the analytical protocol, are discussed by Baum (1988). Baum (1988) assessed the success of the range coding procedure on the basis of the congruence of his cladograms
197 with biogeographic hypotheses and previously published trees. Since there appear in the literature to be pros and cons to using each type of coding, segment and range (Thorpe,
1983), both types were employed here.
The size-corrected morphological data were employed since cladistic analysis operates on the premise that characters are independent. If the effects of size were not removed the characters would not be independent since they would inevitably be correlates of size. Previous studies have indicated that when size is not controlled for, this factor exerts a dominant influence on relationships, which may not accurately reflect phylogeny (Chamberlain and Wood, 1987). As described in detail earlier
(section 3.4) the data was size-corrected using one of the Mosimann family of shape ratios (Mosimann, 1970). This involves dividing each variable by the geometric mean of all variables.
In segment coding the range of character states is divided into equal segments and then given an ordinal code, depending on the number of segments. The segments
were computed in the following way: 1 ) variable means were established for each
taxon, 2 ) an overall taxon range across all of the variables was established by selecting the minimum and maximum variable mean, 4) evenly spaced segments within the overall taxon range were selected and given a code, e.g. A, B, C, D, etc. The number of segments is a function of the number of taxa and the overall taxon range.
In range coding the variable mean for each taxon is ranked along a scale (e.g. 1 -
9). For example, the lowest mean for a particular variable, across the range of taxa, is
ranked 1 , the next lowest mean, is ranked 2 , and so on.
The size-corrected morphological datasets were segment and range coded into three separate matrices: cranial, dental and postcranial. This was necessary in order to
198 maintain manageable segment sizes and ranges. For example, if the cranial, dental and postcranial datasets were coded together the range would be huge and the number of segments too many to analyse computationally. The cranial dataset comprised 25 characters (with a segment size of 0.15), the dental dataset comprised 16 characters
(with a segment size of 0.15), and the postcranial dataset comprised 34 characters (with a segment size of 0.50).
Range coded and segment coded matrices comprising the following multiple combinations of the data files were also constructed in order to evaluate all possible permutations of the data: a matrix containing all cranial, dental and postcranial characters (75 characters), a matrix containing cranial and dental characters (41 characters), a matrix containing cranial and postcranial characters (59 characters), and a matrix containing dental and postcranial characters (50 characters). Matrices were constructed in Microsoft Excel version 5.0.
Outgroup
Outgroup taxa are groups related to, but not included within, the monophyly of the organisms under investigation (Chamberlain and Wood, 1987). Human morphological data was used as the outgroup in these analyses, since there were large enough sample sizes to measure. The same metric variables as measured on gibbon samples (Table 3.3) were recorded on 11 human skeletons. These data were coded along with the gibbon data, using the coding procedures discussed above.
199 Tree building and interpretation
Cladistic analysis operates on the general criterion that among competing hypotheses, the best hypothesis is the one which explains the data most simply and efficiently, i.e. most parsimoniously (Kitching et a l, 1998). Cladistic analysis uses parsimony to infer phylogenies. This operates by selecting trees that minimise the total tree length and hence the number of steps required to explain the data (Swofford et a l,
1996). This procedure is very time consuming, hence there are several methods available for searching for most parsimonious trees. These methods fall into two categories: heuristic and exact search mechanisms (Kitching et al., 1998). Heuristic search mechanisms do not guarantee to find all, or even any, of the shortest length trees.
Hence, this technique is computationally the fastest. Exact methods guarantee to find one or all of the shortest trees. Of these, the simplest is the exhaustive search mechanism. In this method every possible fully resolved, unrooted tree for all the included taxa is examined. This method is extremely time-consuming computationally, hence another exact method, branch and bound, is generally favoured over an exhaustive search (Kitching et al, 1998). The branch and bound method does not require every completed topology to be examined individually. Initially a cladogram is constructed by means of an heuristic method. The length of this cladogram is used as the initial upper bound limit for an exhaustive search. The number of resultant topologies is then restricted by discarding all partial cladograms whose length exceeds the upper bound (Kitching et al., 1998).
The morphological data were analysed using parsimony methods implemented using the software program PAUP Version 3.1.1 (Swofford, 1993). Datasets were exported from Excel into Microsoft Word version 6.0 where they were converted into a
PAUP readable format (see Appendix III for data matrices). The data matrices were
200 then directly imported into PAUP. Where possible, the branch and bound method was employed to construct trees since this is computationally the most feasible time-wise
(exhaustive searches being too slow, e.g. days, and heuristic searches not guaranteeing to find the shortest tree). However, when bootstrapping (see below) methods were employed the branch and bound method proved too slow (e.g. several days to analyse one dataset), hence a heuristic search mechanism was invoked.
Bootstrap methods were implemented using PAUP, in order to ascribe statistical confidence to tree branches. In bootstrapping a large number of pseudoreplicate data sets of the same size of the original are created by randomly sampling characters with replacement. The most parsimonious cladograms for these pseudoreplicates are calculated and a majority rule consensus tree is used to assess the degree of consensus/agreement among them (Kitching et a l, 1998). A majority rule consensus tree is a consensus tree formed from all of those components that occur in at least 50% of all of the trees (Kitching et al., 1998). This random sampling is repeated multiple times. The greater the number of repetitions the more time-consuming the method is.
Here, samples were repeated 100 times. Increasing the sampling number to 1000 was computationally too time consuming (e.g. days for one search). Hillis and Bull (1993) suggest that a confidence limit of 70% is an appropriate cut-off point for bootstrap values, and this limit was followed here (cf. Collard and Wood, 2000).
201 3.4.4 Results
Parsimony analysis of segment coded morphological data
Segment coded cranial data
Parsimony analysis involving the segment coded cranial dataset produced 11 equally parsimonious trees. The strict consensus tree (Figure 3.24) was unable to resolve relationships among gibbon subgenera. The tree shows {muelleri + pileatus),
{hoolock + syndactylus), and (concolor, gabriellae + leucogenys) as sister groups.
These clades, along with the rest of the taxa, form a polytomy. In this tree Nomascus is monophyletic.
Segment coded dental data
Fifty equally parsimonious trees were produced from a parsimony analysis using the segment coded dental dataset. The strict consensus tree (Figure 3.25) shows that pileatus, klossii, hoolock, concolor, gabriellae, leucogenys and syndactylus form a separate, polytomous, clade to lar, agilis, moloch and muelleri. The latter four species also form a polytomy. Hence, in this tree the subgenus Hylobates is paraphyletic.
Segment coded postcranial data
Parsimony analysis of the segment coded postcranial dataset produced 17 equally parsimonious trees. In the strict consensus tree (Figure 3.26) the subgenus
Hylobates is polyphyletic. The tree shows pileatus, muelleri and syndactylus as successively more closely related to a polytomous clade comprising {lar + agilis), moloch, klossii, hoolock, concolor, gabriellae and leucogenys.
202 Segment coded cranial, dental and postcranial data
Parsimony analysis using the segment coded dataset comprising all characters
(cranial, dental and postcranial) produced two equally parsimonious trees (Figure 3.27,
Tl, T2). In both trees subgenus Hylobates is polyphyletic, subgenus Nomascus is paraphyletic and {syndactylus + hoolock) form sister taxa.
203 - human
" lar {Hylobates)
- agilis {Hylobates)
- moloch {Hylobates)
•^muelleri {Hylobates)
- pileatus {Hylobates)
“ klossii {Hylobates)
- syndactylus {Symphalangus)
- hoolock {Bunopithecus)
- concolor {Nomascus)
- gabriellae {Nomascus)
-leucogenys {Nomascus)
Figure 3.24 Strict consensus of 11 equally parsimonious trees constructed using segment coded cranial data. (Subgenera are indicated in brackets).
204 human
lar {Hylobates)
agilis {Hylobates)
moloch {Hylobates)
muelleri {Hylobates)
pileatus {Hylobates)
klossii {Hylobates)
syndactylus {Symphalangus)
hoolock {Bunopithecus)
concolor {Nomascus)
gabriellae {Nomascus)
leucogenys {Nomascus)
Figure 3.25 Strict consensus of 50 equally parsimonious trees constructed using segment coded dental data. (Subgenera are indicated in brackets).
205 human
lar {Hylobates)
agilis {Hylobates)
moloch {Hylobates)
klossii {Hylobates)
hoolock {Bunopithecus)
' concolor {Nomascus)
' gabriellae {Nomascus)
' leucogenys {Nomascus)
i syndactylus {Symphalangus)
■ muelleri {Hylobates)
' pileatus {Hylobates)
Figure 3.26 Strict consensus of 17 equally parsimonious trees constructed using segment coded postcranial data. (Subgenera are indicated in brackets).
206 human
' lar {Hylobates)
' agilis {Hylobates)
• moloch {Hylobates)
■ pileatus {Hylobates)
' klossii {Hylobates)
■ concolor {Nomascus)
• leucogenys {Nomascus)
■ gabriellae {Nomascus)
hoolock {Bunopithecus)
' syndactylus {Symphalangus)
' muelleri {Hylobates) T l
' human
' lar {Hylobates)
■ agilis {Hylobates)
• moloch {Hylobates)
“ hoolock {Bunopithecus)
- syndactylus {Symphalangus)
“ pileatus {Hylobates)
- klossii {Hylobates)
- concolor {Nomascus)
- leucogenys {Nomascus)
- gabriellae {Nomascus)
” muelleri {Hylobates) T2
Figure 3.27 Two equally parsimonious trees (Tl and T2) constructed using all (cranial, dental and postcranial) segment coded data. (Subgenera are indicated in brackets).
207 Segment coded cranial and dental, cranial and postcranial, and dental and postcranial.
Parsimony analyses of the segment coded cranial and dental, cranial and postcranial, and dental and postcranial datasets resulted in three, four, and, two equally parsimonious trees, respectively (trees not shown). None of these trees shows the subgenus Hylobates to be monophyletic, and in only one tree (using the dental and postcranial dataset) is Nomascus monophyletic. In most cases subgenus Hylobates is polyphyletic and Nomascus is paraphyletic. In all of the trees produced using the cranial and dental and cranial and postcranial datasets hoolock and syndactylus consistently group as sister taxa.
Bootstrap analysis of segment coded data
Bootstrap analysis using the branch and bound method proved computationally too slow (e.g. several days to analyse one dataset). Thus, an heuristic search mechanism was employed to find bootstrap trees. This method is quicker since it is not guaranteed to find the most parsimonious solution. Bootstrap analyses of the segment coded datasets consistently produced low support values (e.g. less than 50%) for all clades except the sister group {hoolock + syndactylus). This is shown in the bootstrap 50% majority rule consensus tree constructed using the segment coded cranial dataset
(Figure 3.28).
208 • human
■ klossii {Hylobates)
■ leucogenys {Nomascus) 16
32 ■ concolor {Nomascus)
27 gabriellae {Nomascus)
pileatus {Hylobates)
" muelleri {Hylobates) 23 lar {Hylobates) 39 41 C moloch {Hylobates)
45 agilis {Hylobates) hoolock {Bunopithecus) 33 94 syndactylus {Symphalangus)
Figure 3.28 Bootstrap 50% majority rule consensus tree constructed using segment
coded cranial data. (Subgenera are indicated in brackets).
209 Parsimony analysis of range coded morphological data
Range coded cranial data
Parsimony analysis of the range coded cranial dataset produced two equally parsimonious trees (Figure 3.29, T3, T4). In T3 gabriellae, concolor, and leucogenys are successively more closely related to a clade comprising lar and {hoolock + syndactylus), and the most proximal clade comprising agilis, and {moloch + klossii) and
{pileatus + muelleri). Hence, in this tree the subgenus Hylobates is paraphyletic. In T4 leucogenys and {concolor + gabriellae) form a separate clade to {hoolock + syndactylus), which in turn form a separate clade to taxa in the subgenus Hylobates.
Hence, Nomascus and {Bunopithecus + Symphalangus) are respectively more closely related to subgenus Hylobates.
Range coded dental data
Parsimony analysis of the range coded dental dataset produced only one tree
(Figure 3.30). The tree shows that subgenus Hylobates is polyphyletic, and that
Nomascus in paraphyletic. A sister group is again formed between hoolock and syndactylus.
Range coded postcranial data
The analysis using the postcranial dataset produced only one tree (Figure 3.31).
This tree shows the same pattern as the tree depicted in Figure 3.30, except that hoolock and syndactylus do not form sister taxa.
210 Range coded cranial, dental and postcranial data
Analysis of the dataset comprising all characters produced one tree (Figure
3.32). The tree shows subgenus Hylobates as polyphyletic; Nomascus is monophyletic and forms a separate clade which is the most proximal on the tree. The tree also shows hoolock and syndactylus as sister taxa.
Range coded cranial and postcranial data
Parsimony analysis of the range coded cranial and postcranial dataset produced two trees. In both trees subgenus Hylobates is polyphyletic, Nomascus is monophyletic and forms a separate clade which is the deepest on the tree, and {hoolock + syndactylus) form sister taxa. This is depicted in the consensus tree (Figure 3.33).
Range coded cranial and dental data
Analysis using the cranial and dental dataset produced only one tree (Figure
3.34). The tree shows that Nomascus is paraphyletic and groups {hoolock + syndactylus) as sister taxa.
Range coded dental and postcranial
Lastly, only one most parsimonious tree was retained by parsimony analysis of the dental and postcranial dataset (Figure 3.35). The tree shows that subgenus Hylobates is polyphyletic, Nomascus is paraphyletic, but hoolock and syndactylus do not group as sister taxa as before.
211 human
gabriellae {Nomascua)
concolor (Nomascus)
leucogenys (Nomascus)
lar {Hylobates)
hoolock {Bunopithecus)
syndactylus {Symphalangus)
agilis {Hylobates)
moloch {Hylobates)
- klossii {Hylobates)
muelleri {Hylobates) -
pileatus {Hylobates) T3
human
leucogenys {Nomascus)
concolor {Nomascus)
gabriellae {Nomascus)
hoolock {Bunopithecus)
syndactylus {Symphalangus)
agilis {Hylobates) '
muelleri {Hylobates)
pileatus {Hylobates)
klossii {Hylobates)
lar {Hylobates)
moloch {Hylobates) T4
Figure 3.29 Two equally parsimonious trees (T3 and T4) constructed using range coded cranial data. (Subgenera are indicated in brackets).
212 human
moloch {Hylobates)
agilis {Hylobates)
lar {Hylobates)
muelleri {Hylobates)
pileatus {Hylobates)
klossii {Hylobates)
hoolock {Bunopithecus)
syndactylus {Symphalangus)
leucogenys {Nomascus)
concolor {Nomascus)
gabriellae {Nomascus)
Figure 3.30 Most parsimonious trees constructed using range coded dental data.
(Subgenera are indicated in brackets).
213 human
pileatus {Hylobates)
muelleri {Hylobates)
agilis {Hylobates)
lar {Hylobates)
moloch {Hylobates) ■
klossii {Hylobates)
concolor {Nomascus)
gabriellae {Nomascus)
leucogenys {Nomascus)
' hoolock {Bunopithecus)
syndactylus {Symphalangus)
Figure 3.31 Most parsimonious tree constructed using range coded postcranial data.
(Subgenera are indicated in brackets).
214 human
pileatus {Hylobates)
muelleri {Hylobates)
lar {Hylobates)
agilis {Hylobates)
moloch {Hylobates)
hoolock {Bunopithecus)
syndactylus {Symphalangus)
klossii {Hylobates)
■ concolor {Nomascus)
' gabriellae {Nomascus)
• leucogenys {Nomascus)
Figure 3.32 Most parsimonious tree constructed using all (cranial, dental and postcranial) range coded data, (Subgenera are indicated in brackets).
215 human
concolor {Nomascus)
gabriellae {Nomascus)
leucogenys {Nomascus)
klossii {Hylobates)
lar {Hylobates)
moloch {Hylobates)
hoolock {Bunopithecus)
syndactylus {Symphalangus)
agilis {Hylobates)
muelleri {Hylobates)
pileatus {Hylobates)
Figure 3.33 Strict consensus of two equally parsimonious trees constructed using range
coded cranial and postcranial data. (Subgenera are indicated in brackets).
216 human
moloch {Hylobates)
agilis {Hylobates)
lar {Hylobates)
muelleri {Hylobates)
pileatus {Hylobates)
klossii {Hylobates)
hoolock {Bunopithecus)
syndactylus {Symphalangus)
leucogenys {Nomascus)
concolor {Nomascus)
gabriellae {Nomascus)
Figure 3.34 Most parsimonious tree constructed using range coded cranial and dental data. (Subgenera are indicated in brackets).
217 human
pileatus {Hylobates)
muelleri {Hylobates)
lar {Hylobates)
agilis {Hylobates)
moloch {Hylobates)
syndactylus {Symphalangus)
hoolock {Bunopithecus)
klossii {Hylobates)
concolor {Nomascus)
gabriellae {Nomascus) cleucogenys {Nomascus)
Figure 3.35 Most parsimonious tree constructed using range coded dental and postcranial data. (Subgenera are indicated in brackets).
218 Bootstrap analysis of range coded morphological data
As with the bootstrap analyses of the segment coded data, bootstrap analyses of the range coded data proved computationally too slow using the branch and bound algorithm. Hence, a heuristic search mechanism was employed. Again bootstrap support values were low (typically less than 50%), except for the sister group {hoolock + syndactylus) for which values over 80% were observed. This is shown in the bootstrap
50% majority rule consensus tree constructed using the range coded cranial dataset
(Figure 3.36).
219 human
leucogenys {Nomascus)
51 F— concolor {Nomascus) 36 gabriellae {Nomascus)
lar {Hylobates)
28 ------hoolock {Bunopithecus)
94 syndactylus {Symphalangus)
47 ------muelleri {Hylobates)
24 ------pileatus {Hylobates)
19 agilis {Hylobates)
10 moloch {Hylobates) 1------38 klossii {Hylobates)
Figure 3.36 Bootstrap 50% majority rule consensus tree constructed using range coded cranial data. (Subgenera are indicated in brackets).
220 Discussion
Most parsimony analyses involving segment and range coded morphometric data were unable to resolve phylogenetic relationships among the four gibbon subgenera or at the interspecific level. Moreover, most trees showed the subgenera comprising more than one species, Hylobates and Nomascus, as paraphyletic or polyphyletic. These results are in contrast to the results of the molecular study presented here and many previous studies which suggest that each of the four gibbon subgenera is monophyletic (e.g. Chivers, 1977; Haimoff et a i, 1982; Cronin et al., 1984; Marshall and Sugardjito, 1986; Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et a l, 1995; Hall, et al., 1996, 1998). Furthermore, the trees are weakly supported and incongruent with each other.
Cronin et al. (1984), Garza and Woodruff (1992), Hayashi et al. (1995) and Hall et al. (1996, 1998) have used a variety of molecular data, including mitochondrial cytochrome b gene and immunological data, which provides evidence of gibbon subgeneric monophyly. Chivers (1977), Haimoff et al. (1984), Marshall and Sugardjito
(1986) and Geissmann (1993, 1995) have employed morphological, behavioural and communicatory characterictics (including pelage and olfactory data) to investigate phylogenetic relationships among gibbons, and all agree on the monophyly of the subgenera. Furthermore, the results of the molecular study presented earlier based on cytochrome b gene, indicate that each of the gibbon subgenera is monophyletic.
The paraphyly and/or polyphyly of Nomascus and Hylobates is also in contrast to the results of multivariate statistical analyses presented here (section 3.4.2) and elsewhere (Creel and Preuschoft, 1976, 1984) which indicate that several characteristics
221 of the skeleton serve to distinguish gibbon subgenera. The exception to this is the grouping of klossii.
In the past other studies based largely on morphological data (Groves, 1972;
Schultz, 1933) have indicated that the subgenus Hylobates may not be monophyletic.
Groves (1972) and Schultz (1933) suggest that klossii is phenetically “intermediate” between hoolock and syndactylus, while the rest of subgenus Hylobates appear as the most recently diverged species, after hoolock. As discussed earlier, however, while the morphological differences separating klossii from the rest of subgenus Hylobates are accepted, most authors agree that there is more evidence (e.g. molecular) to indicate it should belong within subgenus Hylobates. Furthermore, the non-grouping of klossii within subgenus Hylobates shown in many of the trees generated in this study is not to be trusted on the basis of the low bootstrap support.
With regard to the other subgenera there is no evidence from this molecular study, or other studies, that these are not monophyletic (Groves, 1972, 1984, 1989,
1993; Haimoff et a l, 1982; Geissmann, 1993, 1995; Garza and Woodruff, 1992;
Hayashi et al., 1995; Hall, et al., 1996, 1998). Some of the cladistic trees presented here show Nomascus as either paraphyletic or polyphyletic (e.g. Figures 3.27, 3.28, 3.30,
3.31, 3.34, 3.35) while others show Nomascus as monophyletic (e.g. Figures 3.24, 3.29,
3.32, 3.33, 3.36). All of the groupings, however, are weakly supported with low bootstrap scores.
In most of the analyses, Bunopithecus and Symphalangus, consistently group as sister taxa (Figures 3.24, 3.27, 3.28, 3.29, 3.30, 3.32, 3.33, 3.34, 3.36), and this is
222 supported by high bootstrap values (e.g. 94%, Figures 3.28 and 3.36). In light of the weak support for the rest of each of the trees, however, the sister taxa grouping of
Bunopithecus and Symphalangus should be treated with caution.
Thus, there are two keys reasons for rejecting the cladistic trees generated in this study based on metric measurements: [1] Lack of resolution among the trees and low bootstrap support; [2] Inconsistencies regarding the monophyly of gibbon subgenera, compared to the results of the molecular study presented in the previous chapter based on cytochrome b gene, and previous works. Most of the trees are badly resolved, showing many polytomies, low bootstrap values and incongruence in topologies among the trees. There is overwhelming evidence from molecular and non-molecular studies that the gibbon subgenera are monophyletic, while many of the trees resulting from this morphological study show them to he either paraphyletic or polyphyletic (e.g. Chivers,
1977; Haimoff et a l, 1982; Cronin et a l, 1984; Marshall and Sugardjito, 1986;
Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et ah, 1995; Hall et al.,
1996, 1998).
These conclusions indicate that the morphological data analysed in this study, to construct cladistic trees, may be problematic. There could be several reasons for this: homoplasy, the method of character coding used, or uninformative characters. It is widely accepted that it is difficult to recognise homoplastic characters within a dataset, hut, that their presence can cause phylogenetic confusion (e.g. Lieberman et ah, 1996).
Hence, the presence of homoplastic characters can result in a cladogram which exhibits incongruence relative to the supposed phylogeny of a particular group.
The choice of character coding employed seems an unlikely explanation for the cause of the poorly resolved trees generated in this study. Two independent re-coding
223 methods were used, and both gave rise to trees showing a lack of resolution, weak support and inconsistencies compared to the molecular estimate of gibbon phylogeny and previous studies.
It is proposed that the most likely explanation for the lack of resolution
(including weak bootstrap support) in the trees presented in this study, and hence the inconsistencies with previous works, is due to the use of metric characters in cladistic analyses. The results of this study are in line with other authors who have found that metric, skeletal data is uninformative and/or is unreliable for phylogenetic reconstruction. For example, Collard and Wood (2000) have shown that a cladistic analysis using metric craniodental data representing the extant hominoids and papionins produced trees which were weakly supported, and which were incompatible with molecular estimates of hominoid and papionin phylogeny. Indeed, several authors have gone one step further and proposed that skeletal morphology is phylogenetically uninformative at the species and genus level (Lieberman, 1995, 1999; Pilbeam, 1996;
Jablonski, 1999a; Collard and Wood, 2000). While there is insufficient evidence here to confirm or reject such a hypothesis, the congruence between the results of the multivariate statistical study presented here, and the taxonomic divisions established for gibbons, indicates that, regarding gibbons, skeletal morphology is not completely useless for systematics: it is simply that the use of continuous, metric data is problematic in cladistic analysis.
224 3.5 Conclusions
Multivariate statistical analyses of 75 cranial, dental and postcranial metric variables recorded on samples representing 11 different species of gibbon, showed these taxa form five distinct morphological groups: (1) syndactylus, (2) lar, agilis, moloch, muelleri and pileatus, (3) klossii, (4) concolor, gabriellae and leucogenys, and (5) hoolock. Except for the grouping of klossii, these analyses have differentiated the various species of gibbon in accordance with the widely accepted subgeneric groupings:
(1) Symphalangus, (2) Hylobates, (3) Nomascus, and (4) Bunopithecus, respectively.
This is in agreement with previous studies which have used fewer metric variables, and/or only covered the cranium and dentition (Creel and Preuschoft, 1976, 1984;
Groves, 1972).
Furthermore, this study has shown that the newly described species in subgenus
Nomascus, H. gabriellae and H. leucogenys, hold close morphological affinities with H. concolor. The multivariate statistical analysis of gibbon metric variables has also identified a suite of cranial, dental and postcranial features which serve to distinguish the five morphological groupings discussed above.
Cladistic analyses of the re-coded metric variables were unable to resolve relationships among gibbon subgenera or species. Cladistic trees were badly resolved, showing many polytomies, and weakly supported. Furthermore, the results are in conflict with the estimate of gibbon phylogeny generated using cytochrome b gene, and with previous studies, that show the gibbon subgenera are monophyletic (e.g. Chivers,
1977; Haimoff et al., 1982; Cronin et a l, 1984; Marshall and Sugardjito, 1986;
Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et al., 1995; Hall, et al..
225 1996, 1998). These analyses indicate that the re-coded metric variables are phylogenetically uninformative at the species and subgenus level. This confirms the
findings of other authors who have used morphological data to investigate phylogenetic relationships among other primates (e.g. Lieberman, 1995, 1999; Pilbeam, 1996;
Jablonski, 1999a; Collard and Wood, 2000).
Despite the lack of resolution regarding relationships among gibbon subgenera,
and the incongruence of the results presented here compared to other gibbon phylogenetic studies, several interesting conclusions can be made in light of this study.
Previous studies have not included a detailed cladistic analyses across the whole gibbon skeleton for all 11 gibbon species. This has been achieved here and may be built on by
incorporating other aspects of gibbon morphology, such as pelage and soft tissue characteristics, and through further molecular analyses. These analyses provide further evidence from another group of taxa, supporting the proposal that metric data are problematic in phylogenetic reconstruction.
226 Chapter 4: Biogeographic Study
Contents
Page numbers
4.1 Introduction to Historical Biogeography 228 4.1.1 Dispersal Biogeography 231 4.1.2 Panbiogeography 232 4.1.3 Vicariance Biogeography 233 4.1.4 Cladistic Biogeography 234 4.1.5 Alternative biogeographic models - Réfugia 237 4.2 Materials and Methods 239 4.2.1 Ancestral area analysis using irreversible parsimony 244 4.2.1.1 Introduction to the method 244 4.2.1.2 The method 244 4.2.1.3 Results 247 4.2.2 Ancestral area analysis using reversible parsimony 248 4.2.2.1 Introduction to the method 248 4.2.2.2 The method 248 4.2.2.3 Results 250 4.2.3 Dispersal-vicariance analysis 252 4.2.3.1 Introduction to the method 252 4.2.3.2 The method 253 4.2.3.3 Results 259 4.3 Discussion 261 4.4 Conclusions 268
227 4.1 Introduction to Historical Biogeography
Historical biogeography aims to reconstruct the pattern of radiation of taxa, in order to explain their present day distributions. Explanations of the distribution of flora and fauna fall into two categories: dispersal and vicariance. Dispersal refers to the spread of an organism across a pre-existing barrier, while vicariance refers to the appearance of a barrier causing fragmentation. Traditionally dispersalist explanations were used to account for the disjunct distributions of related taxa. Under the dispersalist scenario disjunct ranges were interpreted as evidence of the dispersal of taxa across pre existing geographical or ecological barriers, usually from a biogeographic ‘centre of origin’. In other words, species originate in a particular area and tend to expand their ranges via dispersal. This explanation for the geographic distribution of organisms was advocated by Darlington (1957), Simpson (1953) and Briggs (1984), who embraced the original theories of Darwin, Hooker and Wallace. It has been termed ‘centre of origin- dispersal’ or ‘classical dispersal’ (Nelson and Platnick, 1981).
Several alternative theories to ‘centre of origin-dispersal’ appeared in the literature since the early work of Darlington (1957) and Simpson (1953). Most notable of these was Croizat’s ‘panbiogeography’ (1958). Panbiogeography can be described as a biogeographic method that focuses on spatiotemporal analysis of the distribution patterns of organisms. A useful review of panbiogeography can be found in Humphries and Parenti (1999) in which Croizat’s hypotheses have been reduced to two succinct, general principles. The first states that tectonic change, not dispersal, explains the disjunct distribution of species. The second principle states that the main biogeographic regions occupied by terrestrial organisms correspond to modem ocean basins. Unlike dispersalist theories, which draw a distinction between geological history and the distribution of organisms, Croizat brought the two together. He proposed that tectonic
228 change was the process which allows spéciation, and gives rise to the present day distinctive forms of flora and fauna.
In the 1960s and 1970’s the general acceptance of plate tectonics and other geological processes indicated that the earth’s features were not fixed, but rather showed vast historical variability. The overall acceptance of theories of continental drift confirmed the idea that disjunct biotic patterns and corresponding geological patterns are due to the same events in earth history. This had the effect of undermining the basic suppositions behind dispersal biogeography which were based on fixed continental positions. This new understanding of earth and biotic history created the field known as
‘vicariance biogeography’.
Vicariance biogeography is the study of repeated patterns of distribution within many members of a biota that may be explained by vicariance events (Humphries and
Parenti, 1999). In vicariance models, taxonomic differentiation is attributed to the isolation of populations by the appearance of barriers, as opposed to the chance dispersal of individuals into new areas.
The subsequent fusion of vicariance biogeography with cladistics (Hennig,
1966), created cladistic biogeography. Humphries and Parenti (1999) draw a distinction between cladistic and vicariance biogeography, describing cladistic biogeography as a method that searches for patterns of relationships among areas of endemism using a combination of cladistics and vicariance biogeography.
It is relevant at this stage to note that across the current literature there are in use a range of terms to describe the different methodological approaches to historical biogeographic reconstruction. For example, Brundin (1988) draws a methodological distinction between cladistic and vicariance biogeography and phylogenetic biogeography. In the latter analysis several hypotheses are incorporated including
229 vicariance, dispersal and geological history; and the congruence of distribution patterns with evolutionary and geological hypotheses is tested. In cladistic biogeography, however, no hypotheses are incorporated about past geological or evolutionary events.
Confusion arises from the fact that Brundin (1988) uses the term phylogenetic biogeography in place of cladistic biogeography. He prefers the phylogenetic prefix since he believes ‘cladistic’ implies a somewhat reductionist attitude in trying to be as independent as possible of an evolutionary theory. Brundin’s perception of phylogenetic biogeography is “a study of the history of life in time and space through simultaneous development and integration of an evolutionary theory beyond neo-Darwinism that answers the requirements of the phylogeneticist and phylogenetic biogeographer”
(1988, p. 348). In contrast, Myers and Giller (1988) view vicariance biogeography as the field incorporating panbiogeography and cladistic biogeography. Humphries and
Parenti (1986, 1999), on the other hand, prefer to view cladistic biogeography more as a field of investigation which had its roots in panbiogeography, and progressed through vicariance biogeography. To avoid confusion and to maintain continuity, the methodology and terminology proposed by Humphries et al. (1988) and Humphries and
Parenti (1986, 1999) are adopted in this study.
In the following sections a more detailed review of the main fields of historical biogeography is provided, along with descriptions of the different methodologies applied for each type. This chronological background is appropriate since the synthesis behind the method adopted in this study, namely cladistic biogeography, is rooted in the older approaches to historical biogeography.
230 4.1.1 Dispersal Biogeography and Centres of Origin
Dispersal biogeography follows the premise that from a centre of origin, a species undergoes chance (sometimes known as jump) dispersal across a pre-existing barrier. Some of the earliest studies of historical biogeography were made by Darwin
(1859) and Wallace (1876). These traditional explanations for the geographic distributions of fauna and flora suggested that species originate in particular areas and tend to expand their ranges. Such explanations presupposed that the main geographic features of the globe were fixed, in other words the theory of continental drift was not entertained.
Dispersal hypotheses encompass two distinct mechanisms, one of which
Platnick and Nelson (1978) suggest is vicariance in disguise. The first, truly, classical dispersalist model postulates dispersal over a pre-existing barrier. In this case an ancestral species (A) expands its range by means of an accidental or chance crossing of a barrier, causing fragmentation of its range. The effect is instantaneous isolation and disjunction. The populations in the disjunct areas subsequently differentiate into two allopatric species (B and C). In the second hypothesis an ancestral species (A) enlarges its range through time and is then fragmented into two disjunct ranges. The resultant populations differentiate over time, ultimately forming two allopatric species (B and C).
Embodied within this explanation is the assumption that some causal factor is responsible for the fragmentation of the range of the ancestral species, namely, the appearance of a barrier. The postulated dispersal takes place prior to the appearance of the barrier, and prior to the fragmentation of the range of the ancestral species, i.e. via vicariance.
231 4.1.2 Panbiogeography
One of the most notable turning points in historical biogeography came from
Leon Croizat’s book “Space, Time, Form: the biological synthesis” (1964). Croizat’s major contribution was to show that geological and climatological history of the earth and spéciation are congruent. He developed a method of analysis that dealt directly with the spatiotemporal aspects of taxa rather than strictly from the point of view of their taxonomic relationships.
Croizat’s approach was to twofold: to establish which areas of endemism are more closely related to one another in terms of other areas, and to develop a general method of identifying biogeographic homologies, by linking distributions of related taxa in ‘track’ analysis (Humphries and Parenti, 1999). A track can be defined as a line graph drawn on a map of the geographic distribution of a particular taxon (this can refer to a species, genus, or family), which connects two or more areas of endemism. This track is interpreted as a graph of the geographic distribution of the taxon representing the ‘primary co-ordinates’ of that taxon in space (Croizat, 1964). Once tracks have been identified they can be linked together to understand biogeographic patterns on .a broader scale to create generalised tracks. These generalised tracks are a way of identifying areas of endemism. However, these statements contain only limited information regarding the relationship between areas of endemism, and this has been proposed as a limiting factor in panbiogeographic analysis (Humphries and Parenti, 1999).
232 4.1.3 Vicariance Biogeography
The main criticisms of dispersal biogeography relate to the fact that as a method
it lacks a testable definition of relationships. Panbiogeography has also been criticised
since track analysis lacks precision in determining what constitutes the raw data of
biogeographic homologies, and what specifically determines area relationships
(Humphries and Parenti, 1999). Hennig (1966) provided the rationale and analytical
tools for reconstructing evolutionary relationships among members of a group. The
growth of Hennig’s phylogenetic systematics, combined with a general acceptance of
the theories of plate tectonics, were the leading forces in the development of vicariance
and cladistic biogeography.
Vicariance biogeography is concerned with discovering the commonality of the
observed distribution patterns shown by unrelated taxa, which implies a common and
simultaneous process. Hypotheses are based on the assumption that biota are vicariated
(split up) by the appearance of barriers through, for example, continental drift, which
separates parts of a once continuous biota. This vicariant event has the effect of altering
the proximity of populations irrespective of their own movements. Vicariance ,
biogeographic hypotheses are formulated to try to piece together the fragments and
allow determination of the sequence of disjunction of the various parts of the once
continuous biota. Furthermore, vicariance biogeography allows inferences to be made
regarding the earth processes that led to its fragmentation (Myers and Giller, 1988).
Vicariance biogeography applies the concept of vicariance to explain panbiogeography’s’ ‘tracks’, in the light of geological theory. This has been criticised,
however, as this method is dependant on the assumptions about biogeographic history
and geological history being accurate. The question of whether a track can be refuted in
the same way as a cladogram, has also been raised. In order to make comparisons
233 between organisms and areas it was deemed necessary to have cladograms of areas that can be compared with taxonomic cladograms - this technique has become known as cladistic biogeography (Humphries and Parenti, 1999).
4.1.4 Cladistic biogeography
Cladistic biogeography involves constructing cladograms of areas occupied by individual taxonomic groups. Relationships between areas inhabited by endemic species are determined on the basis of shared derived taxa (equivalent to shared derived character states in taxonomy). General area cladograms are derived by adding the individual cladograms together to give statements about biotas. The method of analysis involves first finding monophyletic groups with taxa occurring in at least three similar areas. This is known as the three taxon three area paradigm, i.e. given three taxa in three areas, the two areas which share the taxa with the more recent common ancestor will be the more recently vicariated. Area cladograms must then be derived for each group of organisms. The names of the taxa at the terminal tips of the cladograms are replaced by the names of the areas in which each taxon occurs (Platnick and Nelson, 1978). The sum of the areas on one cladogram is equivalent to a track or ancestral area. To obtain a cladogram of a biota, the individual cladograms are added together.
Humphries et al. (1988) suggest that the earliest application of cladistic methods to biogeography was that of Hennig (1966) when he used a cladogram to determine the
‘centre of origin’ of a monophyletic group. Central to Hennig’s (1966) method was the idea that phylogenetically primitive members of a monophyletic group, will by definition be found near the centre, and hence these will be the most plesiomorphic taxa. This concept is known as the ‘progression rule’. It supposes that within a
234 continuous range of species of a monophyletic group, a transformation series of characters can run parallel with space such that the youngest members are on the periphery of a group (Humphries and Parenti, 1999). Subsequently, Humphries et al.
(1988) have rejected the progression rule, suggesting that it is not viable for predicting disjunct distributions of organisms that have been caused by vicariance. More recently however, Bremer (1992) has invoked the progression rule in a method which optimises areas on a single cladogram to infer a centre of origin or dispersal. Platnick and
Nelson’s (1978) concept of cladistic biogeography relies on the premise that disjunct distributions can be explained by vicariance events, largely rejecting the dispersal role.
Modem methods of cladistic biogeography, therefore, have differed regarding their treatment of vicariance over dispersal, and much controversy exists about the inclusion of these processes in the different protocols.
The principle of area cladograms can be tested by comparison with other unrelated taxonomic groups that maintain the same geographic distribution as, or are endemic with, the taxonomic group being studied. A consensus cladogram, or general area cladogram, can then be sought to test congruence. Early applications of this method (Rosen, 1978) encountered problems of incongruence and unresolved patterns in the general area cladogram. This is because the observed geographic distribution of a group of organisms is likely to be the result of a variety of natural processes, including extinction events, dispersal and vicariance. All of these possibilities might have resulted in the distribution pattern of the organisms under study. The concept in cladistic biogeography of testing congruence between area cladograms has been incorporated into computer applications, including component analysis (Page, 1988).
Many of the approaches to cladistic biogeography have been criticised, however, on the basis that they rely only on vicariance to explain the disjunct distribution of
235 endemic taxa. In these methods little or no allowances have been made for dispersal and extinction, and their treatment has posed a problem in the development of an analytical protocol. Clearly there is a need to consider dispersal, since it is perfectly feasible that it may have contributed to the present day distribution of organisms and hence ought to be included in any reconstruction of a group’s historical biogeography (Ronquist, 1997).
As a result a number of new methodological approaches have emerged which attempt to deal explicitly with dispersal and extinction, these include: ancestral area (AA) analysis
(Bremer, 1992, 1995; Ronquist, 1994, 1995) and dispersal-vicariance analysis (DIVA)
(Ronquist, 1997), and these will be explored in the present study.
The term cladistic biogeography is here taken to include the newly developed techniques which incorporate dispersalist theories, such as AA analysis and DIVA.
Some descriptions of the term cladistic biogeography state that it is the combination of cladistics with vicariance biogeography (Humphries and Parenti, 1999). This implies that those methods which incorporate dispersalist theories are not cladistic biogeographic methods. However, since AA analysis and DIVA both rely on cladograms and parsimony algorithms to make inferences regarding the pattern of distribution of taxa, it is proposed that they are cladistic biogeographic methods. AA analysis and DIVA differ from other methods of cladistic biogeography in that they only require one cladogram, and hence do not use congruence testing. Rosen (1994) has proposed that it is valid to only use one cladogram to reconstruct biogeography, since it is still possible to make inferences regarding that groups past distribution patterns and ancestral areas.
236 4.1.5 An alternative biogeographic model used to explain distribution patterns -
Réfugia
Before continuing with the methods of analysis employed in this study it is worth mentioning, briefly, an alternative model which has been used to investigate patterns of geographic distribution on smaller, regional scales, particularly of tropical forest faunas. The ‘refuge’ hypothesis proposes that the distribution and spéciation patterns of tropical forest fauna and flora can be explained by the contraction and fragmentation of forests under past climatic conditions (Haffer, 1969). This method suggests that during the dry phases of the Pleistocene, tropical lowland forests contracted and further fragmented into small isolated patches that were separated from each other by areas of savannah. Within these isolated patches of rain forest known as
‘refuges’, it is further suggested that trapped forest species frequently underwent spéciation.
There is a huge body of evidence indicating that during glacial-interglacial cycles the earth’s surface has experienced dramatic climatic and vegetation changes
(e.g. Flenley, 1979; Lowe and Walker, 1997). Evidence from a variety of sources has revealed that during the Pleistocene there were a number of glacial cycles which resulted in fluctuations of sea-level and climate (e.g. Chappell and Shackleton 1986;
Chappell, 1994; Bellwood, 1997 and references therein). The ratio of oxygen isotopes
(16o;18o) from sediments in deep-sea cores, for example, has been analysed, and the resultant fluctuations plotted to estimate the periodicity of glacials and interglacials. It is now known that there have been approximately twenty glacial periods within the past
2 million years, with the same number of intervening interglacials, plus periodic intermediate interstadials within the glacials themselves. This record has been determined from deep-sea cores, from deep pollen-bearing soil profiles, from gastropod
237 faunas in loess deposits, and also from the dating of coral reefs as indicators of past sea levels (see Bellwood, 1997 and references therein). Furthermore, there is specific evidence from fossil pollen records that during these glaciations Southeast Asian forest habitats contracted (Morley and Flenley, 1987; Heaney, 1991).
While there is much evidence that climatic deterioration caused tropical forests to contract (Heaney, 1991), it is not clear that this contraction was necessarily accompanied by fragmentation of the forests into isolated patches, or refuges. Sufficient evidence for the latter has created much controversy regarding the refuge hypothesis.
Proponents of the refuge hypothesis have invoked the theory to explain the distribution patterns of a variety of organisms (e.g. Wenink et al., 1996; Roy, 1997;
Haffer, 1997; Murdoch and Hebert, 1997; Kidd and Freisen, 1998; Schneider and
Moritz, 1998; Hewitt, 1999). Among primates, the refuge hypothesis has been employed by Hamilton (1988) and Brandon-Jones (1996). Critics of the refuge hypothesis, however, claim that much of the evidence can be explained alternatively
(e.g. Endler, 1982; Colinvaux, 1989, 1998).
The refuge hypothesis is not necessarily mutually exclusive to other historical biogeographic explanations for the distribution of organisms. Indeed, it is through implication another form of vicariance since climatic deterioration causes fragmentation, allowing for spéciation.
238 4.2 Materials and Methods
Of the various methods outlined above, cladistic biogeographic techniques have been employed here since this method offers the most comprehensive approach to studying relationships among areas and making inferences about biogeographic history on the basis of those relationships. This is one of the key aims of this study. In addition, these methods require no prior information regarding palaeontological or palaeoenvironmental history. This is particularly relevant in this study since the fossil record for gibbons is sparse (see section 1.3.4 for more detail).
There are a number of different computer applications available for cladistic biogeographic methods, including: Brooks Parsimony Analysis (Brooks, 1990),
Component Compatibility Analysis (Zandee and Roos, 1987), Component Analysis
(Page, 1988), Three-Area Statements Analysis (Nelson and Ladiges, 1991 a and b), and
Paralogy-free Subtree Analysis (Nelson and Ladiges, 1996). These protocols rely on testing congruence of area cladograms derived from two or more different groups of organisms occupying the same geographic areas. There are comparable taxa occupying similar geographic distributions across SE Asia, such as the Asian colobines and cercopithecines, which could be used to test congruence. However, phylogenetic relationships within these groups are controversial (Zhang and Ryder, 1998; Rosenblum et al., 1997; Hayasaka et a l, 1996; Zhang and Shi, 1993; Jablonski and Zhang, 1993).
Rather than selecting one phytogeny from several competing phytogenies it was decided that further analysis of the phylogenetic relationships within these groups is warranted, as this could have severe effects on the outcome of these biogeographic analyses. Phylogenetic analysis of relationships within, for example, the Asian colobines or cercopithecines is not with in the remit of this study. Hence, it was decided that non-congruence testing forms of cladistic analysis were appropriate to reconstruct
239 the biogeographic history of gibbons. Three such techniques have been developed which require only one monophyletic group plus the distribution data of the taxa in that group: these are AA (ancestral area) analysis using irreversible parsimony (Bremer,
1992, 1995), AA analysis using reversible parsimony (Ronquist, 1994, 1995) and DIVA
(dispersal-vicariance analysis) (Ronquist, 1997).
These three techniques differ in their treatment of vicariance, dispersal and extinction, as well as in their treatment of widespread species, missing areas, redundant distributions and overlapping areas. Both forms of AA analysis invoke dispersal theories to explain ancestral distribution of organisms, and assume no vicariance. DIVA on the other hand invokes vicariance, dispersal and extinction scenarios to explain the ancestral distribution of organisms. In this study all three techniques were employed to reconstruct the biogeographic history of gibbons, since it was decided both dispersal and vicariance are likely to have affected gibbon biogeography.
Materials
The estimate of gibbon phytogeny proposed in the first chapter is used as the basis for biogeographic reconstruction. This phytogeny was used in preference to other, published, phytogenies since previous studies have provided conflicting results regarding phylogenetic inter-relationships among gibbon subgenera. Since DIVA
(Ronquist, 1997) requires a fully dichotomous tree, the tree derived from maximum likelihood analysis of cytochrome b gene assuming a molecular clock (Figure 2,3) has been used in each of the three biogeographic analyses, to maintain continuity.
Next, area characters must be chosen and an area cladogram depicting area relationships must be constructed. There are no set criteria for choosing area characters, except that DIVA operates on a maximum of 15 area characters. Hence, this was a
240 limiting factor in the number of characters that could be selected, since the same area characters are used for each analysis to maintain continuity. Geissmann’s (1995) breakdown of the geographic distribution of gibbon species was used as the basis for determining area characters. These are based mainly on political boundaries of countries, and in order to keep the number of area characters to less than 15, subdivisions of areas within a country were removed for each species (e.g. for hoolock which is distributed in west Yunnan, the area character selected is Yunnan [a province in South China]). The area characters selected using these criteria and used in the following analyses are shown in Table 4.1, and represented in the taxon area cladogram depicted in Figure 4.1.
241 Table 4.1 Area characters of gibbon species used in this analysis
Subgenus Species Area characters Hylobates lar Thailand, Burma, Malay peninsula, Sumatra, Yunnan pileatus Thailand, Cambodia agilis Sumatra, Borneo, Malay peninsula, Sumatra moloch Java muelleri Borneo klossii Mentawai Islands Bunopithecus hoolock Assam, Bangladesh, Burma, Yunnan Nomascus concolor Laos, Vietnam, Yunnan, Hainan Island leucogenys Laos, Vietnam, Yunnan gabriellae Laos, Vietnam, Cambodia Symphalangus syndactylus Malay peninsula, Sumatra
242 outgroup
Vietnam, Laos, Yunnan
Vietnam, Laos, Yunnan, Hainan Is.
Cambodia, Laos, Vietnam
Malay Peninsula, Sumatra
Burma, Assam, Bangladesh, Yunnan
Java
Malay Peninsula, Sumatra, Borneo
Thailand, Burma, Malay Peninsula, Sumatra, Yunnan
Borneo
Mentawai Islands
Thailand, Cambodia
Figure 4.1 Taxon area cladogram generated using the gibbon phylogeny estimated using cytochrome b gene sequence data analysed under maximum likelihood assuming a molecular clock (Figure 2.3).
243 4.2.1 Ancestral area analysis - using irreversible parsimony.
4.2.1.1 Introduction to the method
Bremer (1992) proposed a method of biogeographic analysis based on the distribution of area characters plotted on a fully bifurcated tree. His method, termed ancestral area analysis (AA analysis), reconstructs the ancestral distribution of a group of organisms assuming dispersal into areas, but no vicariance. AA analysis offers a cladistic reinterpretation of the centre of origin concept. The concept of centres of origin is fundamental to dispersal biogeography, as discussed in previous sections
(4.1.1). For many years the notion of centre of origin has fallen into disrepute since it was believed to have been founded upon spurious criteria. Bremer (1992, 1995) resurrected such a notion, suggesting that it is plausible to assume that if a group originally had a more restricted ancestral distribution than it has today, then a method should be devised to estimate the likelihood that any of the areas where a group is found today belonged to the ancestral range.
4.2.1.2 The method
Bremer’s AA analysis addresses directly two main assumptions of historical
biogeography: [ 1 ] areas that are positionally more plesiomorphic (i.e. present on “deep” branches) in a cladogram of a particular group, are more likely to be part of the
ancestral area of that group, than are positionally more apomorphic areas, and [ 2 ] areas represented on numerous branches of the cladogram are more likely to be part of the ancestral area, than areas represented on fewer branches.
In the construction of an area cladogram, each area is treated as a single character, which may be optimised onto the cladogram using Camin-Sokal parsimony.
Camin-Sokal optimization (Camin and Sokal, 1965) constrains character transformations such that once a state has been acquired it may never be lost. Each area
244 is coded as a simple binary character (presence or absence) for all the taxa on the cladogram. It is possible to estimate which areas were most likely to be part of the ancestral area by comparing the numbers of necessary gains and losses of an area cladogram, under two complementary optimisations. Under the all loss-no gain model, the ancestral state for a given area character is present and is plotted as a gain on the cladogram, whereas the absence of the same area is considered to be a loss (i.e. it is regarded as the derived state). It is assumed that the area examined belonged to the ancestral range of the group. In the complementary approach, it is assumed that the ancestral range is ‘empty’, that is, it does not contain the area in question (i.e. absence of an area is regarded as the primitive state). All area occurrences, therefore, are considered to be ‘derived’, i.e. they represent gains. This is shown in the simple example depicted in Figure 4.2. A clade comprising sister taxa occupying the same area is counted as only 1 gain or loss. No assumptions about the environmental and/or evolutionary processes involved in area losses (e.g. extinction) are made.
AA analysis uses the ratio between gains (G) and losses (L) of individual areas to measure the probability that each of the areas was part of the ancestral range. The
G/L ratios are rescaled to a maximum value of 1 by dividing them by the largest G/L value, this providing an AA (ancestral area) value. According to Bremer (1992, 1995) those areas with the highest AA value are more likely to be part of the ancestral area.
Gains, losses and predicted ancestral areas were calculated for the taxon area cladogram depicted in Figure 4.1. Results of this analysis are shown in Table 4.2.
245 A
A
A
B
B
B
Area G L G/LAA
A 3 2 1.5 1
B 2 3 0 . 6 6 0.44
Figure 4.2 A simple example of ancestral area analysis using irreversible parsimony
(Bremer, 1992). A and B = area distributions. G = number of necessary gains under forward Camin-Sokal Parsimony. L = number of necessary losses under reverse Camin-
Sokal Parsimony. AA = G/L quotients rescaled to a maximum value of 1 by dividing them by the largest G/L value.
N.B. A clade comprising sister taxa occupying the same area distribution (e.g. area B,
above) is counted as only 1 gain/loss.
246 Table 4.2 Ancestral area analysis using irreversible parsimony (Bremer, 1992) applied
to the taxon area cladogram depicted in Figure 4.1.
Area G LG/L AA
Thailand 2 7 0.29 0.29
Burma 2 6 0.33 0.33 Malay Peninsula 3 5 0.60 0.60
Cambodia 2 8 0.25 0.25 Sumatra 3 5 0.60 0.60
Java 1 4 0.25 0.25
Borneo 2 6 0.33 0.33
Vietnam 1 1 1 1
Laos 1 1 1 1
Yunnan 4 6 0 . 6 6 0 . 6 6
Hainan Island 1 3 0.33 0.33
Mentawai Islands 1 7 0.14 0.14
Assam 1 3 0.33 0.33
Bangladesh 1 3 0.33 0.33
4.2.1.3 Results
Ancestral area analysis using irreversible parsimony suggests that Vietnam and
Laos are most likely part of the ancestral range of gibbons. Yunnan is the next most likely ancestral area. The Mentawai Islands, followed by Java and Cambodia represent the least likely ancestral areas, according to this analysis. AA analysis only allows statements regarding the most likely, and least likely ancestral areas at the basal node of the tree. It cannot provide information regarding ancestral areas at other, more distal nodes, within the tree. Hence, it is not possible to deduce a pattern of radiation for the taxa represented on the tree.
247 4.2.2 Ancestral Area Analysis - using reversible parsimony
4.2.2.1 Introduction to the method
Ronquist (1994, 1995) has criticised Bremer’s AA analysis, stating that it emphasises the irreversibility of gains and losses of geographical areas, hence it can only be justified by irreversible biogeographical processes occurring during the evolutionary history of a group. Ronquist suggests that a more appropriate technique for reconstructing ancestral distribution areas under the constraint of no vicariance would be to use standard parsimony optimisation allowing reversible change, using Fitch optimisation. Fitch optimization (Fitch, 1971) allows free reversibility, but for multistate characters there is equal cost, measured as steps, in transforming any one state into another.
4.2.2.2 The method
In AA analysis using reversible parsimony (Ronquist, 1994, 1995) the likely ancestral distribution at the basal node on a tree is the one which requires the minimum number of steps (S) given the distribution of area characters at the terminal branches of the tree. Hence, area characters are optimised under Fitch (or reversible) parsimony.
The number of steps (S) is calculated for each area character, and each S value is subsequently inverted, by dividing by 1 (e.g. if S = 3, the inverted value = 0.33) and rescaled to 1 by multiplying them by the smallest S value (Table 4.3).
The number of necessary steps was calculated in MacClade Version 3.0.1
(Maddison and Maddison, 1992). An area/taxa dataset was built in MacClade and presence/absence data for the 15 area characters and 11 gibbon species were entered. A tree with the same topology as the cytochrome b gene tree constructed under the assumption of a molecular clock was also built in MacClade. The minimum number of steps (S) required to optimise a certain area character on the tree was computed using
248 the TRACE CHARACTER command in MacClade. Table 4.3 shows the S values and predicted ancestral area values (RP) which are the inverted S values rescaled to 1. As with Bremer’s (1992) method, the area with the highest RP value represents the most likely ancestral distribution of the group, whereas the area with the lowest RP value represents the least likely ancestral distribution. AA analysis using reversible parsimony, like AA analysis using irreversible parsimony, can only provide information regarding the basal node of the tree.
249 Table 4.3 Ancestral area analysis using reversible parsimony (Ronquist, 1994) applied to the taxon area cladogram depicted in Figure 4.1. S = number of necessary steps if the area were the ancestral area. RP = S values rescaled to a maximum value of 1 by inverting them and multiplying them by the smallest S value.
Area SRP
Thailand 2 0.50
Burma 2 0.50 Malay Peninsula 3 0.33
Cambodia 2 0.50 Sumatra 3 0.33
Java 1 1
Borneo 2 0.50
Vietnam 1 1
Laos 1 1 Yunnan 4 0.25
Hainan Island 1 1
Mentawai Islands 1 1
Assam 1 1
Bangladesh 1 1
4.2.2.3 Results
Results of the AA analysis using reversible parsimony indicates several equally likely ancestral areas at the basal node of the tree: Java, Vietnam, Laos, Hainan Island,
Mentawai Island, Assam and Bangladesh. The least likely ancestral area according to this analysis is Yunnan, followed by Sumatra and Malay Peninsula. This is in partial contrast to the results of the analysis using Bremer’s (1992) method, which indicated that Yunnan was the second most likely ancestral area. However, the results of the
Ronquist (1994) method agree with those of the Bremer (1992) method in the recognition of Vietnam and Laos as being part of the ancestral range of gibbons.
250 As evident from the large number of equally likely ancestral areas, AA analysis using reversible parsimony is less decisive in determining ancestral area distributions than Bremer’s (1992) method. This is because AA analysis using reversible parsimony doesn’t have the power to discriminate among areas, since only the minimum number of steps accounting for a certain area distribution is taken into account. Thus, regardless of the number of branches on which an area occurs, the number of steps may be the same.
251 4.2.3 Dispersal-Vicariance Analysis
4.2.3.1 Introduction to the method
Ronquist (1997) proposed a method of biogeographic analysis which allows for the inclusion of vicariance, dispersal and extinction events in the evolutionary history of a group. This method is known as dispersal-vicariance analysis (DIVA). DIVA is a quantitative method which aims to reconstruct the biogeographic history of a group in the absence of any information concerning nested patterns of area relationships.
Ronquist (1997) was concerned about the assumption of cladistic biogeography that there is a single branching pattern among areas of endemism. He proposed that dispersal barriers may appear and disappear throughout the evolutionary history of a group, thus affecting the distribution of different taxa. This could result in the biota of an area consisting of several components with separate histories. Ronquist’s (1997) method makes use o f‘character optimisation’, which allows multiple and reticulate relationships among areas.
Another confounding feature of the vicariance scenario is that it implies that ancestral species were generally more widespread than their descendants. According to
Ronquist this paradox can be avoided by allowing successive dispersal events that counteract the decrease in distribution range caused by vicariance. In this case some dispersal is required to explain the occurrence of widespread ancestors.
252 4.2.3.2 The Method
DIVA is a method for reconstructing the distributional history of a group of organisms from the distribution areas of extant species and their phytogeny, implemented using the computer program DIVA version 1.1 (Ronquist, 1996). The method operates on several assumptions. Firstly, spéciation is assumed to be caused by vicariance (i.e. by the division of the ancestral species into two component parts). This model has two possibilities. First, the ancestor occurs in a single unit area immediately prior to spéciation, which it is assumed is caused by allopatric spéciation (possibly coupled with small-scale dispersal). Immediately after the spéciation event, both descendant lineages occur in the same area as their ancestor (Figure 4.3a). The cost of this event is zero. Alternatively, the ancestor occurs in more than one unit area. In this case spéciation leads to subdivision of the ancestral distribution into two mutually exclusive sets of areas (Figure 4.3b). The cost of this event is zero.
A
(a) " (b)
Figure 4.3 Expected biogeographic outcomes of spéciation under the DIVA hypothesis.
Letters refer to areas, (a) When the ancestral species occurs in a single area, the daughter species will be expected to occur in the same area, (b) When the ancestral species occurs in several areas, the daughter species will be expected to occur in mutually exclusive sets of areas (Ronquist, 1997).
253 A second rule is that dispersal is the addition of one or more unit areas to the distribution, and carries the cost of 1 per added area. Thirdly, extinction is the deletion
of one or more unit areas from a distribution, and carries a cost of 1 per area deleted.
Finally, all species must occur in at least one unit area.
The optimal reconstruction of ancestral distributions is sought by determining the reconstruction with the minimum dispersal-extinction costs. This is achieved through the parsimony criterion. DIVA uses a three-dimensional step matrix, in which the cost of a particular event depends on the combination of descendant distributions.
Having defined the cost matrix, the reconstruction of the ancestral distributions is computed in a similar way to an ordinary step-matrix optimisation.
The first step in this method is to construct an area cladogram, by assigning the observed distributions of the terminal taxa to the terminal nodes of the cladogram. All the internal nodes are then assigned an array of all the possible area distributions, in other words, all the possible combinations of unit areas. For example, if the terminal taxa occur in unit areas A, B and C, all the internal nodes are assigned the range of all possible distributions, i.e.: {A}, {B}, {C}; {A,B,C}; {A,B}; {A,C}; {B,C}. The range of distributions at each ancestral node is then assigned costs using a generalised parsimony algorithm. This optimisation procedure consists of three steps: a downpass, an uppass, and a fmalpass.
A downpass involves the following: proceeding down from the terminal branches, each internal node is assigned an array, specifying for each possible state at the node, the minimum number of steps (cost) required in the section of the tree above the node. Next an uppass is performed: proceeding up from the root, each node is assigned an array, specifying for each possible state at the node, the minimum number of steps (cost) required in the section of the tree below and beside the node. The final array (= fmalpass) of a node is obtained by combining the downpass array of the node
254 with the uppass array of its ancestral node (Ronquist, 1995). Following the final pass,
the optimal distribution at each node is the distribution associated with the minimum
overall dispersal-extinction cost.
DIVA requires a fully dichotomous tree and less than 180 taxa and 15 area
characters. For this reason only the maximum likelihood cytochrome b tree constructed
under the assumption of a molecular clock could be employed, since it is the only tree
which is completely resolved. A data matrix comprising a total of 15 binary area
characters was constructed in MacClade Version 3.0.1 (Maddison and Maddison, 1992)
(Table 4.4). For each gibbon species the different area characters were entered as either
absent (0) or present (1), as in the Bremer (1992) and Ronquist (1994) methods.
The maximum number of areas included in the optimisation must be set before
running DIVA. The ancestral distribution was constrained to include a maximum of two
areas. Increasing the number of maximum areas provided results that were unintelligible, since optimisations were represented by innumerable combinations of
ancestral areas. Constraining the maximum number of areas does not mean that any
more weight is applied to certain areas. All of the areas are given equal weighting;
however, in the output under the constraint of two, the two most likely ancestral areas
are determined from the whole range of areas using the algorithm described previously.
According to the DIVA 1.1 users’ manual (Ronquist, 1996) two further settings
must also be made: the upper bound of the length of the optimal area distribution (the
default setting is 250), and the maximum number of alternative reconstructions kept at
each node (the default setting is 32767).
In the DIVA output, area characters are defined by letters. Reconstructed area
distributions are displayed at the internal nodes (Figure 4.4) as single letters, or
255 combinations of letters, since a node can show several optimal distributions of area characters. These are separated by commas on the DIVA tree.
256 Table 4.4 Data matrix containing presence(l)/absence(0) information for the 14 area distributions and 11 gibbon species.
Area leucogenys concolor gabriellae syndactylus hoolock moloch agilis lar muelleri klossii pileatus
Thailand 0 0 0 0 0 0 0 1 0 0 1
Burma 0 0 0 0 1 0 0 1 0 0 0
Malay Peninsula 0 0 0 1 0 0 1 1 0 0 0
Cambodia 0 0 1 0 0 0 0 0 0 0 1
Sumatra 0 0 0 1 0 0 1 1 0 0 0
Java 0 0 0 0 0 1 0 0 0 0 0
Borneo 0 0 0 0 0 0 1 0 1 0 0
K> Vietnam 1 1 1 0 0 0 0 0 0 0 0 Yunnan 1 1 0 0 1 0 0 1 0 0 0 Hainan Island 0 1 0 0 0 0 0 0 0 0 0 Mentawai Islands 0 0 0 0 0 0 0 0 0 1 0 Assam 0 0 0 0 1 0 0 0 0 0 0 Bangladesh 0 0 0 0 1 0 0 0 0 0 0 human . leucogenys (Nomascus) r— concolor (Nomascus) — gabriellae (Nomascus) 0.3 [D, I,H] CH,EH, Cl, El 10.5 syndactylus (Symphalangus) [C,E] hoolock (Bunopithecus) C,E (B, M, N,J1 8.6 moloch (Hylobates) BC, BE, CJ, EJ, CM, EM, CN, EN 7.7 agilis (Hylobates) IC,E,G] CF,EF lar (Hylobates) [ A, B, C, E, J ] C, AC,E,AE AG, CG, EG 4.8 muelleri (Hylobates) A, AC, CD, AE, DE, CL, EL klossii (Hylobates) 4.6 AL, DL pileatus (Hylobates) 3.7 IA,D] Figure 4.4 DIVA optimisation of character areas. A = Thailand F = Java K = Hainan Island B = Burma G = Borneo L = Mentawai Islands C = Malay Peninsula H = Vietnam M = Assam D = Cambodia I = Laos N = Bangladesh E = Sumatra J = Yunnan Subgenera and present day distributions are indicated in brackets. Divergence dates (Ma) estimated in Chapter 2 (Figure 2.3, Table 2.4) are superimposed at the internal nodes. 258 4.2.3.3 Results The optimal area distribution in Figure 4.4 requires 23 dispersal events. For the primary radiation of gibbons, involving the separation of the taxa in subgenus Nomascus from the rest of the gibbons, DIVA favours several optimal ancestral areas: [Malay Peninsula and Vietnam] or [Sumatra and Vietnam] or [Malay Peninsula and Laos] or [Sumatra and Laos]. Thus, several areas are not part of the ancestral area of all gibbons, including: Thailand, Burma, Cambodia, Java, Borneo, Yunnan, Hainan Island, Mentawai Islands, Assam and Bangladesh, (see Figure 1.1 for a map of SE Asia). These results suggest that the ancestral distribution for the Nomascus clade included Vietnam and Laos. This indicates that Cambodia, Yunnan and Hainan Island were not part of the ancestral distribution of Nomascus. Regarding Symphalangus, both the Malay Peninsula and Sumatra appear to have been part of this group’s ancestral range. For the next most distal node involving the separation of the subgenera Bunopithecus and Hylobates, DIVA provides numerous optimisations. However, it is possible to infer from these results which areas are derived. For example, according to DIVA within the subgenus Hylobates, Borneo was not part of the ancestral range of H. agilis. However, Borneo was part of the ancestral range o f H. muelleri according to these results. Furthermore, this analysis indicates that Burma and Yunnan were not part of the ancestral range of H. lar. Ronquist (1996) suggests that the optimisations become less reliable as you approach the root node because any one tree only represents a small part of the tree of life. Hence, optimisation of characters at the basal node of a cladogram (representing only a subtree) is affected by the remaining parts of such a tree. This has the effect of accumulating uncertainty as you reach the basal node, since more areas are accumulated 259 at each node from the more distal to the most basal node. Therefore it is not surprising to find that many areas are plotted together. 260 4.3 Discussion A A analysis and DIVA are useful methods of biogeographic reconstruction especially for groups of organisms which have a controversial or limited palaeontological and palaeoenvironmental record. It is important to note, however, that one of the limitations of AA analysis compared to DIVA is that it can only provide information regarding ancestral area distributions at the basal node of a tree, and cannot provide information for more distal nodes. Hence, it is not feasible to only use AA analysis to reconstruct the pattern of radiation of a group of organisms. Despite this, reconstructing ancestral distributions at the basal node of the tree is important, especially in light of the lack of resolution provided at the basal node of the DIVA reconstruction. In this sense the two types of reconstruction, DIVA and AA analysis, are complementary. As shown above, AA analysis using irreversible parsimony (Bremer, 1992) is more informative regarding ancestral area distributions than AA analysis using reversible parsimony (Ronquist, 1994). There are advantages and disadvantages to both methods. One disadvantage of Bremer’s AA analysis (1992) is that it operates on the assumption of irreversibility, and that the ancestral distribution of a taxa was more limited historically compared to the present day. Ronquist’s (1994, 1995) method of AA analysis makes no assumptions regarding irreversibility of areas. Ronquist (1995) states that there is no need to impose the assumption that areas were more limited in the past compared to the present, unless there is sufficient palaeontological evidence to do so. One disadvantage of not applying the constraint of irreversibility, however, is the lack of resolution in determining ancestral areas. Hence, the results of the AA analysis using reversible parsimony were unable to discriminate among likely ancestral areas. 261 DIVA also has advantages and disadvantages. The main advantage over both forms of AA analysis is that ancestral distributions are provided for all of the nodes on the tree, allowing inferences to be made regarding the possible pattern of radiation. One disadvantage is the lack of resolution provided at the base of the tree. The aim of this analysis is to propose a new scenario for the radiation of gibbons on the basis of the results presented here. Since DIVA and AA analysis using reversible parsimony were relatively uninformative regarding the ancestral distribution at the base of the tree, several assumptions have to be invoked regarding the most likely centre(s) of radiation. Similarly, some of the more distal nodes on the DIVA tree show numerous optimisations. Hence, further assumptions have been made regarding the occurrence of areas. The results of the AA analysis and DIVA have been employed, along with the estimates of divergence time generated under the assumption of a molecular clock, to propose a new pattern of radiation for the gibbons. In the following paragraphs a step by step discussion of the likely pattern of radiation according to these results is presented, along with a discussion of any necessary assumptions which have to be made. 262 Proposed pattern and timing of the gibbon radiation At the basal-most node of the gibbon phytogeny, dated at 10.5 Ma, both forms of AA analysis, plus the DIVA results, predict that Vietnam and Laos were part of the ancestral range of gibbons. However, AA analysis using reversible parsimony and DIVA also predict several other equally likely ancestral areas for the basal node of the tree. Hence, it is unclear from these analyses which of the area characters included is most likely part of the ancestral range at the basal node of the tree. Other sources of evidence must therefore be invoked to make statements regarding the most likely centre of radiation of gibbons. While the fossil record for gibbons is sparse (as discussed in Chapter 1) some deductions can be made regarding their possible ancestral distribution. If the earliest appearance of gibbons (or a gibbon ancestor) in the form of Laccopithecus fossils from the late Miocene Lufeng deposits of Yunnan is accepted, then it could be assumed that the ancestral area for gibbons is Yunnan. Several studies have indicated that these fossils represent an ancestral gibbon (Wu and Pan, 1984, 1985; Tyler, 1993; Jablonski, 1993a). The main problem with this assumption is that that neither DIVA nor AA analysis using reversible parsimony identified Yunnan as the likely ancestral area for gibbons. With regard to the DIVA result, the non-recognition of Yunnan is partly due to the way in which the algorithms operate and partly due to constraining the maximum allowable areas to two. As discussed above, the algorithm operates in such away that resolution is lost as more and more areas are assigned as you reach the more basal node of the tree. Regarding the constraint of the maximum allowable areas, when this was increased to include three areas, Yunnan was identified as a likely ancestral area at the basal node. 263 There are at least three possibilities regarding the ancestral distribution of gibbons at the basal node of the tree: [1] The ancestral range for all gibbons included Vietnam and Laos. However, there is no fossil evidence to substantiate this. [2] If the fossil evidence for Laccopithecus is accepted, it could be proposed that Yunnan was part of the ancestral range of gibbons. [3] Yunnan, Vietnam and Laos represent the ancestral areas for gibbons since these three areas are contiguous. It is proposed, that until further fossil evidence becomes available, the region incorporating Yunnan, Vietnam and Laos is taken as a working hypothesis to represent the most likely ancestral area at the basal node of the tree. This region is from here onwards referred to as Eastern Indochina. The next divergence of gibbons occurred approximately 8 . 6 Ma and involved the separation of syndactylus from the other gibbons. According to the DIVA results (results of the AA analysis can not provide any further information regarding more distal nodes) the predicted ancestral areas at this node include the Malay Peninsula and Sumatra. It is proposed, therefore, sometime between 10.5 and 8 . 6 Ma gibbons radiated southwards from mainland SB Asia, via the Malay Peninsula to Sumatra. There is no fossil evidence from the Malay Peninsula or Sumatra dated to the late Miocene, although Hall (1998) has shown that there was emergent land connecting mainland SB Asia and Sumatra throughout the middle and late Miocene. Furthermore, there is evidence from carbonate platform sequences derived from these areas that emergent land during the end of the Miocene was covered by tropical rainforests (Morley and Flenley, 1987). Hence, it is feasible that syndactylus could have evolved on mainland SB Asia and spread to Sumatra during the Miocene. The present-day occurrence of more than one species on Sumatra indicates that subsequently members of the subgenus 264 Hylobates spread to Sumatra. According to the estimates of divergence times, this spéciation probably occurred between 4.8 and 3.1 Ma. The next reconstruction relates to the differentiation of hoolock. While the DIVA results are fairly uninformative regarding this node, it is possible to state that the results indicate that at the latest, approximately 7.7 Ma, hoolock radiated into Burma, Assam, and Bangladesh. Again there is no fossil evidence to corroborate this hypothesis, but Hall (1998) has suggested that there was emergent land in these parts of SE Asia during the late Miocene and early Pliocene. Furthermore, there is evidence to suggest that these areas were covered by tropical rainforest during this period which could have supported primates (Jablonski, 1993a; Heaney, 1991; Morley and Flenley, 1987). The penultimate stage in this reconstruction involves the differentiation of species in the subgenus Hylobates. From the results presented here, and on the basis of palaeoenviromental evidence, it is possible to suggest that between 4.8 and 3.1 Ma taxa in the subgenus Hylobates spread to the islands of Sumatra, Borneo, Java and . Mentawai. It is not possible to deduce the exact pattern of radiation of these taxa from the available evidence; however, there is evidence to suggest that emergent land connected the islands of Sumatra, Borneo and Java with mainland SE Asia, throughout the early Pliocene (Hall, 1998). Again, palaeoenvironmental records suggest these areas were likely covered by tropical rainforest (Jablonski, 1993a; Heaney, 1991; Morley and Flenley, 1987). According to this reconstruction the Mentawai Islands were not connected to Sumatra between the time period 15 to 5 Ma. This implies that gibbons radiated to these islands sometime between the end of the Pliocene and the Pleistocene. 265 The most recent radiation according to the results of these analyses involved species in subgenus Nomascus. DIVA predicts that the most likely ancestral areas for these species included Vietnam and Laos. If the assumption that the ancestral area for all gibbons included Vietnam, Laos and Yunnan is correct, the combined DIVA results and divergence date estimates indicate that sometime between 1.8 and 0.3 Ma concolor radiated onto Hainan Island off south China, and gabriellae spread into Cambodia. 266 Summary of the proposed pattern and timing of the gibbon radiation Figure 4.5 Proposed pattern of gibbon radiation: 1. The gibbon radiation initiated approximately 10.5 Ma in Eastern Indochina. 2. Between about 10.5 and 8 . 6 Ma gibbons radiated southwards to the Malay Peninsula and Sumatra. Subsequently, they differentiated into two types of gibbon on Sumatra, representing the subgenera Symphalangus and Hylobates. 3. Approximately 7-8 Ma Bunopithecus spread into Burma, Assam, and Bangladesh. 4. At around 3-5 Ma there was a second radiation of subgenus Hylobates, involving dispersal into the islands of Borneo, Mentawai and Java. 5. Between 0.3 and 1.8 Ma taxa in the subgenus Nomascus differentiated into Cambodia and Hainan Island. 267 4.4 Conclusions Results of the biogeographic reconstruction analyses, DIVA and AA analysis, have been combined with estimates of divergence dates, to produce a hypothesis of the pattern and timing of the gibbon radiation. Previous studies regarding the pattern and timing of the gibbon radiation are not congruent with recent advances in molecular estimates for the timing and nature of gibbon phylogenetics (Chivers, 1977; Groves, 1972). These studies suggest that the gibbon radiation dates to the Pleistocene. Data presented here and elsewhere (Zehr et a l, 1996; Porter et al., 1997) suggest that the gibbon radiation dates to between 6 and 10 Ma, based on combined molecular evidence. However, the accuracy of the reconstruction presented here is dependant on several factors: how closely the gene tree matches the true-species tree, the reliability of the estimates of divergence times, and the robustness of the biogeographic reconstruction techniques used. At the very least, however, the data analysed here have provided a novel reconstruction of the biogeographic history of gibbons, which represents a testable hypothesis. As more information becomes available regarding the palaeoenvironment of SE Asia, including climate and the extent of exposed land, plus evidence from the fossil record, this hypothesis can be developed and refined. 268 Chapter 5: Final discussion, conclusions and future research. Contents Page numbers 5.1 Final discussion 270 5.1.1 Molecular approach to gibbon phytogeny 271 5.1.2 Morphological study 274 5.1.3 Gibbon biogeography 277 5.2 Concluding remarks 282 5.3 Future research 284 269 5.1 Final discussion This thesis has aimed to address the question of gibbon phylogenetic relationships and biogeographic history. A number of methodological approaches have been applied to molecular, morphological and distribution data to reconstruct gibbon phytogeny and biogeography. This study may be viewed in a wider context, since the techniques employed here are broadly applicable to a variety of questions relating to phytogeny, spéciation, biogeography, congruence/conflict between morphological and molecular data, for other mammalian groups. The methods applied here have been rigorously tested, and their usefulness with regard to the reconstruction of gibbon phytogeny and biogeography, fully assessed. Maximum likelihood and parsimony methods have been applied to molecular and morphological data to investigate phylogenetic interrelationships among gibbons, and to explore the congruence between these two data-sets. These analyses have generated a new estimate of gibbon phytogeny. Furthermore, this is one of the first studies within the Class Mammalia to employ novel biogeographic reconstruction techniques to investigate patterns of distribution and to provide an estimate of the pattern of radiation. 270 5.1.1 Molecular approach to gibbon phylogeny The mitochondrial control region has been identified by several studies as a putatively informative region for investigating phylogenetic relationships at the species level (Brown et al., 1986; Garza and Woodruff, 1992; Zischler et a l, 1998; Gemmell et al., 1996). Hence, this region was chosen for study in this analysis. Many studies have focused on the cytochrome b gene for investigating phylogenetic relationships, and the gibbons are no exception (Garza and Woodruff, 1992; Hall et al., 1996, 1998). However, these two studies differ regarding their interpretation of the results of the variability in cytochrome b gene among gibbons. Garza and Woodruff (1992) suggest, that although different types of analyses (e.g. maximum likelihood and parsimony) provided somewhat conflicting results, they were able to resolve some relationships among gibbon subgenera. Hall et al. (1996, 1998) on the other hand, determined that cytochrome b gene was unable to provide any resolution between gibbon subgenera. It is proposed that this conflict is due to the fact that sequence data from only seven and six species, respectively, were included in these analyses. However, cytochrome b gene sequence data for all eleven currently recognised species of gibbon are now available via the world wide web. Thus, in light of the above conflict these data were re^analysed, to investigate phylogenetic relationships among gibbons. Control region sequence data and cytochrome b gene sequence data were analysed separately using maximum likelihood, parsimony and bootstrapping methods. Phylogenetic analysis of the control region sequences data provided results that were incongruent with most previous studies regarding the monophyly of the subgenus Hylobates (e.g. Chivers, 1977; Haimoff et al., 1982; Cronin et al., 1984; Marshall and Sugardjito, 1986; Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et al., 1995; Hall et al., 1996, 1998). Furthermore, control region trees were weakly supported 111 and different trees generated using different types of phylogenetic reconstruction (parsimony and likelihood) produced incongruent results. Several explanations may account for the incongruence of the control region trees: [ 1 ] systematic sequencing errors have occurred; [ 2 ] unusual evolutionary processes have occurred in the control region of gibbons; [3] these results are due to the effects of saturation; [4] nuclear inserts of the control region have been amplified; [5] subgenus Hylobates is not monophyletic and all other indicators are wrong. Each of these hypotheses were explored, and results indicate that saturation provides the most likely explanation of the anomalous grouping of certain gibbons. It is proposed that the control region evolves too rapidly to detect spéciation events within the gibbons. Phylogenetic analysis of cytochrome b gene produced trees that were well resolved and congruent both in terms of the types of analysis used (i.e. Maximum likelihood and parsimony) and regarding gibbon subgenera monophyly. The cytochrome b tree certainly supports gibbon subgenera monophyly and these analyses indicate a gibbon phylogeny which shows the following subgeneric relationships: Nomascus, Symphalangus and Bunopithecus are successively more closely related to Hylobates. However, the less than 70% boostrap values for some subgeneric groupings indicates that cytochrome b gene is not completely able to resolve relationships among the four subgenera. Cytochrome b gene was unable to resolve relationships among gibbons at the species level. Most trees showed polytomies within subgenera. On the basis of these results it is proposed that cytochrome b gene is useful for phylogenies which are several millions of years old, however, it is less informative for regions which have evolved more rapidly (e.g. less than about 1 million years). 272 Cytochrome b gene data were also analysed under the assumption of a molecular clock to provide estimates of divergence dates at the various nodes on the gibbon phylogeny. A 15 Ma calibration was used in these analyses, based on the combined results of numerous different studies (Tyler, 1993 and references therein). These estimates indicate that the gibbon radiation dates to 10.5 Ma, and involved the divergence of subgenus Nomascus from the other gibbons. At around 8 . 6 Ma the next divergence involved subgenus Symphalangus {syndactylus) from the subgenera Bunopithecus {hoolock) and Hylobates {lar^ agilis, moloch, muelleri, pileatus and klossii). The divergence of the subgenera Bunopithecus and Hylobates, occurred at around 7.7 Ma according to these estimations. The species within subgenus Hylobates diverged from each other between about 3 and 5 Ma, while the species in subgenus Nomascus represent the most recent species to divergence, between 0.3 and 1.8 Ma. These estimates of divergence are in general agreement with other estimates based on different genetic data. These previous molecular estimates suggest that the gibbon radiation dates to between about 6 and 10 Ma (Zehr et al., 1996; Porter et al., 1997). These results are in contrast to earlier studies largely based on morphological and behavioural data, which suggest that the gibbon radiation dates to the Pleistocene (i.e. not later than about 2.5 Ma) (e.g. Chivers, 1977). It should be borne in mind that the older phylogeny proposed here (and elsewhere) is based on a gene tree and that the nodes of a species tree may be somewhat younger; however, it is unlikely that this could account for such a large difference. Despite this, the results of this analysis compared to the results of previous analyses might not be entirely mutually exclusive. For example, the divergence dates estimated in the present study for the various subgenera may date to the late Miocene; however, the actual spéciation events which have occurred within certain subgenera, Nomascus and Hylobates, may have occurred 273 in the Pleistocene. Indeed, the estimates of divergence time for these subgenera indicate a Plio-Pleistocene spéciation. 5.1.2 Morphological study Previous morphological studies have used skeletal, pelage and other communicatory indices such as olfactory data (e.g. skin glands) to assess variability and phylogenetic relationships among gibbons (Groves, 1972; Creel and Preuschoft, 1976, 1984; Haimoff et al., 1982; Geissmann, 1993). However, these studies have either provided conflicting results regarding gibbon phylogenetics, or been unable to resolve interrelationships among gibbons. Since several previous studies have concentrated on pelage and behavioural differences among gibbons, these data were not included in this morphological analysis (Chivers, 1977; Haimoff et a l, 1982; Marshall and Sugardjito, 1986; Geissmann, 1993). A detailed assessment of morphometric variability across the gibbon skeleton has not previously been included in phylogenetic analyses. Groves (1972) analysed a small number of craniodental and postcranial variables (twelve in total), but this study only includes the six species which he recognised at that time: concolor, syndactylus, hoolock, klossii, pileatus and lar. Creel and Preuschoft (1976, 1984) analysed detailed morphological variability across the gibbon cranium and concluded that five morphological groupings could be identified: (1) syndactylus {Symphalangus), (2) concolor {Nomascus), (3) lar {Hylobates), (4) klossii {Hylobates) and (5) hoolock {Bunopithecus). Haimoff et al. (1982) analyse a small number of cranial, dental and postcranial variables (18 in total) along with other characteristics such as behavioural, to assess phylogenetic relationships among gibbons. However, these studies have not incorporated detailed morphometric data from the whole of the gibbon skeleton, and in light of the newly described species of gibbon published since these earlier works, it 274 was decided that full statistical analysis of such data was warranted. Furthermore, detailed gibbon morphological data has not been widely incorporated into cladistic analyses. This has been achieved here, and the degree of correspondence between the morphological and molecular datasets has been assessed in light of the molecular estimate of gibbon phylogeny. In the first part of the morphological study seventy-five craniodental and postcranial metric measurements recorded on gibbon skeletons representing the eleven currently recognised species of gibbon were analysed using multivariate statistical techniques. Discriminant function analysis and principal components analysis of raw and size-corrected craniodental and postcranial data agree that the different species of gibbon form five distinct morphological groupings: (1) syndactylus {Symphalangus), (2) concolor (Nomascus), (3) lar (Hylobates), (4) klossii (Hylobates) and (5) hoolock (Bunopithecus). This is in agreement with Creel and Preuschoft (1976, 1984), however, this study has added to Creel and Preuschoft’s results in providing evidence from the postcranium. Multivariate statistical analyses have provided a suite of craniodental and postcranial differences which serve to distinguish the five groups, and which rnay be used to identify fossil and recent material. These interesting results shows that gibbon subgenera, distinguished on the basis of pelage, diploid number, size, etc., also have consistent shape differences in the cranium, dentition and postcranium. One implication of this may be that these results provide evidence to suggest that the differences which distinguish gibbon subgenera arose at the earliest stage of divergence, and that morphologically at least gibbons have remained relatively little changed since the subgenera separated. The above metric data were further employed in a cladistic analysis to test whether cladistic re-coding of metric data can be used in phylogenetic reconstruction. 275 The results of these cladistic analyses were also assessed in light of the molecular estimate of gibbon phylogeny. Parsimony and bootstrap analysis of re-coded craniodental and postcranial data produced numerous tree. Most of the trees were badly resolved, showing polytomies, inconsistent with each other, and weakly supported, with low bootstrap values. In addition, all of the trees, except for one, show the subgenera Nomascus and Hylobates to be para- or polyphyletic. This is in contrast to the molecular evidence presented here, and to most other studies which indicate that the gibbon subgenera are monophyletic (e.g. Chivers, 1977; Haimoff et al., 1982; Cronin et al., 1984; Marshall and Sugardjito, 1986; Geissmann, 1993, 1995; Garza and Woodruff, 1992; Hayashi et al., 1995; Hall et a l, 1996, 1998). In light of the above it is proposed that the trees generated using re-coded metric data do not accurately reflect phylogenetic relationships among gibbons. Since multivariate statistical methods distinguished gibbons in accordance with the widely accepted subgeneric groupings, it is proposed that the anomalous result of the cladistic analysis is due to the use of metric data. This conclusion is in line with other studies which have suggested that metric data is uninformative and/or unreliable for phylogenetic reconstruction (e.g. Collard and Wood, 2000). These results, though negative, are of value in the design of any future analogous studies. 276 5.1.3 Gibbon biogeography The biogeographic history of gibbons is an intriguing subject and has been relatively understudied compared to other aspects of gibbons, such as behaviour (e.g. Preuschoft et al., 1984). The biogeographic history of gibbons has remained little understood partly due to a poor fossil record. Previous studies have used hypotheses about gibbon phylogeny to make inferences about the possible patterns of radiation within the genus that have given rise to the present day distribution of gibbons (Chivers, 1977; Groves, 1972). This study has taken this approach one step further by incorporating a new estimate of gibbon phylogeny in a variety of biogeographic reconstruction techniques. These techniques use a simple algorithm to estimate the number of dispersal, vicariance and extinction events which may have occurred within a groups evolutionary history. To date, the application of such techniques within the primates to answer questions relating to biogeography is very limited, although their application to other taxa has proved fruitful in terms of generating hypotheses about biogeographic history (Humphries and Parenti, 1999, and references therein). In order to reconstruct the biogeographic history of gibbons, the cytochrome b gene estimate of gibbon phylogeny was combined with the estimates of divergence time and analysed using three forms of cladistic biogeography: AA (ancestral area) analysis using irreversible parsimony (Bremer, 1992, 1995), AA analysis using reversible parsimony (Ronquist, 1994, 1995) and DIVA (dispersal-vicariance analysis) (Ronquist, 1997). Results of these analyses were used to propose a new pattern of radiation for the gibbons, invoking various pieces of evidence from the palaeoenvironmental and palaeontological record. 277 The proposed scenario for the radiation of gibbon suggests a radiation which began in Indochina, as early as 10.5 Ma. After around 8 Ma gibbons from the subgenera Symphalangus and Bunopithecus, radiated southwards towards the Malay Peninsula and Sumatra, and westwards into Burma, Assam, Bangladesh, respectively. From approximately 5 Ma gibbons in the subgenus Hylobates spread to the islands of Borneo, Mentawai and Java. More recently gibbons in the subgenus Nomascus speciated into Cambodia and Hainan Island off south China. It is not possible to provide a more detailed pattern of gibbon radiation on the basis of the data presented here, since the exact time of spéciation of the various species is unknown. However, this study has suggested the geographic appearance of the various subgenera, using a novel technique. This provides a working hypothesis which may be built on and tested further, as details of interspecific evolution, and information regarding the palaeaoenvironment and fossil evidence comes to light. In many ways this reconstruction is compatible with Chivers (1977) regarding the general pattern of divergence among the subgenera. For example, in Chivers’ (1977) reconstruction, as in the scenario presented above, there is an early divergence of Nomascus in China, followed by Hylobates and Symphalangus heading southwards, and Bunopithecus spreading westwards. However, the dating suggested by Chivers (1977) is more recent than the estimates provided here. Regarding species origins it is difficult to infer the exact biogeographic history of the various species of gibbons as Chivers has done, in light of the early divergence dates provided by molecular data, the limited information available for patterns of interspecific evolution and regarding the lack of fossil evidence. Certainly gibbon spéciation may have occurred in the Pleistocene, involving dispersal around the mainland and to the islands of Sumatra, Borneo, Java and Mentawai. There is fossil evidence of gibbons on these islands between about 60,000 and 80,000 years ago. 278 Climatic fluctuations could have encouraged island hopping, as suggested in Chivers (1977). There is evidence to suggest, for example, that syndactylus reached Java before moloch, and subsequently returned to Sumatra (Van den Bergh et al., 1995). However, this does not mean that gibbons were definitely not present on these islands prior to approximately 80,000 years ago. This is especially so, in light of evidence from Morley and Flenley (1987), Heaney (1991) and Hall (1998), which suggests that much of late Miocene and Pliocene SE Asia was exposed and covered by tropical rainforest. The evidence presented here certainly suggests that the various subgenera (or more likely their ancestors) were dispersing across SE Asia earlier than previously thought. However, more recent spéciations for members of the subgenus Hylobates and Nomascus are indicated. Without crucial fossil evidence from the late Miocene and Pliocene, plus further evidence from the Pleistocene, precise estimates regarding the pattern of radiation during this time cannot be made. The lack of fossil evidence for gibbons from faunal assemblages dating to 2 Ma on Java, could be taken as evidence that gibbons had not yet arrived here (Van den Bergh et al., 1995). The same applies to the other islands of Borneo, Sumatra and Mentawai (De Vos, 1983). Furthermore, numerous other authors have suggested that Asian forest primates, like many other Asian animals, were speciating in the Pleistocene (e.g. Brandon-Jones, 1996; Van den Bergh, 1995; Jablonski, 1993a; De Vos, 1993; Heaney, 1991). Brandon-Jones (1996) invokes evidence regarding the distribution of primates, other vertebrates, invertebrates and plants to suggest that the disjunct geographic distributions observed in SE Asia are the cause of climate change during the Plio- Pleistocene, brought about by glaciations. While it is possible to make approximate inferences regarding lowered sea level, exposed land, and colonisation by plants (and some animals), on the basis of evidence from pollen and soil core data, the lack of 279 direct fossil evidence makes it difficult to make inferences about the time of appearance in particular geographic areas in SE Asia (Jablonski, 1993b, 1997). Furthermore, there is considerable controversy regarding the exact pattern, timing and effects of glacial activity in SE Asia (Ferguson, 1993). The episodic glacial activity of the Pleistocene, which brought about variations in sea-level, climate and vegetation cover, certainly appears a likely explanation for the disjunct distribution of gibbons in the subgenus Hylobates. However, this does not discount the evidence presented here and elsewhere that the initial radiations of gibbon subgenera (or their ancestors) may date to the late Miocene and Pliocene (Jablonski, 1993a). There is considerably more information regarding the palaeoenvironment of East Asia (China and the Tibetan Plateau) (Jablonski, 1999b). During the Pleistocene, environmental change brought about increases in environmental seasonality at all latitudes, increasing environmental heterogeneity and fragmentation, an increasing potential for physical isolation of populations as a result of habitat fragmentation, and changes in the configuration of biogeographic corridors (Ferguson, 1993). This environmental reorganisation had the effect of reducing the size of the subtropical zone in East Asia. This zone, which had extended across the breadth of the continent in the Tertiary, lost its western flank and was shifted southwards over the course of the Pleistocene. This can be seen in the fossil record of gibbons and other mammals in China, which show a southerly shift in distribution from Early to Late Pleistocene (Jablonski, 1998, 1999b). Different faunas show interesting patterns in response to these shifts. Jablonski (1998, 1999b) has plotted the geographic distribution of various fossil faunas in East Asia during the Pleistocene. For gibbons, this shows a southerly shift in distribution as with other East Asian genera such as Gigantopithecus, Pongo, Macaca, and Rhinopithecus\ however, gibbons are exceptional in terms of their number of 280 occurrences (Gu, 1989; Jablonski, 1998, 1999b). According to the evidence from the fossil record, gibbons were some of the most successful mammals, with distributions maintained in the tropical and subtropical zones throughout the Pleistocene (Jablonski, 1998, 1999b). Jablonski (1998) suggests this success is due to the small body size of gibbons and several important adaptations in life history parameters, and that these parameters evolved under relatively predictable and stable climatic regimes of the Miocene. Jablonski, further suggests that despite having relatively large and metabolically expensive brains, their total metabolic requirements are lower than those of orang-utans, because of their small body size. The gibbons’ success during the Pleistocene in East Asia (outlined by Jablonski (1998, 1999b) as being due to small body size and efficient metabolic parameters), provides suggestive evidence for their success before the Pleistocene in exploiting Pliocene and Late Miocene environments, even if the paucity of fossil evidence from the Late Miocene and Pliocene makes it difficult to reconstruct a biogeographic scenario for gibbons (or other mammals) during this time period. Furthermore, it is not fully understood why these fossils are so scarce. The shortage may be due to taphonomic factors, but may represent a real decrease in diversity and numbers during this time (Jablonski, 1998). Thus, the exact nature and timing of climatic and environmental change in SE Asia, is still hotly debated and uncertain (Jablonski, 1993b, 1997 and references therein). At such time when it become possible to reconstruct the palaeoenvironment of SE Asia during the last few million years, such information may be combined with fossil evidence and reconstructions such as those provided here, to postulate specific colonisation events with regard to particular species. Without crucial fossils from the this time period, however, the picture will remain patchy. 281 5.2 Concluding remarks This study has generated a new estimate of gibbon phylogeny based on molecular sequence data, which provides further evidence to suggest that cytochrome b gene is useful for reconstructing evolutionary history at the timescale of several millions of years. According to the results of this study, morphological metric data derived from the entire gibbon skeleton does not map the molecular estimate of gibbon phylogeny. This provides further evidence to suggest that metric skeletal data may be phylogenetically uninformative at this level. The results of this study indicate that the gibbon radiation dates to much earlier than previously thought i.e. late Miocene (Chivers, 1977; Groves, 1972). This is congruent with other recent estimates based on molecular data (Zehr et a/., 1996; Porter et al., 1997). Although the data suggests an early divergence for the various gibbon subgenera, however, these dates may apply to the respective gibbon progenitors, since it is well known genetic divergence pre-dates spéciation (Nei, 1987). In this respect the spéciations giving rise to the present day gibbons may have occurred during the Plio- Pleistocene. The exact pattern of radiation of such spéciations cannot be fully explored due to a controversial palaeoenvironmental record and sparse fossil record (Tyler, 1993; Jablonski, 1993b, 1997). However, this study has provided a new scenario for the pattern and timing of divergence of the gibbon subgenera, which provides a testable hypothesis. As evidence from the palaeoenvironmental and palaeontological record comes to light the hypothesis can be developed and refined. Furthermore, this study has tested a novel technique, namely cladistic biogeography, not previously used within the field of primates to address questions regarding the biogeographic history of a group of organisms. 282 There are several cautionary factors which must be taken into account when using such techniques, however. Firstly, the results of such biogeographic analyses are entirely dependant on the estimate of phylogeny, and divergence times, being correct. Secondly, such methods operate on a fairly simple algorithm. Thus, when dealing with a particularly complex aspect of a group’s evolutionary history these techniques may be limiting in terms of resolution. This was evident in this study regarding divergence within the subgenus Hylobates. This group have complex evolutionary history involving a relatively recent spéciation as seen by their estimated divergence dates, and involving a complex geographic area including various parts of mainland SE Asia and four separate islands. However, the biogeographic reconstruction techniques employed in this study are all relatively new. As this field of enquiry develops and improves, so will the level of interpretation and resolution of pertinent biogeographic questions. In conclusion, the gibbons represent a diverse radiation with a complex, but fascinating, evolutionary history, several aspects of which this study has shed light on. Furthermore, the testable hypothesis which has been generated in this study inspires further research in the fields of gibbon genetics, external and soft-tissue morphology, and SE Asia environmental history. 283 5.3 Future research The key area which is likely to spread new light on the interrelationships among gibbons, particularly among closely related species of gibbons in the subgenus Hylobates, is genetic variability. Work into the variability of the gibbon control region to investigate the possible causes of the ambiguous results these data provided, will continue. This will also contribute to a greater understanding of the evolution of the control region itself. In addition, different genetic loci, such as microsatellites, Y- chromosome markers and nuclear genes, may provide further insight into the evolutionary history of gibbons. The present study is also being taken further using pelage data in the phylogenetic reconstruction of interrelationships among gibbons. Pelage data collected while recording skeletal measurements will be supplemented, and various methods of re-coding explored, in order to analyse the data using cladistic methods. Several studies have suggested that soft-tissue characteristics can yield relationships that are compatible with molecular phylogenies (Shoshani et a l, 1996; Groves, 2000; Gibbs et al., submitted). This area is also to be explored further. In addition, techniques for combining data and/or trees is being investigated. Such an approach might combine several aspects of gibbon data including pelage, molecular, behavioural and soft-tissue. Other approaches to reconstructing gibbon biogeographic history are also being investigated. These approaches include congruence testing forms of reconstruction such as Component Analysis (Page, 1988). Phylogenies for comparable groups of organisms endemic to SE Asia are required for such analyses, such as the colobines or cercopthecines. Further work may have to be done regarding phylogenetic relationships within these groups as well, however, since there is considerable controversy over this. Sequence data is available for many comparable Asian primate groups on the world 284 wide web, so phylogenetic reconstruction of these groups for inclusion in congruence testing forms of biogeographic analysis is being investigated. Finally, field study ventures are being considered for the purposes of conservation. In particular, a population genetics study of the Hainan gibbon, H. concolor hainanus, which is especially threatened in the wild, is being looked into with the help of Chinese collaborators. The study aims to assess the population dynamics of the gibbons on this small island, and determine the long term prospects for its survival, which at the present seems highly uncertain. 285 Appendices Contents Page numbers Appendix I. Pelage Descriptions 287 Appendix II. Control Region sequence data 294 Appendix III. Segment and Range coded metric data matrices 296 Appendix IV. Non size-corrected (raw) cranial, dental and postcranial data 308 286 Appendix I. Pelage Descriptions Below is a description of the pelage of each species/subspecies of hylobatid based on previously published descriptions and observations made during Museum visits H. agilis - Polymorphic: light buff/yellow/brown/blackish brown FACE • white brow band (Males + Females) • white/pale brown cheek patches - often joined under chin (M) • white face ring (Juveniles) TORSO • light individuals: sometimes have contrasting darker brown ventral fur, sometimes extending on to inner aspect of limbs, occasionally with dark cap • blackish brown individuals: sometimes with brown lower back, occasionally with brown corona and brown distal limbs HANDS AND FEET • usually not contrasting with limbs GENITAL AREA • prominent genital tuft in males: in brown individuals tufts often contrasting buff or light grey coloration (not so though in blackish brown males) : in light individuals tuft is not contrasting. • in females, hair in genital region has same colour as ventral fur. H. agilis alhiharbis - Polymorphic [formerly H. cinereus albibarbis - Kloss, 1929] • Kloss’s key - differs from muelleri muelleri in having the lighter, browner cap; lighter browner whiskers and chin, which are generally buffy; lower back usually light buffy, much lighter in colour than the rest of the upper parts, and the arms tending to be similar; underparts and inner sides of limbs brownish black, less widely diffused. Seems to be a form o f paler colour generally than muelleri with lighter whiskers FACE • white brow band (M + F) • white/cream cheek patches (often joined under chin) (M + 50% F (according to Marshall and Sugardjito, 1986)) • white face ring (Juveniles) TORSO • dorsal side greyish brown, contrasting with blackish brown or black on ventral fur and inner side of limbs • rump often golden buff/brown • dark brown cap • buff/light grey corona • buff/light grey distal limbs HANDS AND FEET • fur on digits black GENITAL AREA • prominent genital tuft in males: contrastingly buff/light grey coloration • in females, hair in genital region has same colour as ventral fur. 287 H. muelleri - Monochromatic: 3 colour ‘races’ intergrade (Marshall and Sugardjito, 1986) • black on digits, cap, and underparts is reduced in successive samples from Southeast counter-clockwise to west around periphery of Borneo • coat grey brown of brown, usually with contrastingly coloured dark brown or black chest ventral fur, inner aspect of limbs, and cap • white brow band (M + F) • sometimes light grey or whitish cheeks or chin, not usually sharply demarcated • genital area: no prominent genital tuft in males, hair black H. muelleri muelleri (S. E. Kalimantan) [Kloss’s H. cinereus muelleri] • Kloss’s key: general colour darker then neutral grey, all-over; hands and feet distinctly darker than the adjoining outer sides of the limbs; chin and whiskers generally brownish • pale grey / greyish brown • white brow band • whitish /brownish whiskers • black cap, venter hands and toes • distinguishable by the brownish black of the lower parts and inner sides of the limbs, and by the black extremities • contrasting dark digits or whole hands and feet • [Lyon’s (1911) description, based on 13 specimens collected by Dr. W. L. Abbott in SE Borneo, - crown patch - blackish, lower 1/2 of back wood-brown, arms inclining towards the same, side and chin whiskers brownish, not conspicuously different in colour from the rest of the animal; underparts, including inner sides of arms and legs, blackish; the extremities and the cap distinctly darker than the rest of the body.] H. muelleri funereus (N Borneo) - [Kloss’s H. cinereus funereus] • Kloss’s key: general colour darker than neutral grey; hands and feet distinctly darker than the adjoining outer sides of the limbs; with cap, whiskers, underparts of body and inner sides of limbs black or brownish-black • black cap and venter • lacks black hands and feet, feet and hands are pale grey • some have feet paler than legs H. muelleri abhotti (W Borneo) [Kloss’s H. cinereus abbotti] Kloss’ s key: browner and more buffy than muelleri muelleri - general colour nearer mousy- grey. It is paler, > buffy on the underside of the body, and the rump is often more buffy still . Breast-band often present. Dark cap ABSENT / obsolescent. Genital patch black • lack pattern; uniform mouse grey or pale brown, some with black on throat 288 H. lar - Polymorphic: light buff/yellow/brown/blackish brown/black GENERAL (from Geissmann, 1993) • white hands and feet, contrasting with distal limb coloration except in very pale specimens • white face ring • no prominent genital tuft in males • in light individuals, esp. males, hair in genital region often coloured contrasting, darker brown or blackish According to Marshall and Sugardjito (1986) and Groves (1984) the subspecies of lar exhibit much variation - H. lar vestitis (N. Sumatra) [Kloss’s (1929) H. I. albimanus] • always white face ring • hands and feet white • buff on back • paler on lower back • contrasting brown cap • brown venter and limbs • [bridge of nose depressed , supracilliary arches only slightly developed at their inner ends. And, the space between the inner angles of the supraorbital ridges is shallow, not trough-like, and the nasal bones are straight (according to Lyon 1908, but not to Kloss), the bridge of the nose not being depressed ; furthermore, the ascending portion of the mandible is lower; the last upper molar is smaller (Kloss, 1929)] H. lar lar (Malaya (south of agilis), Trang district of S. Thailand) • 2 colour phases: pale creamy / medium-dark brown • dark phase has light hair bases, extending over 1/2 - 2/3 the length of the hair • bridge of nose depressed, suprascilliary arches well developed at the their inner ends (Kloss, 1929) H. lar entelloides (peninsular Thailand and Burma - tropical rainforest zone) • 2 colour phases: pale fawn or honey / dark blackish brown • dark phase has lightened hair bases, but extend only 1/3 length of the hair H. lar carpenteri (N and W Thailand, and parts of Burma (broadly the monsoon forest zone) • 2 colour phases: pale creamy white / very dark chocolate brown • in both phases the hair bases are lighter, greyer, extending over 1 / 2 the length of the hair • often pubic hairs are white 289 H. moloch - Monochromatic: silver grey, rarely light grey brown [Kloss’s H. cinereus cinereus] • Kloss’s key: colour near neutral grey, generally very uniform FACE • white brow band (M + F), surrounded according to Kloss, by a rather pale face and throat • white forward projecting goatee beard • a blackish or dark brown cap present, generally very marked (Kloss, 1929) TORSO • sometimes dark grey or black chest and cap (more common in F than in M, absent in juveniles) • next to the face the abdomen is the generally the palest part of the animal. There is often a dark breast band. On the upper side the rump and nape are often palest (Kloss,1929) HANDS AND FEET • not contrasting with distal limb coloration GENITAL AREA • no prominent genital tuft in males • hair in genital region usually contrasting black, occasionally grey H. pileatus -Sexually Dichromatic MALES • glossy black with white or light grey corona, sometimes interrupted by the occipital region, • white brow, • white figure eight facial rim, • white genital tassel (55mm long; Marshall and Sugardjito, 1986) • white hands and feet • black encroaches upon the upper surface of the wrist and ankle so that extremities show less white that those of H. lar FEMALES • light buff grey with contrasting black ventral fur (shaped like an inverted triangle - Geissmann, 1993) / pinkish grey (Marshall and Sugardjito, 1986) • black cap (Geissmann, 1993) • all black areas clearly demarcated • corona does not contrast with pale back (Geissmann, 1993) / corona white (Marshall and Sugardjito, 1986) • white brow band (Marshall and Sugardjito, 1986) • white facial rim (Marshall and Sugardjito, 1986) • crown, throat, sides of head and ventral shield are black (Marshall and Sugardjito, 1986) • hands and feet not contrasting with distal limb coloration - white coloration less than in H. lar • genital hair not contrasting in colour = buff grey (Geissmann, 1993) N.B. juvenile pileated gibbons are coloured like the female (reversing the plan followed by other sexually dichromatic species (Marshall and Sugardjito, 1986) 290 H. hoolock - Sexually dichromatic FEMALES • pale brown, with darker brown ventral fur (not sharply demarcated) • darker brown throat and sides of head • cream cap, contrasting with sides of head • contrasting white brow band • contrasting thin white rim completely surrounds facial area and also each eye • H. h. hoolock'. same colour as distal limbs; H. h. leuconedys some lightening on hands and feet • genital hair brownish, not contrastingly coloured H. hoolock hoolock (westbank and westward of Chindwin River, Burma) MALES • black on back • have black genital tassel • white brow band • hands and feet same colour as distal limbs H. hoolock leuconedvs (eastbank and eastward of Chindwin River, Burma) MALES • glossed with brown on back • tassels are contrastingly non-black, usually lighter yellow, grey or white • white patch on chin • white brow band H. concolor - Sexually dichromatic MALES • H. c. concolor black, with long fur (Groves, 1972; Marshall and Sugardjito, 1986) • H. c. hainanus black, with short fur (Groves, 1972; Marshall and Sugardjito, 1986) • H. c. lu black, with a trace of silver in a band along the side of the head (Groves, 1972; Marshall and Sugardjito, 1986) • small crest of erect hairs on top of the head • no facial markings • hands and feet: not contrasting with distal limb coloration • no prominent genital tuft FEMALES yellow to pale grey brown, dark brown or blackish ventral fur (often sharply demarcated and often interspersed with lighter hairs throat sometimes dark no darkening of ventral fur in H. c. hainanus no crest but black cap distinct small black tufts on ears contrasting black patch on chin ) absent in H. c. hainanus sometimes contrasting white patch below eyes light brow band rare below eyebrows a distinct rim of black hairs commonly occurs - absent in H. c. hainanus hands and feet: contrasting with distal limb coloration, with black digits - sometimes in H. c. hainanus 291 • hair in genital region usually contrastingly black - absent in H. c. hainanus H. leucogenys- Sexually dichromatic MALES • black, with relatively big crest of erect hairs on top of the head (small in H. 1. siki) • contrasting white cheek patches • hands and feet: not contrasting with distal limb coloration • no prominent genital tuft FEMALES pale yellow to golden yellow, without dark hairs on ventral fur no crest, but black cap small black tuft on ears often indistinct contrasting white brow band frequently thin white rim completely surrounding facial area, rim extends to contrasting white patch below eye hands and feet: not contrasting with distal limb coloration - frequently lack black distal digits hair in genital region usually contrastingly darker brown or rusty H. gahriellae - Sexually dichromatic MALES • black, with small crest of erect hairs on top of the head • orange brown lightening on chest • contrasting creamy orange, light yellow or whitish cheek patches • hands and feet: not contrasting with distal limb coloration • no prominent genital tuft FEMALES yellow to orange yellow, without dark hairs on ventral fur no crest, but black cap distinct small black tufts on ears sometimes contrastingly white patch below eye light brow band rare, but below eyebrows a distinct rim of black hairs commonly o’ccurs hands and feet: contrasting with distal limb coloration - black distal digits hair in genital region usually contrastingly black H. syndactylus -Monochromatic • black • no facial markings: but some exceptions are described by Geissmann, 1993 • prominent genital tassel in males • throat naked • Several authors (Groves, 1972; Kloss, 1929) note that while most siamangs male and female, seem not to display any circumfacial markings, that the ‘odd’ specimen shows evidence of some short white hairs are occasionally present, especially in the lower half of the face. Groves equates this with old age, while Geissmann (1993) reports evidence of such facial hair in about 50 captive siamangs of various ages. 292 H. klossi - Monochromatic • glossy black all over • no facial markings • throat furred (Kloss, 1929) • no preputal tassel or tuft, as seen in siamangs (Kloss, 1929) 293 Appendix II. Control Region sequence data generated in this study Hylobates muelleri ************************************************************** TAA* * TCACTTGAACAACTACAATACA* TTAACTACCAAACGTACATATCAACATCCCCAACAT GCTTACAAGCAAGCACCAGTATACCTCAACCAACTGTAGAGCATTCACTTCACTCTCACGA* * * * * * * * * * * * * * * CATACACACCAACCAGCA* * * * * * * * * * * *AAGATTGTCCATCTAA* *AGGG CGTCCTGCACTCGTTCTTCACCGCACATACAAAC*CTCCACCAAAATCAACTCACAATCCATAC AA**AGCCTACTTC Hylobates concolor ACGATT* *TTTATGTACTTCGTACATTAATGC*CAGCCCCCATGAATATTGTAC* * *GGTACTA AAATATTACTTAACTAACTATAGAACA*TACACTGCCAAACGCACATATTAAGTAACGCAACAT GCTTACAAGCAGGAACCAGCATACCTCCGACAACTGCAAGACATCCATCCTACTCCAACATCCC AAACCCAATCAACACGCGTATCAACCGATA* * * * * * * * * * * *AAGATAATCCATCCTG* * * *GG CATAGCACATTAAATCGTTCATTGCGCATAACACGCCATCTCCAAAATCAACTCACACTCCATA CAAGTAATCTATTTC Hylobates concolor 2 ACGATT* *TTTATGTACTTCGTACATTAATGC* CAGCCCCCATGAATATTGTAC* * *GGTACTA AAATATTACTTAACTAACTATAGAACA*TACACTGCCAAACGCACATATTAAGTAACGCAACAT GCTTACAAGCAGGAACCAGCATACCTCCGACAACTGCAAGACATCCATCCTACTCCAACATCCC AAACCCAATCAACACGCGTATCAACCGATA* * * * * * * * * * * *AAGATAATCCATCCTG** * *GG CATAGCACATTAAATCGTTCATTGCGCATAACACGCCATCTCCAAAATCAACTCACACTCCATA CAAGTAAT * * * * * * * Hylobates concolor siki ACAATTA* TTTATGTACTTCGTACATTAATGC * CAGCCCCCATGAATATTGTAC * * *GGTACTG AAATATTACTTAACTAACTATAGAACAATATACTGCCAAGCGTACATATTAAGTACTACAACAT GCTTACAAGCAAGAACCAGCATACCTCCAACAACTGTGAGACATCCATCCTACTCCAACATTCC AAACCCAATCAACACGCGTATCAACCGATA* * * * * * * * * * * *AAGATAGTCCATCCCG** * *GA CATGGCACATTAAATCGTTCATTGTACATAACATGCCATTTCCAAAATCAACTCACACTCCATA CGAGTAATCTATTTC Hylobates concolor siki * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * CCCCCATGAATATTGTAC* * *GGTACTG AAATATTACTTAACTAACTATAGAACAATATACTGCCAAGCGTACATATTAAGTACTACAACAT GCTTACAAGCAAGAACCAGCATACCTCCAACAACTGTGAGACATCCATCCTACTCCAACATTCC AAACCCAATCAACACGCGTATCAACCGATA* * * * * * * * * * * *AAGATAGTCCATCCCG* * * *GA CATGGCACATTAAATCGTTCATTGTACATAACATGCCATTTCCAAAATCAACTCACACTCCATA CGAGTAATCTATTTT Hylobates hoolock CTATAAT * TTTATGTACTTCGTACATTACTGC * TAGCCCCCATGGATATTGTAC* * *AGTACTT TAA* * TCACTTAACTAACTGTAGTACA* TAAACCCACAACCGTACATACCAAGCAATCCAACAC GCTTACAAGCAAGCACTAAAATACCTTAACCAATTGTAGGACATCCACTCCAACCCCACTAC** AATCCTTCCCAACATGCTTACCAACCAGCA* ********** *AAGATCATCCATCATT* * *GGA CATAGCACATTCATTCATTTACCGTACATGTAGAC*CTCCCCCACAATCAACTCACACTCCATA CGAGTAATCTATTTC 294 Hylobates hoolock 2 CTATAAT * TTTATGTACTTCGTACATTACTGCGTAGCCCCCATGGATATTGTAC ** *AGTACTT TAA* * TCACTTAACTAACTGTAGTACA* TAAACCCACAACCGTACATACCAAGCAATCCAACAC GCTTACAAGCAAGCACTAAAATACCTTAACCAATTGTAGGACATCCACTCCAACCCCACTAC** AATCCTTCCCAACATGCTTACCAACCAGCA** * * * * * * * * * *AAGATCATCCATCATT** *GGA CATAGCACATTCATTCATTTACCGTACATGTAGAC*CTCCCCCACAATCAACTCACACTCCATA CGAGTAATTTATT ** Hylobates agilis CTAATCACCCTGTGTACATCATGCATTATGTAATTTACCCCATTAATTATGTAG**TAGTACTA TAT * * ACACTTCATCATACATTATACA* TTTATATCTTATAGTGCTTTTAAATCCATTCCCCAT GCATACAAGCATGCACATCAAACTAATCACAGTGCGTAATACATTAAATTAAAAGTCCACACAT ATGGATT * TAACCGTGAGTATTGTTCCATA* ******** CAGTAAGTTCTTCATTTTA* ***** CATAGGACATAATATTATTGATCATACATAGCACA*GGAAGTCA*AATCCTTTTTTTGACAACA TGC * TTATCACCTCC Hylobates pileatus CTAATCACCCTGTGTACATCATGCATTATGTAATTTACCCCATTAATTATGTAG**TAGTACTA TAT** ACACTTCATCATACATTATACA* TTTATATCTTATGGTGCTTTTAAATCCATTCCCCAT GCATACAAGCATGCACATCAAACTAATCACAGTGCGTAATACATTAAATTAAAAGTCCACACAT ATGGATT * TAACCGTGAGTATTGTTCCATA******* * *CAGTAAGTTCTTCATTTTA* ***** CATAGGACATAATATTATTGATCATACATAGCACA*GGAAGTCA*AATCCTTTTTTTGACAACA TGC * TTATCACCTCC Hylobates moloch CTAATCACCCTGTGTCCATCATGCATTATGTAATTTACCCCATTAATTATGTGCGTCAGTACTA TAT**ACACTTGATCATACATTATACA*TTCATATCTTATAGTGCTTTAAAATCCATTCCCCAT GCATACAAGCATGCACATCAAACTAATCACAGGGCGTAATACATTAAATTAAAAGTCCACACAT ATGGATT * TAACCGTGAGTATTGTTCCATA******* * *CAGAAAGTTCTTCATTTTA* ***** CATAGGACATACTATTATTGATCATACATAGCACA*GGAAGTCA*AATCCTTTTTTTGACAACA TGC * TTATCACCTCC 295 Appendix III. Segment Coded Metric Data: Data Matrix Segment coded cranial data Taxa Characters Variable No. 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 LARC EE C E C P NR HDDBLKRND G AGILISC EEB Q C PN Q ID DB KL S MD G MOLOCH c EEBE c PNRHD DB LL s MD G MUELLERI c EEBE c PN Q HD DB KL s MD Q PILEATUS c EEBE c PN Q HD DB K L s MC Q KLOSSII c EEBE c P NR IDDBLK s MD G HOOLOCK c ED C Q c P N RIDD C KLRMC Q CONCOLOR c EEB Q c NM Q HE DB L K S LD G GABRIELLAE c E E C Q c NM Q HD DB L KRLD Q LEUCOGENYS c EEC Q c P N Q IDDBLK s LD G SYNDACTYLUS c E D C Q c P N s ID DBKKRM C Q HUMANA DD C E c NMN EE CC PLUMHJ Taxa Characters Variable No. 36 37 38 39 40 41 LARL Q KIDD AGILIS L Q KI DD MOLOCHL Q K J DD MUELLERILLKI DD PILEATUS L EK IDD KLOSSII LEKIDD HOOLOCKL Q KI ED CONCOLOR L E K IE D GABRIELLAE LEKIDD LEUCOGENYS LEKIDD SYNDACTYLUSM Q KIDD HUMAN L G KKD E Segment coded dental data Taxa Characters Variable No. I 2 3 4 5 6 26 27 28 29 30 31 32 33 34 35 LAR B CC CE F C D C DDEE FDE AGILIS B CC BD F C E C DDEE FDE MOLOCHCC BBDFC E C DD FE FDE MUELLERI B c C BD F c D c DDE DFDE PILEATUS B c BBEG BEBC DEDF DE KLOSSIIC c B B EG BF C DD FDFDE HOOLOCK B c B BD G BEC DD FE G EF CONCOLOR BDBBDF BE C D C EDFD E GABRIELLAEB C B C D G C E C D C FDFD E LEUCOGENYS B C BBD G CFC DD FDFDF SYNDACTYLUS B CC BDFBE c D C FDFDF HUMANC D CC D C CC D C E FEEE E See Table 3.3 for details of Variable numbers. 296 Appendix III. Segment Coded Metric Data: Data Matrix Segment coded postcranial data Taxa Characters Variable No. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 LARUB A A C VAAA RBAA A P CB AA AGILIS U B AA C WAAA RBAAAP CBAA MOLOCHTB A A C VAAA RBAAAP CBAA MUELLERIS B AA C VAAA Q BA A A N C BAA PILEATUS RB AAC u AAA Q BAA APCBA A KLOSSII T B AAC w AAA S BAA A Q CBBA HOOLOCK T B AAC V AAARBAA A p CBAA CONCOLOR T B A A C X AAA S BAAA p CBBA GABRIELLAEUB A A C X AAA Q BAAA N C BAA LEUCOGENYSU B AAC X AAA p BAA A P CBAA SYNDACTYLUS T B A A C YAAA p BA A AMCBAA HUMANLB A A CJ AAA RBA B A N CB BA Taxa Characters Variable No. 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 LAR B C F G BB CC A BHD C BB AGILIS B C E G BBB C AB G DC BB MOLOCH B C E G B B B C B CH D C BB MUELLERI B C E G BBB C A CG D c BB PILEATUS B C E G BBC C B C GC c BB KLOSSII B C EGBB BB A CHE c B B HOOLOCK B C EHB C B C B CHD c BB CONCOLORCC E HB C ?DA ? HD c BB GABRIELLAEB C EH BBBDA C HD c B B LEUCOGENYS B C EH BC B DA c H D c B B SYNDACTYLUS B C EH BB B B B c H E c BB HUMAN BED FBBBDBBEF c BB See Table 3.3 for details of Variable numbers. 297 Appendix III. Segment Coded Metric Data: Data Matrix Segment coded cranial, dental and postcranial data Taxa Characters LARCEEC E C PNRHDDBL K RND G L Q K IDD AGILISC EEB Q C PN Q IDDBKL S MD G L Q KIDD MOLOCH c EEBE CPNRHDDBLL S MD G L Q K JDD MUELLERI c EEBE CPN Q H D DBKL S M D Q LLK IDD PILEATUS c EEBE C P N Q HDD BKL s MC Q LEKIDD KLOSSII c EEBE C PNRIDDBLK s MD G LEKIDD HOOLOCK c EDC Q C P N RIDD C KL R M C Q L Q KIED CONCOLOR c EEB Q C NM Q HEDBLK S LD G LEKIED GABRIELLAE c EE C Q CNM Q HDDBL KRL D Q LEKIDD LEUCOGENYS c EE C Q CPN Q IDDBL KS LD GLE KID D SYNDACTYLUS c ED C Q C P NS IDDBK K RM C Q M Q KID D HUMANADD C E CN MNEE CC PL U MH J L G KKDE LAR B CCC EF C D C DDEEFDE U BAAC V AA A AGILIS B CC BDF C E C DDEEFDE U BAA C WA A A MOLOCHCC BBDF C E C DDFE F DETBAA C VA AA MUELLERIB CC BD F c D c DDEDFDE S B A A C VAAA PILEATUS B C BBE G BE B C DED FDER B AAC u AAA KLOSSIICCB B E G BFCD DFD FDETBAAC w AAA HOOLOCK B C B B D G BE C DDFE G EFTBAA c VAAA CONCOLOR BDBBDFBE C D C EDFDETBAA C XAAA GABRIELLAE B C B C D GC E C D C FDFDE U BAA C XAAA LEUCOGENYS B C BB D GC FC DDF DFDF U BAA C XAAA SYNDACTYLUS B CCBD FBE C D C FDFDFTBAA C V AAA HUMANCD C CDC C C D C EFEEEE LBAA c J AAA LARRBAAAP C BAA B C F G BB CC ABHD C BB AGILISRBAAAPCB A A B C E G BBBCAB G D C BB MOLOCHRB AA AP C BAAB C E G BB B C B C HD C BB MUELLERI Q B AA AN C BAAB c E G B BB C A CG D c BB PILEATUS Q B AAA P C BAAB c E G B B CC B CGC c BB KLOSSIIS B AA A Q C BBAB c E G B BBBA C HE c BB HOOLOCKRB AA A p C BAAB c EH B C B C B C HD c BB CONCOLOR S B A A A p C B B AC c E H B C ?DA?H D c BB GABRIELLAE Q B AAANC BAAB c EHBB BDA C HD c BB LEUCOGENYS p B A A A P C BAAB c EHBC BDA C H D c B B SYNDACTYLUS p BAAAMCB AAB c EHBBB BB c HE c BB HUMAN RBABAN C BBABEDFB BBDBBEF c B B 298 Appendix III. Segment Coded Metric Data: Data Matrix Segment coded cranial and dental data Taxa Characters LARC EE C E C PNRHDDBL K RND G L Q KI DD AGILIS C EEB Q CPN Q IDDBKL S MD G L Q KIDD MOLOCH c EEBE C PNRHDDBLL S MD G L Q K J DD MUELLERI c EEBECPN Q HDDBKL S MD Q L LKIDD PILEATUS c EEB E CPN Q HDDBKL s M C Q L EKID D KLOSSII c EEBE C PNRIDDBLK s MD G LEKIDD HOOLOCK c ED C Q C PN RID DC K LRM C Q L Q KIE D CONCOLOR c EE B Q CN M Q H E D B L K S LD G LEKIED GABRIELLAE c EE C Q C N M Q HD D BL KRLD Q L EKID D LEUCOGENYS c EE C Q C PN Q IDDBLK S LD G L EKIDD SYNDACTYLUS c ED C Q CPNS IDDBK KRM C Q M Q K I DD HUMANADD C E C NMN E ECCPL U MH J L G KKDE LAR B CC C EF C DC D DEEFDE AGILIS B CC BDFCEC DDEEF DE MOLOCH CC BBD F C E c DDFEFD E MUELLERIB CC BDF c D c DDEDF DE PILEATUS B C BBEGBE B C DEDFD E KLOSSII C C BB E GBFC DDFDFDE HOOLOCK B C BB D G BECDDFEGEF CONCOLOR B DB BDFBEC D C ED FDE GABRIELLAE B CBC D GC ECDC FD FDE LEUCOGENYS B CB BD GC FC DDFDF DF SYNDACTYLUS B CC B D FBEC D C F DFDF HUMAN C D C C D C C C D CE F E EEE 299 Appendix III. Segment Coded Metric Data; Data Matrix Segment coded cranial and postcranial data Taxa Characters LARCEECE C PNRH D DBLKRND G L Q K I D D AGILIS C E E B Q CPN 0 IDDBKL S MD G L Q KIDD MOLOCH c EEBE C PNRHDDBLL S MD G L Q K JDD MUELLERI c EEBECPN Q HDD BK LS MD Q L LKIDD PILEATUS c E EBE C P N Q HDDBKL s M C Q LEKID D KLOSSII c EEBE C PNRIDDBLK s MD G LEKIDD HOOLOCK c E DC Q CP NRI D D C KLRM C Q L Q KIED CONCOLOR c E EB Q C N M Q H E DBLK S LD G LEKIED GABRIELLAE c E EC Q CN M Q H D D B L K R L D Q LEKIDD LEUCOGENYS c EE C Q CPN Q IDDB L KSLD G LE K IDD SYNDACTYLUS c ED C Q C PN S IDDBKKRM C Q M Q KIDD HUMAN A D D C E C N M N EE CC PL U MH J L G KKDE LAR u BAA C VAAARBAAAP C BAABC F GBB AGILIS u BAA C w A A ARBAAAP c BAAB C E G BB MOLOCH TB AACVAAA R BAAAP c BAA B C E GBB MUELLERI SBAA C V A A A Q B AAAN c BAAB C E G BB PILEATUSR B AACUAA A Q B AAAP c B AA B C E G BB KLOSSII TBAA c w A AASBAAA Q c BBA B C EGBB HOOLOCKTBAA c VAAARBAA A p c BAABCEHBC CONCOLOR TB A A c XA AA S B A A A p c BBA CC EHB C GABRIELLAEUBAA c XAAA Q B AAAN c BAA B C E HBB LEUCOGENYSU B A A c X A A A p B A A A P c BAAB C EHB C SYNDACTYLUSTBAA c V A A A p B A A A M C BAAB C EHBB HUMAN L BAA c JA A AR B A BAN c BBA B E D FBB LAR CCA BHD C BB AGILIS B CA BGD C BB MOLOCH B CBCH D C BB MUELLERIB C ACGD c BB PILEATUS CC BCG C c BB KLOSSII B BAC HE c BB HOOLOCK B C B C HD c BB CONCOLOR ? DA ? H D c B B GABRIELLAEBDA c HD c BB LEUCOGENYS BD A c HD c BB SYNDACTYLUS BB B c HE c BB HUMAN BDBBEF c BB 300 Appendix III. Segment Coded Metric Data: Data Matrix Segment coded dental and postcranial data Taxa Characters LAR B CCC EF C D C DDEE F DE U BAA C VA AA AGILIS B C C BD F C E C DDEEFD EU B AA C W A AA MOLOCHCC BBDF C E C DDFEFDETBA AC V AAA MUELLERI B CC B D F c DC DDEDFD ES B AA C VAAA PILEATUS B C BBE G BEB C DEDFDERBA A C u A A A KLOSSII C C BBE G BF C DDF DFD ETBA AC w AA A HOOLOCK B c BB D GBEC DDFE G EF TBAA C VAAA CONCOLOR BDBBDF B E C D C EDFDETB AA c X A A A GABRIELLAE B C B C D GC E C D C FDF DE U BAA c XAAA LEUCOGENYS B C BBD GC F C DDFDFDF U B AA c XA A A SYNDACTYLUS B CC BDFBE C DC F DFDFTBAA c VAAA HUMAN CD CC D CCC DC EFEE EELBAA c JAAA LAR R B A A A P C BA AB C F G B B CC ABH D CBB AGILIS RBAAAP C BAABC E GBBB C AB G DCBB MOLOCHR B A AAP C BAABC E GBBB C B C HDCBB MUELLERI Q B A AAN C BAABCEGBBB C A CG D c BB PILEATUS Q BAA AP C BAABCE G BB CC B CGC c BB KLOSSII s BAAA Q C BBAB C E G BBBBA C HE c BB HOOLOCK RBAAAP C BA AB C EHB C B C B C HD c B B CONCOLOR SBAAAP C BB A CC EH BC?DA ? HD c B B GABRIELLAE Q BAAAN C BA AB C EHB B B DA C H D c B B LEUCOGENYS p B AAA P C B AABCEHB C BDA C HD c BB SYNDACTYLUS p BA A AM C BA AB C EHBBBBB c HE c BB HUMAN R BAB AN C BBABED F BBBDBBEF c B B 301 Appendix III. Range Coded Metric Data: Data Matrix Range coded cranial data Taxa Characters Variable No. 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 36 37 38 39 40 41 LAR 3 6 5 3 7 8 3 9 5 A A 9 8 7 8 AI 9 2 5 3 2 4 A 9 AGILIS 9 2 3 8 5 1 7 7 7 4 6 7 6 A 5 6 4 7 6 A 6 6 B 5 7 MOLOCH B 4 1 9 9 3 2 2 4 8 5 1 B 6 4 3 2 5 5 3 4 1 2 8 2 MUELLERI 5 5 6 7 8 7 6 5 9 6 B A 3 8 I 4 3 8 8 9 9 7 8 6 8 PILEATUS 4 1 2 C A 6 1 6 A B 7 8 4 9 2 2 5 A B 8 8 5 5 B 4 KLOSSII 1 3 4 A C 4 5 I 3 5 8 4 A 4 7 8 6 6 4 B B 8 A 9 5 HOOLOCK 2 9 A 2 4 2 9 4 2 2 4 5 1 B 3 C 7 C 9 2 5 3 C 2 6 CONCOLOR 6 A 8 B 6 B A A 8 7 2 3 7 2 A 7 C 2 3 7 C 9 9 1 C GABRIELLAE 8 7 7 6 2 C B C B 9 9 6 C 5 C 9 B 3 A C A B 3 7 A LEUCOGENYS 7 8 9 5 3 9 8 8 6 3 3 B 9 3 9 5 A 4 7 6 7 A 7 4 B SYNDACTYLUSAB B 1 1 A 4 3 I 1 C 2 5 C BB 9 B C I 2 4 6 3 3 HUMAN C CC 4 B 5 C B CC 1 C 2 1 6 1 8 I 1 4 1 C 1 C 1 Range coded dental data Taxa Characters Variable No. 1 2 3 4 5 6 26 27 28 29 30 31 32 33 34 35 LAR 7 7 2 3 2 6 4 B 2 A 4 C 4 B 7 9 AGILIS 5 6 I 5 7 8 7 8 7 6 5 9 5 9 9 8 MOLOCH 2 5 7 4 C A 6 7 6 3 3 6 3 4 5 4 MUELLERI 4 3 4 7 4 7 5 A 5 9 7 B 6 A 4 A PILEATUS 6 4 8 6 3 2 C 3 C B 6 8 7 8 3 5 KLOSSII 3 9 A C I 1 9 I 8 7 8 4 9 6 CC HOOLOCKB A C 9 8 4 B 9 A 4 2 5 2 1 1 2 CONCOLOR C 2 B B 5 9 A 4 3 2 CA C 7 AB GABRIELLAE 9 8 6 1 A 5 2 5 4 5 B 7 A 5 B 7 LEUCOGENYS A B 9 8 6 3 3 2 9 8 9 1 B 3 8 3 SYNDACTYLUS 8 C 5 A 9 B 8 6 B 1 A 3 8 2 6 1 HUMAN 1 I 3 2 B C 1 C 1 C 1 2 1 C 2 6 See Table 3.3 for details of Variable numbers. 302 Appendix III. Range Coded Metric Data: Data Matrix Range coded postcranial data Taxa Characters Variable No. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 LAR 3 8 7 7 8 6 3 8 8 3 7 7 7 8 4 B 8 7 6 9 B 1 8 8 7 AGILIS 2 9 9 6 7 4 8 6 7 5 9 8 5 5 3 9 9 8 7 8 9 7 7 9 8 MOLOCH 9 A C A 6 A B A A 4 B 6 8 7 6 7 BB 9 4 A 3 6 C A MUELLERI A C A 8 C 9 4 C 6 9 6 9 6 A B C 7 9 5 A C A B B 6 PILEATUS B B 6 5 2 B A 9 B 8 5 B 4 9 5 6 A 4 3 3 8 9 A 3 C KLOSSII 5 5 5 3 3 5 6 5 4 2 3 4 2 2 1 3 2 3 2 B 5 8 9 5 9 HOOLOCK 7 6 3 4 5 8 2 7 5 6 8 5 9 4 7 5 6 5 8 7 2 4 2 7 1 CONCOLOR 8 3 1 1 1 2 1 2 1 1 2 2 3 3 2 2 C 2 A 1 4 6 5 A 3 GABRIELLAE 4 7 8 9 B 3 5 3 3 A A A B 6 9 A 5 A C 5 6 5 1 1 4 LEUCOGENYS 1 4 2 2 4 1 7 1 2 B C C C C 8 4 3 6 4 2 3 B 4 4 2 SYNDACTYLUS 6 2 4 B 9 7 9 4 9 C 4 3 AB C 8 4 C B 6 7 2 3 6 B HUMAN C1BCACC B C 7 1 1 1 1 A 1 1 1 1 C 1 CC 2 5 Variable No. 67 68 69 70 71 72 73 74 75 LAR 2 5 7 A 4 A 8 3 4 AGILIS A 9 A 9 A 9 A 5 B MOLOCH 4 8 5 5 3 5 4 4 8 MUELLERI 9 A 8 6 B B 9 8 C PILEATUS 1 6 2 1 9 C B A 2 KLOSSII 6 C 6 8 6 3 5 2 7 HOOLOCK 5 7 13 1 7 2 6 6 CONCOLOR ? 3 C ? 8 6 C 7 9 GABRIELLAE 7 2 9 4 7 8 6 B 5 LEUCOGENYS B 4 B 7 5 4 1 C A SYNDACTYLUS 3 B 4 2 2 2 3 9 3 HUMAN 8 1 3 BC 1 7 1 1 See Table 3.3 for details of Variable numbers. 303 Appendix III. Range Coded Metric Data: Data Matrix Range coded cranial, dental and postcranial data Taxa Characters LAR 365378395A A 9 8 7 8 AI925324A9 AGILIS 9 2 3 8 5 I 7 7 7 4 6 7 6 A 5 6 4 7 6 A 6 6 B 5 7 MOLOCH B 4 1 9 9 3 2 2 4 S 5 1 B 6 4 3 2 5 5 3 4 I 2 8 2 MUELLERI 5 5 6 7 8 7 6 5 9 6 BA 3 8 1 4 3 8 8 9 9 7 8 6 8 PILEATUS 4 I 2 C A 6 1 6AB7849225AB 8 8 5 5 B 4 KLOSSII I 3 4 A C 4 5 I 3 5 8 4 A 4 7 8 6 6 4 B B 8 A 9 5 HOOLOCK 2 9 A 2 4 2 9 4 2 2 4 5 IB 3 C 7 C 9 2 5 3 C 2 6 CONCOLOR 6 A 8 B 6 B A A 8 7 2 3 7 2 A 7 C 2 3 7 C 9 9 I C GABRIELLAE 8 7 7 6 2 CBC B 9 9 6 C 5 C 9 B 3 A C A B 3 7 A LEUCOGENYS 7 8 9 5 3 9 8 8 6 3 3 B 9 3 9 5 A 4 7 6 7 A 7 4 B SYNDACTYLUS A B B I I A 4 3 II C 2 5 C B B 9 B C 1 2 4 6 3 3 HUMAN CCC 4 B 5 C B CC I C 2 I 6 I 8 1 1 4 I C 1 C I LAR 7 7 2 3 2 6 4 B 2 A 4 C 4B79387786388 AGILIS 56I5787876595998 2 9 9 6 7 4 8 6 7 MOLOCH 2 5 7 4 CA676336345 4 9 A C A 6 ABAA MUELLERI 4 3 4 7 4 7 5 A 5 9 7 B 6 A 4 A A C A 8 C 9 4 C 6 PILEATUS 6 4 8 6 3 2 C 3 C B 6 8 7 8 3 5 B B 6 5 2 B A 9 B KLOSSII 3 9ACI I 9 I 8 7 8 4 9 6 C C 5 5 5 3 3 5 6 5 4 HOOLOCK B A C 9 8 4 B 9 A 4 2 5 2 II 2 7 6 3 4 5 8 2 7 5 CONCOLOR C 2 B B 5 9 A 4 3 2 C A C 7 A B 8 3 I I I 2 1 2 I GABRIELLAE 9 8 6 IA 5 2 5 4 5 B 7 A 5 B 7 4 7 8 9 B 3 5 3 3 LEUCOGENYS A B 9 8 6 3 3 2 9 8 9 1 B 3 8 3 I 4 2 2 4 1 7 1 2 SYNDACTYLUS 8 C 5 A 9 B 8 6 B IA 3 8 2 6 I 6 2 4 B 9 7 9 4 9 HUMAN 1 I 3 2 B CIC I C 1 2 I C 2 6 C IBCACC B C LAR 3 7 7 7 8 4 B 8 7 6 9 B 1 8 8 7 2 5 7 A 4 A 8 3 4 AGILIS 5 9 8 5 5 3 9 9 8 7 8 9 7 7 9 8 A 9 A 9 A 9 A 5 B MOLOCH 4 B 6 8 7 6 7 BB 9 4 A 3 6 C A 4 8 5 5 3 5 4 4 8 MUELLERI 9 6 9 6 A B C 7 9 5 A C A B B 6 9 A 8 6 B B 9 8 C PILEATUS 8 5 B 4 9 5 6 A 4 3 3 8 9 A 3 C I 6 2 I 9 C BA 2 KLOSSII 2 3 4 2 2 I 3 232B589596C6 8 6 3 5 2 7 HOOLOCK 6 8 5 9 4 7 5 6 5 8 7 2 4 2 7 1 5 7 I 3 1 7 '2 6 6 CONCOLOR I 2 2 3 3 2 2 C 2 A 1 4 6 5 A 3 ? 3 C ? 8 6 C 7 9 GABRIELLAE A A A B 6 9 A 5 A C 5 6 5 I 1 4 7 2 9 4 7 8 6 B 5 LEUCOGENYS B CC C C 8 4 3 6 4 2 3 B 4 4 2 B 4 B 7 5 4 I C A SYNDACTYLUS C 4 3 A B C 8 4 C B 6 7 2 3 6 B 3 B 4 2 2 2 3 9 3 HUMAN 7 I I II AI III C 1 CC 2 5 8 I 3 BC 1 7 II 304 Appendix III. Range Coded Metric Data: Data Matrix Range coded cranial and dental data Taxa Characters LAR 3 6 5 3 7 8 3 9 5 A A 9 8 7 8 A I 9 2 5 3 2 4 A 9 AGILIS 9 2 3 8 5 1 7 7 7 4 6 7 6 A 5 6 4 7 6 A 6 6 B 5 7 MOLOCH B 4 1 9 9 3 2 2 4 8 5 1 B 6 4 3 2 5 5 3 4 1 2 8 2 MUELLERI 5 5 6 7 8 7 6 5 9 6 BA 3 8 1 4 3 8 8 9 9 7 8 6 8 PILEATUS 4 1 2 C A 6 1 6 A B 7 8 4 9 2 2 5 A B 8 8 5 5 B 4 KLOSSII 1 3 4 AC 4 5 1 3 5 8 4 A 4 7 8 6 6 4 B B 8 A 9 5 HOOLOCK 2 9 A 2 4 2 9 4 2 2 4 5 1 B 3 C 7 C 9 2 5 3 C 2 6 CONCOLOR 6 A 8 B 6 B A A 8 7 2 3 7 2 A 7 C 2 3 7 C 9 9 1 C GABRIELLAE 8 7 7 6 2 C B C B 9 9 6 C 5 C 9 B 3 A C AB 3 7 A LEUCOGENYS 7 8 9 5 3 9 8 8 6 3 3 B 9 3 9 5 A 4 7 6 7 A 7 4 B SYNDACTYLUS A B B 1 1 A 4 3 1 1 C 2 5 C B B 9 B C 1 2 4 6 3 3 HUMANC CC 4 B 5 C BC C 1 C 2 1 6 1 8 1 1 4 1 C 1 C 1 LAR 7 7 2 3 2 6 4 B 2 A 4 C 4 B 7 9 AGILIS 5 6 1 5 7 8 7 8 7 6 5 9 5 9 9 8 MOLOCH 2 5 7 4 C A 6 7 6 3 3 6 3 4 5 4 MUELLERI 4 3 4 7 4 7 5 A 5 9 7 B 6 A 4 A PILEATUS 6 4 8 6 3 2 C 3 CB 6 8 7 8 3 5 KLOSSII 3 9 A C 1 1 9 1 8 7 8 4 9 6 C C HOOLOCK B A C 9 8 4 B 9 A 4 2 5 2 1 1 2 CONCOLOR C 2 B B 5 9 A 4 3 2 C A C 7 A B GABRIELLAE 9 8 6 1 A 5 2 5 4 5 B 7 A 5 B 7 LEUCOGENYS AB 9 8 6 3 3 2 9 8 9 1 B 3 8 3 SYNDACTYLUS 8 C 5 A 9 B 8 6 B 1 A 3 8 2 6 1 HUMAN 1 1 3 2 B C 1 C 1 C 1 2 1 C 2 6 305 Appendix III. Range Coded Metric Data: Data Matrix Range coded cranial and postcranial data Taxa Characters LAR 3 6 5 3 7 8 3 9 5 A A 9 8 7 8 AI 9 2 5 3 2 4 A 9 AGILIS 9 2 3 8 5 1 7 7 7 4 6 7 6 A 5 6 4 7 6 A 6 6 B 5 7 MOLOCH B4 199 3 2 2 4 8 5 1 B 6 4 3 2 5 5 3 4 1 2 8 2 MUELLERI 5 5 6 7 8 7 6 5 9 6 B A 3 8 I 4 3 8 8 9 9 7 8 6 8 PILEATUS 4 1 2 C A 6 I 6 A B 7 8 4 9 2 2 5 A B 8 8 5 5 B 4 KLOSSII 1 3 4 A C 4 5 I 3 5 8 4 A 4 7 8 6 6 4 B B 8 A 9 5 HOOLOCK 2 9 A 2 4 2 9 4 2 2 4 5 1 B 3 C 7 C 9 2 5 3 C 2 6 CONCOLOR 6 A 8 B 6 B A A 8 7 2 3 7 2 A 7 C 2 3 7 C 9 9 I C GABRIELLAE 8 7 7 6 2 C B C B 9 9 6 C 5 C 9 B 3 A C AB 3 7 A LEUCOGENYS 7 8 9 5 3 9 8 8 6 3 3 B9 3 9 5 A 4 7 6 7 A 7 4 B SYNDACTYLUS A B B I 1 A 4 3 I 1 C 2 5 C BB 9 B C 1 2 4 6 3 3 HUMAN CCC 4 B 5 C B CC 1 C 2 1 6 I 8 I I 4 1 CICI LAR 3877863883777 8 4 B 8 7 6 9 B 1 8 8 7 AGILIS 2 9 9 6 7 4 8 6 7 5 9 8 5 5 3 9 9 8 7 8 9 7 7 9 8 MOLOCH 9 A C A 6 A B A A 4 B 6 8 7 6 7 B B 9 4 A 3 6 C A MUELLERIA C A 8 C 9 4 C 6 9 6 9 6 A B C 7 9 5 A C AB B 6 PILEATUS B B 6 5 2 B A 9 B 8 5 B 4 9 5 6 A 4 3 3 8 9 A 3 C KLOSSII 5 5 5 3 3 5 6 5 4 2 3 4 2 2 I 3 2 3 2 B 5 8 9 5 9 HOOLOCK 7 6 3 4 5 8 2 7 5 6 8 5 9 4 7 5 6 5 8 7 2 4 2 7 1 CONCOLOR 8 3 1 1 1 2 1 2 1 I 2 2 3 3 2 2 C 2 A 1 4 6 5 A 3 GABRIELLAE 4 7 8 9 B 3 5 3 3 A AAB 6 9 A 5 A C 5 6 5 1 1 4 LEUCOGENYS 1 4 2 2 4 1 7 1 2 B C CC C 8 4 3 6 4 2 3 B 4 4 2 SYNDACTYLUS 6 2 4 B 9 7 9 4 9 C 4 3 AB C 8 4 C B 6 7 2 3 6 B HUMAN C 1 B C A C C B C 7 1 1 I 1 A 1 1 1 I C 1 CC 2 5 LAR 2 5 7 A 4 A 8 3 4 AGILISA 9 A 9 A 9 A 5 B MOLOCH 4 8 5 5 3 5 4 4 8 MUELLERI 9 A 8 6 B B 9 8 C PILEATUS I 6 2 1 9 C B A 2 KLOSSII 6 C 6 8 6 3 5 2 7 HOOLOCK 5 7 13 1 7 2 6 6 CONCOLOR ? 3 C ? 8 6 C 7 9 GABRIELLAE 7 2 9 4 7 8 6 B 5 LEUCOGENYS B 4 B 7 5 4 IC A SYNDACTYLUS 3 B 4 2 2 2 3 9 3 HUMAN 1 3 B C 1 7 1 1 306 Appendix III. Range Coded Metric Data: Data Matrix Range coded dental and postcranial data Taxa Characters LAR 7 7 2 3 2 6 4 B 2 A 4 C 4 B 7 9 3 8 7 7 8 6 3 8 8 AGILIS 5 6 1 5 7 87876595998 2 9 9 6 7 4 8 6 7 MOLOCH 2 5 7 4 C A 6 7 6 3 3 6 3 4 5 4 9 A C A 6 A B A A MUELLERI 4 3 4 7 4 7 5 A 5 9 7 B 6 A 4 A A C A 8 C 9 4 C 6 PILEATUS 6 4 8 6 3 2 C 3 C B 6 8 7 8 3 5 B B 6 5 2 B A 9 B KLOSSII 3 9 A C 1 1 9 1 8 7 8 4 9 6 C C 5 5 5 3 3 5 6 5 4 HOOLOCK B A C 9 8 4 B 9 A 4 2 5 2 1 1 2 7 6 3 4 5 8 2 7 5 CONCOLORC 2 B B 5 9 A 4 3 2 C A C 7 A B 8 3 1 1 I 2 1 2 1 GABRIELLAE 9 8 6 1 A 5 2 5 4 5 B 7 A 5 B 7 4 7 8 9 B 3 5 3 3 LEUCOGENYS A B 9 8 6 3 3 2 9 8 9 1 B 3 8 3 1 4 2 2 4 I 7 I 2 SYNDACTYLUS 8 C 5 A 9 B 8 6 B 1 A 3 8 2 6 1 6 2 4 B 9 7 9 4 9 HUMAN 1 1 3 2 B C 1 C 1 C 1 2 1 C 2 6 C 1 B C A CC B C LAR 3 7 7 7 8 4 B 8 7 6 9 B 1 8 8 7 2 5 7 A 4 A 8 3 4 AGILIS 598553998 7 8 9 7 7 9 8 A 9 A 9 A 9 A 5 B MOLOCH 4 B 6 8 7 6 7 BB 9 4 A 3 6 C A 4 8 5 5 3 5 4 4 8 MUELLERI 9 6 9 6 A B C 7 9 5 A C A B B 6 9 A 8 6 BB 9 8 C PILEATUS 8 5 B 4 9 5 6. A 4 3 3 8 9 A 3 C 1 6 2 1 9 C B A 2 KLOSSII 2 3 4 2 2 1 3 2 3 2 B 5 8 9 5 9 6 C 6 8 6 3 5 2 7 HOOLOCK 6 8 5 9 4 7 5 6 5 8 7 2 4 2 7 1 5 7 1 3 1 7 2 6 6 CONCOLOR 1 2 2 3 3 2 2 C 2 A 1 4 6 5 A 3 ? 3 C ? 8 6 C 7 9 GABRIELLAEA AAB 6 9 A 5 A C 5 6 5 1 1 4 7 2 9 4 7 8 6 B 5 LEUCOGENYS B C C CC 8 4 3 6 4 2 3 B 4 4 2 B 4 B 7 5 4 I C A SYNDACTYLUSC 4 3 A B C 8 4 CB 6 7 2 3 6 B 3 B 4 2 2 2 3 9 3 HUMAN 7 1 1 1 1 A 1 1 1 IC 1 C C 2 5 8 1 3 B C 1 7 1 1 307 Appendix IV. Non size-corrected (raw) cranial, dental and postcranial data. (See Table 3.3 for details of Variable numbers). If) T f If) O) CD CM Q CM CO M. CM CO h - 00 Q ID 00 CD O O) CD CO ID O) T t CD CD CD CO If) If) If) If) ID CD Z CD If) CD CD If) If) If) z If) If) CD If) z If) CD CD CD CD If) If) If) CD If) If) O) ID T f CO 00 Q ID IDO) ID CM Q ID Q t T CM CD T f CM ID CM N CO If) If) If) If) If) Z If) If) If) If) If) z If) If) If) If) Z If) If) If) ID If) If) If) If) If) CO T f O) h - ID CO CD O) ID O) Tj- ID O) N CO ID CD Q CM O) Q ID CM CO CO CO T t O) CD ID R CD If) If) cd CD CD If) CD If) CD CD ID If) If) CD CD If) Z Cf) CD Z If) CD CD CD CD CD CD If) CD CD CO O) CD CD O) M. O) CO CM 00 CD CM CO T f T f CD ID Q ID Q CO T t CD ID T t ID CD CO 00 % If) If) If) ID If) If) If) If) If) If) If) If) If) If) If) Z ID Z If) If) If) If) If) If) If) If) If) ID O) CD N CD CD ID O) N- ID N N. CO O) CD 00 CM OO Q T t 00 CD CM h - ID CD 00 CO If) If) If) CD If) CD CO If) If) If) CD If) ID If) If) If) If) CD If) ID Z If) If) CD CD If) ID If) If) If) CM O) CM O) CO N CM O) CD N CD 00 N CO 00 O) Û CD O) ID ID CD CD R If) T f If) If) ID If) Tf Tj- T f If) T t T t T f T t If) T f If) T f Tt" z T t T t If) T t -M- T t If) If) 00 If) CM ID N CM ID CO 00 CO O) CO O) IDID O) Q If) CM 00 00 If) CD CO R Tf- "4" If) "4" If) If) "4- T t Tj- If) T f T t T f •M- If) T f T f Z T t If) T t T t T t T t T t T t If) T t CD If) CO N CO CM T t CM T t O) CM CM If) CO CO O) T t Q CM CO CO T t ID CM CO CO T t T t % Tf- Tf- T f T t Tj- cd T f T t T t T f T f T t cd Z T t T t T t T t T t T t T t CM CM 00 N- CD N. O) 00 h - CO ID O) ID N- ID O) ID CD Q h> N CD CD CM CO CM CO CD ID If) T f If) If) If) T f If) T f if) T t If) T t If) If) If) If) ID Z Ifj If) If) If) If) If) If) If) T t h - If) If) Q CM CM CO O) T t h - CM CM 00 CD CD CD CM CD N CO ID 00 N- ID 00 G) 00 Z If) CO "4- 00 If) N T t cd ID N N T t N cd T f CD cd cd CD cd CM (O O) Q O i M- CM ID ID h - T t O) CM M- ID ID N CM CO CD T t O) CD CM CO 00 CD CD Z CD CD N CD CD (D CD 00 CD n ! N- CD CD N cd CD CD N N CD N CD CD If) a> o CO ■M" CD CO CD 00 CM CD CM O) 00 00 T t CD N O N ID ID CO M. CM CD CD z If) If) ID If) If) T f CD CD If) If) If) If) If) If) CD If) If) CD Z If) If) If) CD T t If) If) If) T f CO If) CM T f CD CD CO O) 00 N N N CM CD Tj- CM T t CD 00 Û Û T t N N CO "4" CO ■M" CO T f T t cd cd CO cd cd T t cd -4" T t cd T t cd cd z z T t T t •cd CO CO •M; CM N- CM T t CM T f O) N O) CM O) T f CO Ti CM CO N CM Q Q CO T t CD CM CO CO "4" If) cd If) Tj- cd cd cd T f cd T t ed T t Tj- T t cd T t z Z T t T t T t T t cd CM N- CO N. ID CD CO CO 00 00 ID ID CO CO CM O) 00 O) ID CD 00 Û Q CM N CO co CO "M" If) "4- If) If) Tj- cd t T T f T t Tj- T t "4- T f T f T t T t T t z Z T t If) T t T t T t O) O) M" O) CO h - Ti T f 00 CD CO CO 00 ID N h - O) O) CO O) CD ID ID h - Q Q CD T t T t T t CO CO CO CO CO Tf- cd ed cd cd cd cd cd cd cd cd cd cd cd cd cd cd cd cd Z Z cd cd 2 u_ LL 2 u. LL LL 2 LL Q Q LL 2 LL 2 Q 2 2 LL LL 2 w Z Z Z s a: 0: a: a: Qd Cd Cd Cd Cd Cd Cd Cd Cd .£3 5 5 5 3 § 5 3 3 3 5 §3 5 5 3 5 §5 3 1 1 (0 10) Q 308 SpeciesSex 1 2 3 4 5 6 7 26 27 28 29 30 31 32 33 34 35 LAR F 3.8 4.4 3.9 3.8 5.3 5.6 ND 4.2 4.9 4.4 4.8 5 6.5 5.4 6.2 5.4 6.6 LAR M ND ND 4.2 4 6.3 8.1 15.6 4 5.6 4.6 5.5 5.2 5.9 5.7 6.4 5.4 6.3 LAR M 3.6 4.1 4 3.8 5.1 6.5 15 3.8 5.2 4 4.5 4.8 5.5 5.4 6.4 5.5 5.7 AGILIS F 3.5 3.6 3.6 3 4.8 6.5 13.7 3.9 5.1 4.1 4.9 3.8 5.4 5.2 6.2 4.5 5.7 AGILIS F 3.4 3.7 3.9 2.9 4.5 6.5 14.7 3.3 6.2 3.5 4.3 4.4 5.7 5.1 6.2 4.6 5.2 AGILIS M 4.3 5.8 4.1 4 4.9 6.5 15.6 4.5 5.6 4.9 5.4 5.8 6.7 6.1 5.7 5.8 7.1 AGILIS F 3.8 4.8 4.7 4.1 5.5 6.5 12.2 3.9 6.1 4.2 5 5 5.8 5.5 6.5 4.9 6 AGILIS M 3.9 4.7 4 3.9 4.8 6.9 15 3.9 5.7 4 4.4 4.9 6.1 5.7 6.3 5.4 6 AGILIS F 4 4.2 4.3 3.6 5.2 5.5 11.3 3.9 4.8 4.1 4.3 5.3 6.4 5.4 6.3 5 5.6 AGILIS M 3.7 4.6 4.2 3.8 4.6 5.5 14.9 4.3 5.8 4.2 5.4 5.1 5.8 5.6 6.6 5.7 6.2 AGILIS F ND ND ND ND ND ND ND 3.8 5.2 ND ND 5.4 7 5.8 7.3 5.7 7.3 AGILIS ND 4.3 4.9 4.5 3.6 5.5 7.7 14.9 3.9 5.8 4.1 4.5 5.2 6.2 5.7 6.6 5.4 6 AGILIS M ND ND 4.1 3.5 6.2 7.7 13.2 3.6 6.7 3.5 5.5 4.7 5.5 5 6.4 4.8 6 AGILIS M 4 5 4.1 4.5 4.8 6.6 12.2 4 7.4 4.4 5.3 5.2 6.6 5.2 6.2 5.1 6.4 AGILIS F 3.9 4.4 ND ND ND ND ND 4.4 6.7 3.9 5.2 4.6 5.2 5.1 6.4 5.1 6 AGILIS M 3.7 4.94 3.9 3.8 5.1 6.2 14.7 4.3 5.2 4 5.2 4.9 5.6 5.7 6.6 5.5 6.3 AGILIS F 3.6 5 4 4.1 5.7 7.1 12.2 4.2 5.7 4.1 5 4.8 5.7 5.3 5.6 5 5.2 AGILIS M 4.4 5.3 4.1 4.2 5.1 7 13.3 3.9 6.1 4 5.3 5.2 6 5.6 6.7 5.7 5.5 AGILIS F 3.8 4.6 3.8 3.4 5.3 6.7 12.4 3.6 5.3 3.8 4.8 4.4 6 5 6.1 4.7 5.6 AGILIS M 3.1 3.6 3.2 3.3 6.2 6.6 10.6 3.7 4.5 4.2 5.1 5.2 6.2 5.7 6.5 5.6 6.4 AGILIS M 3.3 3.6 4.1 3.2 5.9 6.4 11.3 4.1 5.5 4.6 4.4 5.3 5.6 5.3 6.4 ND ND AGILIS F 4.1 5.8 4.6 4.7 5.1 7.5 14.5 4.2 5.9 4.4 5 5 6.2 5.8 6.7 5.5 5.7 AGILIS F 4 5 4.3 4.1 5.3 7.4 15.9 4.1 5.4 3.8 4.3 4.3 6.2 4.9 5.9 5 5.5 AGILIS M 3.3 3 3.6 3 5.3 7.4 13.6 4.1 5.3 4.3 4.6 4.5 5.7 5.2 5.9 5 5.3 AGILIS M 3.6 4.3 4.3 3.9 5 7.1 11.2 4.1 5.4 4.2 4.9 4.9 6.2 5.4 ND ND ND AGILIS F 4 5.6 4.2 4.4 4.3 6.1 15 3.8 5.7 4 5.4 4.9 6.8 5.5 6.8 5.3 6.8 AGILIS M 3.9 5 4.4 3.7 ND ND ND 3.8 5.1 3.9 4.5 5.5 6.3 5.9 6.5 5.6 6 AGILIS F 4.1 4.6 4.6 4.5 5.7 8.1 15.3 4.4 6.1 4.6 5.5 4.8 6.3 5.7 7 5.9 6.7 MOLOCH M 4 5 3.6 3.9 ND ND ND 3.9 4.5 4.2 5.4 4.5 4.9 5.9 5.9 5.6 6.6 MOLOCH M 4.2 4.9 4.2 3.6 6 7.5 15.7 4 5.7 4.1 4.7 4.9 6.3 5.5 6.7 5.3 5.9 MOLOCH ND 4.2 4.6 4 4.1 5.7 7.1 16.2 3.9 5.9 4.4 5.2 4.7 6.3 5.5 6.8 5.6 6.2 MOLOCH F 3.8 4 4.1 '4 5.4 6.5 ND 4.1 5.7 4.1 5.2 5.3 5.9 6 6.8 5.7 6.3 MOLOCH M 4.1 5.3 3.8 3.5 5.8 6.9 16 4.1 5.8 4.6 5.1 5.3 6.3 5.9 7.6 5.8 6.4 SpeciesSex 1 2 3 4 5 6 7 26 27 28 29 30 31 32 33 34 35 MOLOCH F 4 4.6 4 4.7 3 4.8 5.1 ND ND 4.5 5.5 5.2 6.6 5.5 6.6 ND ND MOLOCH ND 3.4 4.5 3.4 4.1 4.4 6.7 11.8 4 6.2 3.4 4.5 4.3 6.6 5.2 6.5 5 5.8 MOLOCH M 3.9 4.4 3.8 4.3 4.6 6.4 11.6 4 7.1 4.4 5.4 5.2 6.7 6.1 7.7 ND ND MOLOCH M 3.8 4.8 3.5 4.5 3.1 4.8 3.4 4.5 5.4 4.3 5.8 5.7 6.8 ND ND ND ND MOLOCH F 4.2 3.8 3.6 3.2 6.2 6.8 10 4.1 6 4.1 4.6 4.6 5.4 5.1 5.9 5.2 6.9 MOLOCH M 3.9 4.6 3.8 3.3 4.7 6.1 12.8 3.1 6.3 3.7 4.7 5.2 6.4 5.3 7 4.5 5.8 MOLOCH F 3.9 4.6 3.9 3.6 4.6 6.2 13.3 4 5.6 4 4.6 4.6 6.2 5.3 6.5 5.3 5.8 MOLOCH M 3.8 4.5 3.9 3.8 5.1 6.9 14.9 4.1 4.9 3.9 5 4.9 6.1 5 6.2 5.3 6.2 MOLOCH F ND ND ND ND ND ND ND 3.7 5.1 3.9 4.7 4.4 5.6 5.1 6.3 4.8 5.4 MUELLERI M 3.9 4.7 ND ND 6 7.4 10.3 3.7 5.4 3.8 4.7 4.8 6.2 4.8 6.1 5.1 5.8 MUELLERI ND 3.9 4 3.8 3.8 5.6 7.3 14.2 3.7 5.4 3.8 4.7 4.4 6.1 5.1 6.5 5.5 6 MUELLERI F 3.8 4.5 4 3.6 5.2 6.5 12.6 3.9 5.3 3.8 4.2 4.6 5.7 5 5.9 5.1 5.2 MUELLERI F 4.1 5.5 4.1 4.2 5.9 5.7 13.5 4.2 5.4 4.6 5.5 5.7 6.9 6.2 7 5.9 6.3 MUELLERI M 4.14 4.3 ND ND 6.2 7.4 15.9 4.4 5.9 5 5.6 5.7 6.7 6.3 7.1 6.5 6.8 MUELLERI M 4.2 5.4 4.3 3.6 6.1 7.5 14.9 4.2 5.6 4.5 5.4 5 6.6 5.9 6.4 5.5 6 MUELLERI M 3.8 4.7 3.6 ND 5 7.1 11.1 4 5.9 3.9 4.6 5.1 6.4 5.4 6.3 ND ND MUELLERI F 3.9 4.8 3.9 4.1 6.5 8.1 16.2 ND ND ND ND 5 6.2 5.7 6.8 ND ND MUELLERI ND ND ND ND ND ND ND ND 3.5 5.1 3.8 4.4 3.9 5.9 4.7 6.2 4.9 6.2 MUELLERI M 3.6 5.1 3.9 3.8 5.3 6.9 13.6 3.9 5.3 4.3 5.3 4.6 6.4 5.6 6.5 5.5 6.5 MUELLERI F ND ND ND ND 5.3 6.9 17.6 3.8 5.1 4.1 4.9 5 6.4 5.7 6.8 5.4 6 MUELLERI M 4.2 5.2 4.1 3.7 5.9 7.5 13.8 3.9 5.3 4.3 4.9 5.2 6.3 5.8 6.6 5.1 6.3 MUELLERI M 4.4 5.4 4.5 4.1 5.6 7.3 13.8 4.5 6.2 4.6 5.5 5.4 6.5 6 7 ND ND MUELLERI M 4 4.8 4.1 3.9 6.3 8.8 14.2 5.4 5.9 4.9 4.9 5 6.5 5.5 6.8 5.6 6.2 MUELLERI F 3.5 4.9 3.5 3.5 5 6.9 13.7 ND ND 4.3 4.7 5.3 6.7 6 7.2 5.5 6.2 MUELLERI F 4.1 4.7 4.2 3.3 5.1 6.4 13.5 3.9 5.3 4.1 4.8 4.9 6.2 5.8 6 6.3 5.1 MUELLERI F 3.5 4.3 4.3 3.5 5.1 6.5 13.9 3.5 5.3 3.7 4.8 4.5 5.1 5 5.8 4.3 4.9 MUELLERI M 4.2 5 4.2 3.8 5.5 6.8 14 5 5.9 4.4 5.2 4.5 6.1 5.4 6.5 5.8 6.9 MUELLERI M 4.7 5 4.3 4.3 6 7.7 17.9 4.3 5.8 3.9 5.1 5.4 6.3 6.2 6.6 5.6 6.9 MUELLERI M 4.4 5.6 4.4 4.1 5.3 7.9 18.1 4.3 6 4.7 4.9 5.2 6.5 5.8 6.7 5.9 6.5 MUELLERI M 3.7 5.1 4.7 4.3 5.9 7 13.6 4.2 5.7 4.5 5.3 4.6 6 5.6 6.6 5.6 6.4 MUELLERI M 3.8 4.7 4.9 4 6.4 7.2 16.8 4.3 6 4.3 5.9 5.3 6.4 6.5 7.1 6.1 6.4 MUELLERI M 3.7 4.5 3.9 3.8 5.7 7.8 15.7 4.2 5.3 4.3 4.5 5 5.7 5.4 6 5.3 5.7 MUELLERI M 4 4.9 3.9 3.9 5.5 7.5 17.3 4.6 5.4 4.4 4.9 5.2 5.8 5.7 6.2 5.5 5.4 Species Sex 1 2 3 4 5 6 7 26 27 28 29 30 31 32 33 34 35 MUELLERI M 4.3 4.6 3.8 3.4 4.3 6.6 15.3 ND ND 4.2 4.7 5.1 5.5 5.5 6.3 ND ND MUELLERI F 4 4.6 3.7 3.3 5 6.8 13.5 3.8 4.8 4.2 4.8 4.6 5.4 5.3 6.6 4.9 5.3 MUELLERI F 4.2 4.3 4.3 3.8 5.6 7.6 15.5 4 5.7 4.4 5.1 5.3 6.4 5.7 5.6 5.6 5.9 MUELLERI F 4 5.2 4.4 4.2 4.6 6.8 15.6 4.4 6 4.6 4.7 5.1 6.2 5.4 6.7 5.8 6.5 MUELLERI M 4.1 4.7 4.3 3.7 5.6 7.5 14.3 4.4 5.8 4.9 5.3 5.2 5.6 5.7 6.2 5.8 6.8 MUELLERI F 4.2 5.5 4.6 4.6 5.8 6.8 15.9 4.5 5.8 4.5 5.9 5.9 6.6 6.5 7.1 6.1 6.6 MUELLERI F 3.8 4.2 3.8 3.6 4.7 5.9 12.9 4 4.9 4 4.4 4.4 5.7 5.2 5.7 5 5 PILEATUS F 4 4.6 4 3.6 5.3 7.8 12 3.6 5.6 3.9 4.9 4.8 5.9 5.4 6.4 5.6 6.3 PILEATUS M 3.7 5.3 3.7 4 5.7 7.3 14.5 3.5 6.5 4 4.7 5.2 6.1 5.6 6.9 5.8 7 PILEATUS M ND ND 3.6 3.2 5 6.5 14.3 3.5 6.2 3.7 4.4 4.3 5.8 5.2 6 5.5 6.1 PILEATUS M 3.4 ND 4 3.7 5.5 8 14.6 3.5 6.8 3.6 4.6 4.3 5.9 5.4 6.4 ND ND PILEATUS M 3.6 4.7 3.6 4 6 6.2 12.8 4 5.9 3.9 4.8 5.1 6.4 5.5 6.3 5 6.1 PILEATUS F 3.5 4 3.7 3.6 4.8 6.7 14.7 3.6 5.8 3.9 4.4 4.6 5.7 5 5.8 5.2 5.2 PILEATUS M 4 5 3.8 4.2 5.6 7.6 15.6 3.7 5.9 4.1 4.8 4.8 6.4 5.5 6.4 5.3 6.3 PILEATUS M 4.3 5.2 3.9 4.3 6.2 8.9 19.3 4.1 6.8 4.1 4.9 5.4 6.5 6 6.8 5.9 6.4 PILEATUS M 3.9 4.9 3.9 3.6 5.5 7 14.5 3.7 6.3 4 5 4.8 6.2 5.2 6.2 5.8 6.3 PILEATUS F 3.8 4.5 3.4 3.7 5.5 6.8 14.2 3.6 6.1 3.9 4.6 4.8 6.2 5.6 6.8 5.4 6.1 PILEATUS F 3.8 4.5 3.9 3.7 5.8 6.8 14.1 3.9 6.4 4.3 4.6 5.6 6.8 5.2 6.6 4.9 6.3 KLOSSII F 4 3.7 3.1 3 5.8 7.2 12.3 3.6 5.4 4 5 4.8 5.9 5 5.9 4.7 5.5 KLOSSII M 3.9 4.6 3.5 3.4 5.7 7.3 14.2 3.5 5.9 3.9 5.2 4.6 5.5 4.9 6.2 4.6 5 KLOSSII F 4 4.4 3.4 3.9 5.7 8 14.8 3.5 6.3 3.8 4.5 4.3 5.7 5 6.3 4.7 4.8 KLOSSII M 3.7 4.5 3.5 3.7 5.3 7.1 11.5 3.7 6.8 4.1 4.9 4.7 5.9 5 5.8 4.7 5.4 KLOSSII M 3.7 4.5 3.2 3.6 5.5 7.8 15.9 3.8 6.5 3.8 4.7 4.5 6.3 5.2 6.5 4.9 6.1 KLOSSII M 4.2 4.2 3.5 3.4 5.4 8.3 14.7 3.7 7.1 3.7 4.7 4.3 5.4 4.9 6 4.7 4.9 KLOSSII F 3.8 3.2 3.7 2.9 5.2 6.8 15.1 3.8 7.1 3.7 4.5 4 5.1 4.3 5.5 4.1 4.5 KLOSSII M 3.6 5 3.2 3.5 5.3 7.2 17.7 3.9 6.2 4 4.5 4.6 6.2 5.3 6.2 4.9 5.5 KLOSSII M 3.4 4.2 3.2 3.2 5.2 6.8 14.9 3.4 5.6 3.6 4.5 4.3 5.8 4.8 6.1 4.6 5.4 KLOSSII F 3.4 4.2 3.5 3.2 5.2 6.9 11.2 3.6 5.7 3.6 4.3 4.4 5.8 4.8 6 4.7 5.1 KLOSSII F 3.4 3.8 3.2 3.3 5.3 6.5 14.7 3.5 5.3 3.8 4.6 4.7 6 5.1 5.7 4.7 5.3 KLOSSII M 3.4 3.9 3.2 3.5 5.7 6.6 12.3 3.8 5.7 3.9 4.6 4.8 6.3 5.1 6.4 5 5.8 KLOSSII F 4 4.7 3.8 3.7 6.1 7 13.6 3.8 5.5 4.2 4.8 4.9 6.6 5.5 6.8 5.1 5.3 KLOSSII F 4 4.6 3.8 3.2 4.2 6.1 13.5 3.7 5.5 3.8 4.4 4.8 5.9 5.1 6.2 4.6 5.5 HOOLOCK F 3.7 4.3 3.6 4.1 6 7.6 13.4 4.8 6.8 4.6 5.7 5.7 7.2 6.7 8.9 6.6 8.2 SpeciesSex 1 2 3 4 5 6 7 26 27 28 29 30 31 32 33 34 35 HOOLOCK M 4.1 5.3 4 4.7 6.5 8.9 15.9 ND ND ND ND ND ND ND ND ND ND HOOLOCK F 4.2 5.5 4.2 4.4 5.8 8 15.3 4 5.7 4.6 4.9 5.6 6.8 6.1 7.5 12 7.5 HOOLOCK F 3.7 5.2 ND ND 6.2 8 16.2 4.3 6.3 4.6 5.4 5.8 6.5 6.4 7.5 6.6 6.3 HOOLOCK F 3.6 4.4 3.4 4 5.6 6.8 13.4 4.2 6.5 4.2 5.5 5.6 7.1 6.4 8 6.3 7.4 HOOLOCK M 3.8 5 3.7 4.4 6.1 8 14.3 4.3 6.3 4.7 5.7 5.9 7.4 7.2 8.6 6.9 7.7 HOOLOCK M 3.9 4.9 3.8 4.4 5.8 7.9 16 3.9 6.4 4.3 5.8 5.7 6.9 6.5 7.9 6.2 7.4 HOOLOCK M 4.1 5.1 3.2 4 6.3 8.2 18.6 4.2 6.1 4.5 5.9 5.5 6.9 6.2 7.8 6.3 7.5 HOOLOCK M 4.2 4.7 3.9 3.7 6.4 8 19.8 4.4 6.3 4.4 6 5.2 6.4 6.1 7.5 6.2 7 HOOLOCK F 4 4.8 3.4 3.9 5.4 7.2 15.5 4 6.1 4.2 5.5 5.6 6.5 6.5 7.3 6.2 7.4 HOOLOCK M 3.5 4.2 3.8 3.5 6.2 7.7 14.7 4.6 6.7 4.8 5.6 5.5 6.7 6.6 8.1 6.3 7.6 HOOLOCK M 3.7 5.1 3.3 4.4 5.3 7 15.5 4.1 6.3 4.3 5.7 5.6 6.8 6.3 8 6.4 7.3 HOOLOCK M 3.3 5 3 3.3 5.1 7.7 17.4 3.6 5.8 4 5.2 5.3 6.8 5.7 7.4 5.3 6.6 HOOLOCK ND 4.2 5.5 3.7 4.2 ND ND ND 4.2 6 4.5 5.5 5.6 6.8 6.1 7.6 5.9 7 HOOLOCK M 3.8 5.2 3.8 3.9 5.3 7.7 16.3 4.3 5.9 4.8 5.7 5.5 6.8 6.4 7.8 6.6 7.7 HOOLOCK F 3.4 4.2 2.9 3.7 4.7 7.1 15.7 3.5 5.9 4.4 5.3 5.4 7.1 5.6 7.5 6.1 6.7 CONCOLOR F 4.2 5.8 4.1 4.2 6.1 7.1 16.7 4.5 7.1 5 6 5.2 7.2 5.9 7 6.1 6.7 CONCOLOR M 3.4 5.1 3.7 3.9 5.3 8.2 16 4.3 6.8 4.6 5.6 5.1 6.7 6.1 7.4 6 6.2 CONCOLOR F ND ND ND ND 6.1 6.1 ND 3.9 6.3 4.3 5.2 4.3 5.7 5 6.6 4.9 5.9 GABRIELLAE M 4 4.4 4.3 4 5.3 7.1 17.7 ND ND ND ND ND ND ND ND ND ND GABRIELLAE M 3.8 4.78 3.8 4.41 4.7 7.4 15 4.7 5.43 4.1 4.98 4.1 5.51 4.87 6.76 4.86 5.32 GABRIELLAE F 3.92 4.61 4.23 3.81 5.59 7.32 13.24 4.17 5.48 4.74 5.07 5 6.45 5.6 7.21 5.38 5.93 GABRIELLAE F 3.15 3.67 3.54 3.89 4.38 6.31 11.84 3.96 5.82 4.05 4.79 3.96 5.81 4.69 6.18 4.82 6.07 GABRIELLAE M 3.73 5.19 4.2 5.1 4.98 7.54 13.16 5.78 5.28 4.58 5.43 5.04 6.53 5.44 6.52 5.67 6.26 GABRIELLAE M 3.38 4.56 2.83 3.93 3.26 4.52 4.15 ND ND ND ND 3.1 5.83 3.9 5.95 4.6 6.65 GABRIELLAE F 3.8 4.5 3.7 4.1 6.9 8.6 13.8 4.4 7.6 4.4 5.4 4.9 6.3 6 7.2 5.2 7.2 GABRIELLAE M 3.6 4.8 4 4.7 4.7 8.4 14.7 4.5 6.6 4.6 5.7 6.1 6.5 6.2 7.5 5.7 6 GABRIELLAE F 3.8 4.6 4.2 4 5.1 6.8 14.8 4.5 5.8 4.4 5 5.2 6.6 5.6 6.9 4.7 5.7 GABRIELLAE F 3.2 4.3 3.6 3.5 4.8 5.6 15.4 ND ND ND ND ND ND ND ND ND ND GABRIELLAE M 3.5 4.5 3.7 3.8 5.7 6 13.1 4.3 5.8 4 5.1 4.9 6.9 5.5 6.7 5.1 5.8 GABRIELLAE M 3.6 4.6 4.1 4.6 5.1 6.5 17.1 4.4 7.5 4 4.8 4.8 6.7 5.5 6.6 5.2 5.9 GABRIELLAE M 3.4 4.6 4.1 4 5.3 7.4 17.2 4.7 6.8 4.1 5.3 4.5 6.4 5.4 6.9 5.1 6 GABRIELLAE M 3.7 5 4.4 4.2 5.4 8.5 17.6 4.4 6.7 4.3 5 4.8 5.8 5.4 6.9 5.4 6.3 GABRIELLAE F 3.5 4.3 4.1 3.7 5.4 7.1 11.4 4.6 5.8 4.4 4.9 4.7 6.6 5.5 6.9 5.3 6 Species Sex 1 2 3 4 5 6 7 26 27 28 29 30 31 32 33 34 35 LEUCOGENYS F 3.4 3.6 3.3 3.5 6.8 7.2 12.1 4.2 6.3 4.4 5.4 5 6.6 ND ND 5.4 6.8 LEUCOGENYS M 3.6 4.5 3.8 3.6 5.1 8.2 17.6 3.9 7.5 3.7 5.2 4.5 6.8 4.8 7.1 5.4 6.6 LEUCOGENYS F 4.2 5.4 4.2 4.1 5.6 7.4 14.8 4.5 5.7 4.5 5 5.1 6.9 5.9 7.5 5.9 6.9 LEUCOGENYS F ND 4.2 4 3.8 4.4 6.3 13.1 4.2 5.4 4.5 4.9 5.1 6.4 5.9 7.3 5.7 6.6 LEUCOGENYS F 3.5 4.6 3.6 3.8 5.2 7.1 16.9 4.2 6.9 4 4.9 4.3 7.3 4.4 6.4 5.2 6.7 LEUCOGENYS M 3.9 4.5 4 4.1 7.1 8 20 4.2 6.4 4.3 5.7 5 7.4 5.7 8.1 5.8 6.8 LEUCOGENYS F 3.8 5.2 3.5 3.8 4.3 6 14 3.9 6.9 4 4.9 4.8 6.9 5.8 6.9 5.9 6.8 LEUCOGENYS F 3.9 4.8 4.3 4.5 6 7.8 16.7 4.7 7.7 4.9 6.2 5.1 7.2 5.8 7.6 5.5 6.6 LEUCOGENYS M 3.4 4.4 3.9 3.6 5.9 8.8 12.1 4.1 6.8 4.3 4.6 5.3 6.5 5.5 6.1 5.3 7.2 LEUCOGENYS F 4.1 5.2 4.4 4.1 4.4 7.4 14.5 ND ND 3.9 5.6 4.8 6.7 5.7 7 5.4 6.5 LEUCOGENYS F 3.8 3.5 4.2 4 5.7 6.7 18.1 4.3 6.2 4.1 4.3 5.1 6.8 5.7 7 5.4 7.1 LEUCOGENYS F 3.7 3.2 3.6 3.4 5.4 7 14.7 3.9 6.3 4 4.9 4.8 6.3 4.9 7.4 4.7 6.3 SYNDACTYLUS M ND ND ND ND 5.9 7.1 14.1 4.6 6.3 4.8 5.5 5.3 6.5 6.8 ND 5.1 8 SYNDACTYLUS M 4.3 4.5 5.1 4.4 7.9 7.7 15.2 4.5 7 5.1 6 5.4 6.9 5.8 8.1 6 7.9 SYNDACTYLUS F 4.4 5.1 4.6 4.5 5.5 7.3 11.3 4.7 6.7 ND 6.1 5.7 7.2 6.5 8 6.8 8.2 SYNDACTYLUS F 4.1 4.9 4.6 4.3 4.9 6.4 12.6 4.6 7.1 4.8 5.5 5.2 7.2 6.1 7.5 6.6 7.8 SYNDACTYLUS M ND ND ND ND 6.7 7.7 19.2 4.4 5.7 4 7.2 5.1 7 5.9 7.6 5.3 6.5 SYNDACTYLUS M 4.6 5.8 4.9 4.5 6.1 8.4 19.4 5 6.8 5 6.8 5.7 7.8 6.2 8.4 6.5 9.3 SYNDACTYLUS M 4.5 5.1 4.9 4.5 6.7 8.5 19.4 5.3 7.1 5.2 6.5 6.2 7.3 6.7 8.6 7 8.4 SYNDACTYLUS F 4.5 5.9 4.9 4.6 5.6 7.7 13.4 5 6.6 5.2 7.1 6.8 8.4 7.2 9.1 7.2 9.6 SYNDACTYLUS ND 4.3 5.2 4.7 4.8 6.1 7.5 15.8 4.9 6.7 5.2 6.6 5.3 7.9 6.5 8.9 6.6 8.2 SYNDACTYLUS ND 4.4 4.9 4.7 4.4 6.9 8.3 16 4.9 7.1 4.9 6.3 5.9 8.4 6.5 8.7 6.3 9.4 SYNDACTYLUS F 4.3 4.7 4.7 4 6.3 7.6 16.5 5 6.2 4.8 6.1 5.6 7.6 6.3 8 6 7.8 SYNDACTYLUS F 4.6 4.9 4.9 4.4 7 9 17.9 5.1 8.4 5.2 5.9 5.5 7.8 7 8.8 6.6 7.5 SYNDACTYLUS F 5.1 5.7 5.4 4.9 6.4 7.8 16.6 5.1 6.8 5.2 5.7 6.2 8.4 7.4 9.2 7.9 9.2 SYNDACTYLUS M 4.7 4.6 4.6 4.1 7.6 9.9 21.6 5.3 7.5 5.2 6.9 5.5 8 6.2 8.7 6.8 8.6 SYNDACTYLUS F 4.8 5.6 5.2 5.2 4 5.1 3.5 5.1 8.1 5 6.7 5.7 8.1 ND ND ND ND SYNDACTYLUS F 4.3 4.5 5 4.2 6.8 8.6 16.4 4.8 6.9 4.8 6.9 5.1 7.8 6.6 8.7 6.5 8.4 SYNDACTYLUS M ND ND 4.2 4 6.2 7.4 14.3 4 7.4 4.3 5.7 5.2 7 5.8 8.1 5.8 7.7 SYNDACTYLUS F 4.3 4.8 4.8 4.6 6.5 8.2 14.1 4.7 7.2 4.8 6 6 8.4 7 9.4 6.4 7.9 SYNDACTYLUS M 4.6 5.9 ND ND 6.8 7.1 15 4.7 8.3 5 6.3 5.9 8.5 7.4 8.1 7.2 8.5 SYNDACTYLUS F 4.3 4.3 4.7 i.8 5.3 6.6 14.3 4.2 6.7 4 6.1 5 7.2 5.1 7.6 5.9 7.4 SYNDACTYLUS M 4.5 5.7 ND ND 6.6 7.4 14.8 5 7.2 5.5 6.7 6.7 8.5 7.7 9.3 7.3 9.2 Dental Variables Species Sex 1 2 3 4 5 6 7 26 27 28 29 30 31 32 33 34 35 HUMAN F 10.9 7.7 9 7.7 7.4 7.6 7.56.6 5.4 7.2 6 9.4 9.3 9.4 8.5 9.4 8.3 HUMAN F ND ND ND ND ND ND ND 8.3 7.5 8.7 7.3 10.5 11.7 10.3 10.5 ND ND HUMAN F 8.6 7.6 7.4 6 ND 8.1 7.7 6.9 6.9 7.56.4 9.8 10.2 9.3 9.7 ND ND HUMAN M ND ND ND ND ND 8.3 7.6 ND ND ND ND ND ND ND ND 9.7 10.3 HUMAN M 7.9 7.2 8 6.3 7.3 8.4 7 ND ND ND ND ND ND ND ND ND ND HUMAN M 10 6.8 8.2 6.1 5.3 8.6 7.6 7.97.3 8.5 7.5 9.9 10.8 9.7 10.1 9.8 11.3 HUMAN F 11.3 7.4 9.1 6.2 7 8.3 8.1 7.5 6.7 8.1 7.3 10.4 11.8 10.5 11 ND ND HUMAN M 9.8 7.4 7.3 6.8 6.4 8.5 7.8 8.5 7 8.66.4 9.9 ND 10.6 10.3 9.5 9.6 HUMAN F 8.8 ND ND 6.1 6.5 7.4 7.1 7 6.7 7.3 6.4 10.5 11.4 9.7 9.9 10.2 10.6 HUMAN M 8.5 7.1 7.4 6.7 5.7 8.9 7.5 7.3 6.3 8 7.2 10.8 10.5 10.6 10.2 ND ND HUMAN M ND 6.9 ND ND ND 8.6 ND 7.8 6.9 ND 7.8 10.6 11.5 10.6 10.6 10.3 10.9 u> Cranial Variables SpeciesSex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 LAR M 23.4 23.2 12.1 27.1 11.6 75.1 63.7 77.7 38.9 21.5 19.5 11.2 55.3 39.8 79.2 64.5 21.6 35.9 LAR F 23.7 23.2 9.6 24.8 9.8 75.5 61.7 74.5 38 21.3 17.8 10 54.9 58.5 80.3 65.1 19 37 LAR M 24 21.1 12.7 26.8 12.4 68.4 62.2 74.3 35.2 19.4 14.2 9.6 55.6 46.9 80.1 59.8 20.6 33 LAR M 22.8 22.1 10.5 24.8 12.9 61.8 60.7 71.6 35.6 17.8 17.2 11.7 48.6 59 79.2 62.1 23.1 33.3 LAR F 24 24.2 12.4 26.4 12.7 67 60.7 73.3 37.7 21.1 18.3 10.6 55 35.5 77.2 61.7 15.3 29.5 LAR M 22.4 22.2 12.8 26.1 16.8 69.6 61.4 77.3 36.8 20.2 20.3 10.8 50.6 60.4 75.1 61 20.2 32.9 LAR M 24 22 14 27.7 12.7 71.4 60.3 75.6 37.6 19.7 18.2 9.8 48.1 45 76.5 62.3 15.3 28.4 LAR M 21.8 20.9 12.9 25.8 12.3 69.3 58.6 70.6 37.2 18.5 18 10.6 50.7 45.5 78.6 60.5 16.3 30.9 LAR M 23.7 22.6 12.2 24.6 11.3 67.7 59.1 70.6 37 18.1 15.8 9.2 58.3 37.6 80.2 59.1 19.7 32.4 LAR F 23.6 24.4 11.5 24.9 11.9 66.5 60 71.8 36.3 20.1 17.2 10.6 53.3 36.1 72.5 59.3 16.9 32.5 LAR F 22.5 24.4 15.3 28.8 15 70.7 64.7 77.5 37.4 21.9 19.5 12.6 57.4 54.5 85 64.5 15.4 32.7 LAR M 23.9 21.9 14.1 24 12.3 71.9 62.7 74.6 36.4 19.6 16.7 11.1 55.4 46.2 86.1 66.2 16.8 31.3 LAR F 21.7 20.6 10.4 21.7 12.4 65.5 58.1 69.4 36.2 18.6 17.2 7.9 54.3 55.4 77.8 56.9 18.9 35.2 LAR M 24.6 23.7 13.3 27 14.4 77.2 62.3 80.2 40.6 21.7 19.4 12.6 50.6 58.8 81.2 65.7 16.6 31.7 LAR M 23.2 22.5 12 25.8 14.4 64.6 59.4 73.3 37.4 22.3 18.9 10.1 ND ND ND 55.7 ND ND u> LAR F 24.1 23.3 11.6 26.8 13.5 67.5 66.9 79.6 40.6 20 18.4 9.4 51.9 53.1 82.7 63.6 14.1 28.4 U\ LAR ND 22.8 22.2 13.5 25.2 14 70.5 62.1 77.4 40.1 20.5 18.5 10 ND 58.4 ND 64.4 ND ND LAR ND 22.7 23 12.4 25.1 13.6 67.4 60.7 72.3 35.6 17.8 16.8 10.7 50.7 53.1 ND 58.3 ND ND LAR M 22.5 24.8 13.6 27.4 13.3 71.8 60.1 80 38.7 21.2 15.9 12.5 53.3 58.8 85.4 63.1 15.8 33 LAR F 23 24 12.7 23.8 12.6 69.9 64.4 76.3 38.8 21.3 17.3 10 53.3 55.3 80.4 62.2 15.9 34.1 LAR M 23.9 21.5 13.2 26.4 14 71 61.6 76.3 37.3 20.4 18.1 12.5 52.9 58.1 78.2 59.7 13.3 32.1 LAR M 24 21.1 12.5 23.6 13.2 71.7 67.2 79.3 40 19.5 18.5 12.2 56.4 52.5 83.6 64.3 14.9 33.7 LAR M 23.3 22.5 12.9 24.3 12.8 70.4 61.9 76.2 36.8 19.3 15.7 10.4 50.3 56.3 80.5 62 17.2 31.1 LAR F 23.2 23.2 11.3 23.2 11.7 63.1 60.7 71.5 33.7 17.6 14.9 10.3 49.9 53.5 78.1 61.2 16.2 32.6 LAR M 21.3 22.1 9.4 24 13.2 64 60.1 74.9 39 18.7 15.4 10.7 50.3 49.6 77 62.5 19 31.9 LAR ND 21.2 22.1 11.6 23.1 12.2 66.6 61.6 73.1 41.2 17.7 16.5 9.6 ND 54.5 ND 62.8 ND ND LAR M 21.7 23.2 13.6 29 14 72.9 64.9 77.8 38.7 20 20.3 13.4 52.9 54.5 77.4 65.5 10.9 31.2 LAR M 24 22.7 15 27.6 15 75.6 63.8 79.9 37.4 22.8 20.8 12.1 52.3 57.1 78.9 63.8 15.8 36 LAR F 23.1 22.2 14.6 25.3 13.3 68.1 63.7 79.5 39.7 18.3 16.8 10 49.9 54.9 82 63.1 16.7 31.8 LAR M 23.9 22.5 12.5 26 14.1 72.2 62.7 77.9 38.7 19.9 18.2 12.5 54.1 53.9 79.6 62.5 14.6 33.8 LAR F 22.1 21.1 13.9 25.3 13.8 74.3 66.4 79.2 40.6 20.8 18.3 11.6 58.9 43.5 82.2 67.5 17.6 35.8 LAR M 22.8 23.3 12 25.3 13 71.9 63.9 76.2 38.9 22 18.7 12.2 51.3 56 81.1 65.2 15 30.3 SpeciesSex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 LAR F 21.9 22.9 11.6 24.4 13 67.6 60.3 74.6 38.9 18.1 19.1 10.2 53.3 54.8 76.6 61.8 16.8 33.2 LAR M 23.9 23.3 13.9 23.7 14.2 70.3 62.4 76.4 39.2 19.2 18.7 10.4 51.4 53.2 78 59 15 29.9 LAR M 22.9 22.4 13.5 23 10.3 69.4 60.4 70.4 34.8 17.7 17.1 9.6 54.2 50.5 78.1 59.2 15.8 33 AGILIS F 21.9 21.9 11.6 26 13.4 64.7 92.8 72.8 36.7 20.1 19.3 10.7 52.3 62.9 ND 61.9 ND ND AGILIS F 23.7 21.3 11.5 26 14.4 68.9 60.1 73.3 39.6 18.9 18.8 11.2 52.4 47.5 80.6 59 15.7 30.2 AGILIS M 26 23.6 12.4 26.2 14 68.3 61.9 74 36.7 20.4 19.5 11.1 53.6 59.7 81.2 61.1 17.1 32.7 AGILIS F 25.1 21.9 9.7 27.3 11.5 63.1 59.8 70.4 37.9 18.7 16 11 49.5 53.3 74.6 57.5 18 34.1 AGILIS M 24.5 21.5 11.5 26.7 14.3 67.1 60.8 72.2 36.6 19.4 18 11.2 47.1 59.5 77.4 59 11.9 28.8 AGILIS F 25 25.2 10.1 26 12.2 64.9 59.3 71.4 35.3 18.8 15.5 10.9 50.1 52.1 79 60.8 15.9 32.3 AGILIS M 23.4 23.8 10.1 28.2 14.3 64.9 57.7 72.2 37.2 18.6 18.6 10.2 53.8 52 81.6 64.4 17.8 33.6 AGILIS F 22.4 22.5 13.4 25.6 15.6 69.9 60.7 75.7 40.3 19.3 16.8 10.6 49.2 58.9 81.2 62.8 14.5 30.4 AGILIS ND 23.6 22.9 10.9 27 17.7 70.6 60.5 77.2 40.5 19.3 18.1 9.7 56.3 47.1 81.4 61.4 15.7 35.2 AGILIS M 24 22.6 13.2 23.1 15.6 68.1 64.8 77.4 41 20.4 17.1 10.3 53.3 44.9 83.6 63.4 13.9 25.4 AGILIS M 25.5 23.9 12.9 27 12.9 64.3 62.5 76.4 37.9 19.1 17.6 11.4 51.7 60 79 59.3 16.4 29.5 AGILIS F 22.8 22.1 11.6 21.2 14.3 59.8 57.2 68.1 34.5 18.4 15.6 10.1 ND 48.6 77.2 53.1 18.4 29.7 AGILIS M 23.4 23.1 10.4 30.5 13.5 64.2 57.6 73.1 39 21.2 16.3 11.2 50.4 57.7 76.8 58.1 21.2 31 AGILIS F 22.5 20.8 11.8 24.7 12.3 61.3 57.6 70 39.4 19.4 16.3 11.1 45.1 56.4 79 57.1 19.6 30.2 AGILIS M 24.5 22.6 12 24.7 13.7 67.2 63.4 78.2 44.2 22.6 19.4 12.6 50.5 61.1 84.9 58.9 18 30.2 AGILIS F 23.7 23.3 11.3 24.4 12.5 67.3 64.7 76.1 38.4 22.2 18.8 12.3 52.1 60.4 81.2 61.1 21.8 29.3 AGILIS M 23.1 22.4 11.7 25.7 13.7 ND 57 75.1 40 20 19.1 12 51.6 48.7 80.8 60.2 ND ND AGILIS M 26.1 24 11.5 27.9 15.8 70.1 60.8 76.5 38.7 22.7 18.9 10.4 48.6 48.1 80.6 62.2 18.2 29.1 AGILIS F 23.5 22.2 11.3 24.2 12.6 67.7 61 72.9 40.1 19.2 18.8 11.5 52.3 55.9 79.3 59 20 30 AGILIS F 25.3 23.4 12.3 26.3 14.3 66 63.5 74.8 38.9 21.7 18.1 12.1 53.3 61.3 82.8 56.7 22.7 31.3 AGILIS M 25 22.7 13.4 26.2 14.7 72.5 63.9 76.3 39.1 19 16.8 10.6 56.6 52.6 83.8 65.5 22.2 30 AGILIS M 23.8 21.3 12.2 21.6 13.2 65.8 60.7 73.8 40.4 20.3 17.8 11.7 53.9 53.2 80.7 60.6 19.5 29.4 AGILIS F 22.4 21.8 12.5 20.7 13.4 65.5 61.7 71.2 38.7 22.1 18.2 10.5 ND ND ND 57.5 ND ND AGILIS M 21.8 20.6 9.8 23.9 11 62.6 61 71.5 38 18.1 14.6 11.6 ND ND ND 57.7 ND ND AGILIS F 24.8 21.6 10.7 25.6 14.2 66.8 63 78 42 21.1 19.3 10.8 ND ND ND ND ND ND MOLOCH M 21.8 22.5 11.6 25.7 12.2 69.1 59.5 71.1 36.3 18.8 21.6 11.2 53.6 44.4 77.7 59.6 18.9 34.8 MOLOCH M 22.3 21.4 10.1 23.1 12.5 70.5 60.2 73.5 38.7 18.8 19.5 10 52.8 56.7 75.3 58.5 19.3 30.4 MOLOCH ND 23.8 24.4 12.7 23.3 13.8 71 62.7 77.4 36.4 22 19.3 12.3 50.8 50.2 81.4 60.1 23.4 36.2 MOLOCH F 21.2 22.7 10.8 21.4 12.4 66 61.2 72.4 39.7 21.5 18.7 10.3 55 48.5 78.1 60.9 16.5 33.7 MOLOCH M 23.3 22.6 10.6 22 14 72.2 62.7 75.5 37.7 19 18.9 12.3 53.2 53.7 81.5 62.2 18.1 30.5 SpeciesSex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 MOLOCH F 21.6 22.4 10.6 22.3 13.6 61 58.5 66.6 34.3 17 17.8 7.2 51.6 52.3 75.7 56.1 ND 31.4 MOLOCH ND 22.7 21.7 10.9 24.6 12.2 62.4 58.9 69.9 34 19.4 14.8 8.5 49.5 57.6 79.4 52.6 19.7 26.6 MOLOCH M 22.8 23.6 12.6 27 16 67.6 63.8 77 37.8 19.8 15.5 11.6 ND ND ND 62.2 12 25.4 MOLOCH M 20.6 20.6 8.6 20.6 10.3 55.8 55.4 61.2 31.1 16.2 19 9.1 49.9 52.5 72.8 51 16.4 28.5 MOLOCH F 21.8 23.2 9.3 22.1 14.8 ND 59 70.5 36.9 20.2 18.3 10.7 51.8 47.8 77.5 56.5 9.4 21.5 MOLOCH M 23 23.3 13 26 14.3 ND 61.2 69.9 33.2 20.5 17.1 9 52.9 57.7 82.5 61.6 23.1 33.3 MOLOCH F 22.2 22.8 9.2 22.6 8.7 63.2 58.1 68.5 34.6 17.2 15.3 7.8 ND ND ND 55.6 20.2 28.2 MOLOCH M 23.2 23.8 10.9 24.5 13.6 65.1 59.5 72.5 37.3 19.9 17.7 9.6 ND ND ND 54.9 16.5 26.4 MOLOCH F 24.1 22.3 13.6 23.7 13 ND 62.5 73.1 34.5 17.9 19.9 8.5 47.7 58.1 85 59 18 25.4 MUELLERI M 22.2 21.2 10.8 23.3 13.2 68.5 60.7 73.9 38.1 19.4 18.3 10.6 53.6 57.7 80.2 80.4 12.1 32.7 MUELLERI ND 24.6 23.4 11.3 22.1 13.3 70.7 62.6 72.1 36.2 21.4 17.5 11.5 53.6 57.7 80.2 80.4 12.1 32.7 MUELLERI F 22.6 20.9 8.9 21.8 11.2 63.6 60.1 66.9 36.8 17.8 15.6 10.4 51.6 58.2 81.2 55.9 14.4 31.5 MUELLERI F 22.1 22.5 11.7 26.1 12.5 63.4 64.3 75.1 36.3 19.2 15.6 10.4 52.1 60 82.9 58.4 15.4 31.8 MUELLERI M 24.6 23.1 13.6 24.7 14.7 70.3 68.9 77.6 39.4 18.9 20.1 10 56.4 51.6 85.3 60 20.3 32.3 MUELLERI M 24.8 22.2 14.6 27 15.9 ND 65.3 81.9 44.1 23.1 18.6 12.5 ND ND ND 62.4 ND ND MUELLERI M 22.9 24.1 12.3 22 13.4 69 65.2 74 39.8 20 18.2 12 54.6 57.6 89.9 59.4 21.4 33.7 MUELLERI F 23.6 21.7 14.3 26.8 12.4 69.8 65.5 78.7 43.9 23.4 18.5 12.6 52.4 58.1 82.9 67.2 16.1 31.7 MUELLERI ND 22.3 21.7 10.4 23.2 11.3 64.8 62.7 71.6 39.9 21.1 16.6 12.1 ND 54.7 81.4 57.5 ND ND MUELLERI M 23.1 23.8 13 26.3 13.6 ND 63.8 72 38.9 20.3 19.2 12.3 57 55.1 81.4 54.8 14.3 ND MUELLERI F 24.5 21.6 12 26.4 13.3 66.2 61.7 74.5 39.5 18.3 18.1 12.4 ND 56.4 ND 58.1 ND ND MUELLERI M 25.1 22.9 12.6 26.7 15.2 74.1 65 77.5 39.9 22.5 20 13.8 56.8 59.6 86.3 61.4 16.9 28 MUELLERI M 24.9 21.9 11 28.8 11.3 70.3 60.7 71.9 40.6 20 17 11.1 47.9 55.3 79.9 58.7 12.1 24.3 MUELLERI M 23.4 21.7 10.7 25.4 12.2 67.1 60.3 72.2 37.3 19.3 17 12.5 49.2 49.1 72.6 57.9 12.5 28.2 MUELLERI F 24.3 22.9 8.8 22.2 11.2 67.1 59.7 68.9 32.2 17.1 15.2 9.7 46.3 58.6 75.3 59.1 17.9 29.9 MUELLERI F 22.9 22.2 12.7 24.3 13.4 67.3 66.6 76.2 37.9 18.1 17.2 11.2 55.9 54.3 80.7 63.2 16.4 27.3 MUELLERI F 22.9 22.7 10.8 26.7 12.8 69.1 63.6 74.3 38.2 21.6 17.7 10.8 50 55 80 62.2 15.8 27.8 MUELLERI M 22.5 21.9 11.6 25.2 13.3 65.9 61.4 72.4 38.9 19.6 17 11.2 49.8 55.8 77.8 61.7 17.1 29.9 MUELLERI M 24 22.4 11.3 24.7 12.2 70.1 64.3 77.9 41.8 18.5 18.2 9.4 53 57.6 84.1 62.5 17.8 26.3 MUELLERI M 24.8 22.8 11.5 25.2 12.5 73.4 63.5 79.6 40.1 18.2 18 13.8 52.3 47.1 78.8 61.4 17 28.8 MUELLERI M 23.8 23 12 23.5 13 71.9 64.1 73.5 38.6 19.3 17.7 12 55.6 53.3 78.3 59 19.7 33.3 MUELLERI M 23.8 20 11.3 24.3 13 69.9 63.2 77.9 40.5 20.3 17.1 12 51 57.2 80.1 59.3 22.7 33.5 MUELLERI M 21.9 21.3 11.7 26.8 12.8 65.7 60.9 72.7 38.4 17.7 17.7 9.7 50 56 80.2 64.5 15.5 23.8 MUELLERI M 23.3 20.2 12.7 22 14.9 72.4 63.2 74.5 41.8 22.3 19.5 12.7 54.1 56.4 79.6 61.2 20.9 32.7 SpeciesSex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 MUELLERI M 23.9 23.9 11.1 25.6 13.2 71.9 65.4 75.1 38.3 18.2 15 12 56.3 67.7 84.3 63.8 20.9 34.1 MUELLERI F 22.6 22.9 11.9 23.2 15.5 62.7 62.2 71.2 37.2 20.7 19 11.4 54 50 78.7 55.1 20.3 27.9 MUELLERI F 21.7 22.7 12.2 26.6 12.1 66.8 60.8 71.3 33.2 16.2 14.9 11.5 46 57.6 74.8 55.2 18.6 27.9 MUELLERI F 22 22.9 12.7 23.4 12.8 68.1 60.8 73.2 38.1 19.6 19.3 10.6 50.1 53.9 79.4 55.4 21 30.5 MUELLERI M 22 20.7 12.4 24.5 14.1 68.3 58.5 70.5 38.5 19.2 18.8 11.7 50.4 52.2 77.2 56 20.6 26.5 MUELLERI F 23.2 23 11.2 27 12.4 66 61.8 73.6 38.5 16.4 16 11.7 51.5 57.5 78.1 55.7 16.9 27 MUELLERI F 21.7 21.5 10.1 24.7 12 63.4 57.3 67.2 34.2 18.5 15.3 11.2 52.3 50.4 74.6 55.4 20.8 29.6 PILEATUS F 23.9 24.4 10.9 24.6 12.3 65.5 61.2 71 35.7 18.1 14.9 11.3 49 60.2 82.4 55.9 15.3 29.9 PILEATUS M 23.6 21.8 13.2 24.4 14.2 68 61 73.1 36.5 19.8 16.9 12.4 49 60.2 82.4 55.9 15.3 29.9 PILEATUS M 23.4 23.1 10.5 23.9 13.6 72.4 62.3 73.3 35.5 21.2 18.6 10.9 52.5 60.1 84.3 ND 17.7 32.4 PILEATUS M 23.5 24.9 11.69 25.6 13.4 73.9 65.7 73 37.2 21 19.1 10.1 54.7 57.8 87.7 59.1 22.1 29.4 PILEATUS M 22.2 21.6 9.9 19.6 11.1 69.5 60.7 69.2 35.7 18.7 18.7 11.2 51.8 38 79.2 57.6 ND 22.5 PILEATUS F 23.3 20.5 10.6 24.4 11.8 64.1 59.7 70.6 34.3 19.4 16.5 10.8 50.4 57.2 80.5 56.9 16.8 27.5 PILEATUS M 23.7 21.1 9.4 24 12.7 69.6 59.4 71.5 35.5 20.1 18 11.2 52.3 56.2 79.7 57.8 15.9 29.4 PILEATUS M 24.9 24.4 10.2 25.3 12.2 71.2 62.4 76 40.3 18.8 14.9 11.6 48.7 55 81.1 59.9 11 25.8 PILEATUS M 23.2 22.4 12 23.7 14.2 70 61.7 73.4 35.6 18.5 18.3 11.8 49 60.2 79.2 58.2 12.2 23.3 PILEATUS F 24.3 22 9.5 21.1 14 68.1 62.2 74.5 37.3 19.1 17.7 11.2 51.4 52.5 79 60 18.6 28.1 PILEATUS F 24.2 21.8 9.8 22.2 12.6 67.1 61.6 73.7 36.3 20.8 18.3 9.5 51.7 43.3 78.9 56.2 16 25.2 KLOSSII F 22.8 21.6 11.3 22.2 12.6 63.4 62.5 71.2 36.7 19.1 16.4 10.7 46.6 61.7 77.3 55.2 14.3 31.7 KLOSSII M 22.4 21.7 10.2 23.1 12.4 ND 59.1 71.4 37.2 19.5 17.1 10.7 50.7 52 77.4 55.1 11.5 28.3 KLOSSII F 22.1 21.3 10.2 23.9 12.7 62.4 59.8 70.9 34.7 17.7 14.3 9 53.1 48.7 74.6 53.38 19.9 30.4 KLOSSII M 21 22.1 10.4 21.3 12.9 65.2 57.7 70.9 36.3 18.1 18.4 9.4 48.8 48.6 76.1 54.8 17.5 27.1 KLOSSII M 22.6 20.7 11.2 22.5 12.4 66.9 59.7 69.4 36.5 18.1 17.5 9.5 55.3 50 77.8 55.9 18.6 28.5 KLOSSII M 23 21.3 10.3 23 12.7 63.8 59.4 71 36 17.7 18.6 8.7 51.6 42.5 76.2 54.6 15.3 30.8 KLOSSII F 21 20.1 10.1 20.9 11.4 63 56.4 68.8 35.4 17.8 15.8 9.3 50.5 51.3 72.7 51 15.5 29.4 KLOSSII M 23 20.7 10.7 23.3 11.5 66.7 59.7 69.8 35.9 18.2 17.2 9.7 55.2 50.8 78 56.3 18.6 28.3 KLOSSII M 21.1 19.6 10.3 20.6 12.5 62.4 59.5 68.4 36.3 18.5 18 10.8 49.7 51.5 73.2 56.7 20.9 29.3 KLOSSII F 22.2 20.4 10.5 20.6 11.6 59.9 59.4 68.6 35.6 19.7 17.1 9.2 53.6 38.1 74.9 54 18.6 27.9 KLOSSII F 20.2 21.5 10.4 21.1 12 62.2 58.4 67.9 34.9 19.4 16.7 9.9 53.5 32.7 75.2 54 19.2 27.5 KLOSSII M 23.5 21.4 9.3 21.2 13.5 65.3 60 73 36.9 ND ND ND ND ND ND ND ND ND KLOSSII F 22.7 21.4 9.9 22.7 12.8 62.2 59.9 70.7 37.9 16.6 16.6 10.2 51.2 55.9 75.9 54.4 15.8 30.5 KLOSSII F 22.3 20.7 9.1 21 13.8 62.9 57.8 67.5 34.8 19.1 17 10.5 ND 40.1 51.9 52 17.4 28.6 HOOLOCK F 23 21.9 12.8 26.2 14.3 70.5 68 84.7 44.5 24.5 20.9 13.4 54.1 59.4 83.9 64.7 20.8 39.2 Species Sex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 HOOLOCK M 22.5 19.9 12.3 26.6 13.5 70.5 68.8 83.2 45.6 21.6 17.4 13 55.2 59.2 82.8 61.7 ND ND HOOLOCK F 22 21 12.1 27 14.1 69.7 65.6 82 44.1 18.2 18.9 13.8 52.3 59.2 81.9 61.5 16.5 29.3 HOOLOCK F 22.5 22.6 12.6 27.7 15.3 71 62.5 79.1 42 21.2 19.3 12 54 58 81.4 61.4 18.5 35.5 HOOLOCK F 22.3 21.3 12.2 28 15.4 67.7 65.5 79.9 41.4 21.4 20.9 11.6 53.7 52.6 82.3 62.9 16 28.7 HOOLOCK M 23.6 23.3 15.8 28 15.7 76.8 71.1 86.4 43.5 23 21.3 15.1 54.3 58.6 86.1 64.8 14.4 28.9 HOOLOCK M 23.5 21.4 14.5 25.6 13.5 71.5 66.5 80.9 41.2 22.6 16 13.2 50.1 59.3 85.9 60.1 14.6 29.7 HOOLOCK M 23.5 22.7 14.3 29.3 15.2 75.4 67.8 86.6 43.2 23.2 19.9 13.7 51.1 60.2 82.9 65.8 17.6 30.8 HOOLOCK M 23.6 23.2 14 27.4 15.4 73.3 67.5 84.4 43.4 23 17.6 13.9 54.5 64.6 85.1 60.5 14.9 32.7 HOOLOCK F 22.7 21.8 12.4 27.1 14.9 68.8 66.5 82.7 42 21.7 19.4 13.9 49.1 62.8 81.6 63.8 14 29.4 HOOLOCK M 21.7 23.2 13.3 26.9 14.7 ND 65.5 84.3 44.9 20.6 20.2 14.3 ND ND ND ND ND ND HOOLOCK M 22.7 21.6 14.9 29 15.8 75.2 68.6 83.9 42.1 23.1 21.7 13.4 52.9 60.9 84.1 61.7 17.2 29.8 HOOLOCK M 23.9 20.8 12.3 26.5 12.9 69.2 64.4 82.4 43.8 20.3 21 13.5 55.7 61.8 82.5 58.1 13.1 30.1 HOOLOCK ND 22.7 21.7 13.6 26.3 14.3 70.5 68.3 84.9 42.1 20.6 16.4 14 ND ND ND 62.4 ND ND HOOLOCK M 24.4 22.8 14.2 27.2 14.5 70 70.9 86.2 44.2 21.6 19.9 13 57.1 61.1 88.5 63.5 17.3 33.3 HOOLOCK F ND 22.3 13.2 27.1 15.5 ND 65.5 83.2 ND ND ND ND 52.4 57.5 82.7 62.8 11.1 ND CONCOLOR F 24.1 24.8 10.6 27.1 14.1 66.9 67.9 82.7 39.9 21.8 19.4 13.4 60.3 56.2 87.6 59.6 17.3 32.8 CONCOLOR M 22.9 22.3 14.7 28.4 14.1 79.3 69.7 83.3 43.1 24 23.4 13.3 61.5 57 89.8 69.4 29.2 37.8 CONCOLOR F 22.6 21.9 10.5 27.7 13 ND 61.7 76.3 41.1 22.1 17.1 9.7 58.7 51.9 81.8 53.2 ND ND GABRIELLAE M 21.2 22.2 12.5 23.4 11.5 ND 60.8 72.5 37.5 16.2 16.8 8.5 56.2 49.2 82.5 56.7 20.3 34.5 GABRIELLAE M 22.6 22.2 13.2 29 14.2 70.3 58.9 72.4 38.5 22.3 20.2 ND 50.5 47.1 78.7 58.7 13.53 26.41 GABRIELLAE F 21.59 22.8 13.17 28.31 14.29 68 60.69 75.21 40.07 19.82 19.21 ND 55.65 51.97 79.35 54.34 6.4 20.61 GABRIELLAE F 21.53 23.76 9.98 26.26 11.62 63.09 57.73 69.7 35.22 17.25 17.49 ND 53.82 46.22 77.57 58.07 15.74 26.49 GABRIELLAE M 23.46 22.88 14.1 31.46 13.76 71.79 62.51 82.66 42.38 20.56 20.3 ND 56.02 50.28 77.83 64.96 ND 23.78 GABRIELLAE M 20.48 19.43 3.41 22.13 7.1 51.96 50.1 53.5 24.4 14.6 13.9 ND 44.6 36.8 69.4 41.2 12.56 26.86 GABRIELLAE F 23.2 21.7 14 29.1 12.9 67.3 64 78.4 44.1 19.7 18.2 10.9 58 45 83.1 56.2 22.7 30.8 GABRIELLAE M 20.9 22.2 11.3 28 11.6 61.7 60 73 36.9 19 17 9.6 58 53.3 80.5 57 25.5 34.7 GABRIELLAE F 20.9 20.2 12.3 28.3 12.4 60.2 58.4 73.1 40.7 21 17.3 10.1 56.1 49.5 80.6 54.4 22.2 32.7 GABRIELLAE F 23.3 21.7 ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND GABRIELLAE M 22.4 21.1 14.1 26 11.2 71.1 64 78.8 42 20.7 19.9 8.9 55.7 53.6 84.4 63.8 22.7 31.8 GABRIELLAE M 23.7 21.7 12.2 27.6 11.4 66.5 64 76 39.1 22.7 17.8 10.3 59.8 45.7 83.3 58.1 22.8 31 GABRIELLAE M 21.4 19.8 13.6 26.8 13 66.2 61.2 74.7 38.5 21.6 19 9.5 56 50.4 81 57.2 23.7 30.5 GABRIELLAE M 21.7 21.9 14.5 27.4 14.1 68.2 60.3 74.4 38.2 22.1 18.6 11.4 54.4 53.4 79.9 58.2 ND 27.8 GABRIELLAE F 24.2 23.4 13.8 25.3 13.3 66.3 62.7 75.1 40.1 23.8 18.6 12.7 56.8 53.4 81.7 61 19.2 27.8 Species Sex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 LEUCOGENYS F 24.8 24.8 14.1 27.9 14.5 70.4 71.2 81.9 44.8 22 18.4 12.6 58.5 58.4 89.1 62.5 25.8 42 LEUCOGENYS M 24.8 23.7 13.7 29.4 13.7 77.1 67.1 82.6 44.7 22.2 20.7 12.1 62.9 67.7 84.5 64.4 19.5 35.5 LEUCOGENYS F 22.5 21.2 12.4 28.6 12.8 69.3 64.2 78.7 42.7 22 16.9 11.3 56.4 63.4 85.3 59.7 11.3 27.9 LEUCOGENYS F 21.4 22.7 14.6 25.6 15.6 67.7 61 75.7 39.7 22.2 18.8 10.1 54.6 41.4 ND ND ND ND LEUCOGENYS F 23 21.8 12.4 26.1 12.6 70.7 64.6 78.6 44.7 20.9 17.8 11.8 60.7 51.2 84.3 60.2 24.1 37 LEUCOGENYS M 25 23.8 14.3 30.4 15.3 75.7 67.6 86.1 46.4 23.2 20.8 12.4 57.7 54.1 87.1 65.3 16.5 28.4 LEUCOGENYS F 22.6 20.9 11.7 28.2 12.9 70.8 66.1 77 39.3 21.3 19 10.8 58.9 54.1 85.7 60.8 22.7 33.1 LEUCOGENYS F 23 23 11.2 30.1 12.9 67.2 63.4 75.9 39.7 20 17.4 11 57.8 50.3 85.4 58.4 17.2 29.5 LEUCOGENYS M 24.7 22.3 12.5 29.9 14.7 69.8 67 80.5 44 23 19.4 12.6 56.5 55.6 85 56.2 21.7 30.4 LEUCOGENYS F 21.7 21.2 11.5 27 11 68 64.3 74.8 37.7 22.2 16.7 10.4 60.4 48.3 83.7 58.4 22.7 30.7 LEUCOGENYS F 22 20.2 14.9 27.8 14.1 68.3 67 80.2 41.2 22.1 19.3 10.6 62 55.6 86.6 58.3 19.7 29.8 LEUCOGENYS F 22.9 21.7 13.2 28.3 13.1 70.1 65.3 77.5 40.7 20.8 17.2 11 60.2 53.3 85.7 61.5 20.7 34.6 SYNDACTYLUS M 27.8 22.6 20.1 30.7 15.4 85.8 70.7 86.6 47.5 25.4 26.4 14 57.5 56.3 87.6 67.7 21 34.3 SYNDACTYLUS M 24.7 23 15.9 29.7 10.6 87.1 76.1 95.5 50 23 21 13 55.2 59.6 91.9 71.9 17.5 31.4 SYNDACTYLUS F 23 ND 14.2 29.9 15.3 ND 71.9 90.1 45.8 22.6 24.1 11.6 57.7 55.6 89.4 67.4 14.9 28.6 SYNDACTYLUS F 21.3 21.3 15.5 28.2 13.9 80.1 70.7 91.1 49.8 23.4 24.3 11.5 54.6 58.7 88 62.3 16 30.8 SYNDACTYLUS M 22.7 21.5 13.2 28.5 9.2 71.9 70 85.6 43.2 21.4 18.3 12.7 56.4 54.9 84.4 62.4 17.3 30.8 SYNDACTYLUS M 24.1 24.6 14.4 32.4 14.3 79.1 76.4 93 48.1 21.3 19.2 13 61 59.4 95.4 68.3 18.5 34.9 SYNDACTYLUS M 24 23.1 19.7 37.5 14.9 83.6 75.6 97.6 49.1 24.4 22.5 13.5 57.2 55.4 90.7 69.5 12.2 31.1 SYNDACTYLUS F 23.8 25.8 13.6 35.6 13.2 75.6 69.8 89.7 44.9 23.3 17.9 13.6 55.2 51.2 82.8 64.1 15.3 32.1 SYNDACTYLUS ND 25.1 23.5 ND ND ND ND ND ND 50.5 ND ND 12.4 ND ND ND ND ND ND SYNDACTYLUS ND 25.5 26.1 18 37.3 17.8 91.5 80 103.8 50 24.3 23.6 15.1 58.6 53.7 94.6 72.2 19.3 34.5 SYNDACTYLUS F 23.8 24.5 16.4 35 15.1 80 74.7 93.2 50.3 20.5 22.2 12.4 58.4 55.7 91.1 63 15.7 33.1 SYNDACTYLUS F 26.7 25.7 14.9 32.1 15.4 84.3 75.6 97.7 52.1 20.1 23.1 14.3 57.2 56.4 94.3 67.2 15.4 29.2 SYNDACTYLUS F 25.7 23.1 16.9 31 16.8 79.5 75.6 97.5 54.7 25.2 21.6 14.6 55.7 59.5 93.1 66.4 16.9 29.1 SYNDACTYLUS M 24.4 23.9 18.7 35.4 15.1 91 80.5 101.6 53.7 26.9 22.9 13.8 54.9 56.7 94.2 77.3 14.9 33.7 SYNDACTYLUS F 22.1 23.9 13.8 28.1 14.6 68.6 67.3 79.8 43.2 20.7 20.2 9.4 55.6 59.2 86.5 61 18.7 30.2 SYNDACTYLUS F 23.6 23.1 14.9 33.7 14.3 82 78.3 96.1 54.4 21.7 21.1 10.2 61.7 60 97 64.2 26 37.6 SYNDACTYLUS M 24.6 23.1 16.4 35.1 15.9 86.5 77.2 99.5 53.8 23.2 23.7 11.6 55.6 65 94.5 69.8 22.3 34 SYNDACTYLUS F 24.1 26.4 15 31.3 17.9 78.8 70.6 90.3 49.7 21.4 22.4 12.6 54.5 60.4 91.1 63.7 16.7 27.9 SYNDACTYLUS M 26.7 24.3 18.1 34.6 16.9 ND 75.1 96.8 49.8 22.4 23.5 14 55.2 56.7 91.2 ND 17.8 25.7 SYNDACTYLUS F 22.6 21.9 12.7 29.4 14.3 75.8 70.2 86.2 47.2 22.2 20.6 12.4 54.6 55.4 88.8 61.3 17.2 28.5 SYNDACTYLUS M 25.2 ND 12.8 32.1 ND 76.5 70.8 91 50.8 22.8 22.5 15.3 51.6 61.1 88.2 64.7 ND ND Cranial Variables Species Sex 36 37 38 39 40 41 LAR M 60 26.9 52.9 45.3 16.5 19.2 LAR F 59.1 25.5 52 46.7 16.1 21.8 LAR M 53 26.3 50.5 49.4 15.8 19.4 LAR M 51.7 24.4 47.7 41.3 17.2 17.9 LAR F 56.3 25.4 52.2 39.5 15.8 18.1 LAR M 54.8 24.9 49.9 46 17.4 20.2 LAR M 55.6 27.1 50.8 45.7 19.2 17.8 LAR M 53.6 23.7 49.6 44.9 17.2 17.4 LAR M 55.6 25.2 46.8 36.8 17.8 18.2 LAR F 55.6 24.8 50.1 41.9 16.7 16.1 LAR F 56.2 27.5 52.6 42.7 19.1 19.6 LAR M 56.3 27.3 51.5 41.5 17.7 19.7 LAR F 52.5 24 46.8 38.3 15.6 16.1 LAR M 58.2 33.8 54.7 47.8 20.5 20.6 LAR M 54.6 21.8 48.1 45.2 17.9 16.8 LAR F 54.7 24.4 47.4 42.8 16.2 20.2 wu> LAR ND 55.2 25.1 52.2 47.5 20.5 18.5 LAR ND 54.7 20.6 46.1 39 17.5 17.3 LAR M 53.5 25.8 52.5 ND 20.9 17.8 LAR F 58.1 26.5 51.8 44.9 18 16.8 LAR M 56.7 30.8 55.2 44.7 20.7 17.7 LAR M ND ND ND ND ND ND LAR M 54.6 28 53.7 46.3 16.8 18.8 LAR F 52.8 25.2 48.5 36.5 16.7 15.8 LAR M 56.2 26.9 49.8 39.6 19.4 16 LAR ND 58.3 30.1 52 39.1 18.5 18.1 LAR M ND 26.5 ND ND 19.7 17.2 LAR M 60.1 29.1 55 46.8 20.2 20 LAR F 55.5 28.3 51.6 ND 19.5 17.4 LAR M 54.1 27.9 50.5 47.4 20 18.9 LAR F 61.9 31.6 54.7 40.3 19.4 16.2 LAR M 59 30 56.2 ND 16.1 16.2 Species Sex 36 37 38 39 40 41 LAR F 57 30.2 52.8 42.7 20.5 18.3 LAR M 57.8 30.1 53.9 42.6 20.1 17.1 LAR M 52.6 25 47.8 42 17.3 17.1 AGILIS F 57.7 28.6 52.1 42.9 19.5 18.8 AGILIS F 55.4 24.6 51.9 40.1 20.4 19.2 AGILIS M 55.5 26.1 52.1 37.9 21.3 17.6 AGILIS F 53.2 26.2 47.6 41.8 18 18.9 AGILIS M 52.9 25.7 49.8 40.2 19.4 20 AGILIS F 60.3 23.5 48.2 38.5 18.1 16.6 AGILIS M 53.4 27.3 50.2 41.4 18.8 18.7 AGILIS F 57.6 27.5 52.9 37.4 21.5 17.9 AGILIS ND 53.4 23.7 48 51.6 18.3 19.7 AGILIS M ND 23.4 ND ND 20.9 ND AGILIS M 53.6 23.25 45.82 45.9 18.6 ND AGILIS F 47.2 20.2 42.5 37.5 16.3 ND AGILIS M 53.4 27.9 49.9 37.7 17.9 16.6 AGILIS F 52.7 27.1 50.6 42 18.8 18.4 AGILIS M 53.1 29.1 52.5 38.6 19.1 19 AGILIS F 56.7 29.3 50.7 42.9 20.1 18.1 AGILIS M 54.8 25 52.6 41.3 20.6 18.8 AGILIS M ND 27.1 ND 41.5 19.7 19.6 AGILIS F 53.1 26.5 49.8 39.1 18.6 18.5 AGILIS F 56.4 24.2 49.8 42.4 17.3 18.4 AGILIS M 57.8 24.6 51.3 39.7 21.1 16.6 AGILIS M 52.2 23.9 49 40.6 19.4 18.7 AGILIS F 55.4 26.9 52.6 41.5 16.1 19.7 AGILIS M 50.5 23.6 45.9 39.6 17.5 15.9 AGILIS F 54.8 21.9 50 46.7 19.9 18.4 MOLOCH M 52.4 24.1 50.8 42 18.5 21.8 MOLOCH M 54.6 27.3 51.8 42.2 17.6 22.6 MOLOCH ND 53.1 25.6 52.3 44.6 19.7 21.3 MOLOCH F 55 22.6 49.2 46:2 18.7 17.3 MOLOCH M 55.5 26 51.5 39.5 21.3 21.9 Species Sex 36 37 38 39 40 41 MOLOCH F 54.2 23.7 51 51 17.5 18.7 MOLOCH ND 49.4 24.7 49.6 36.3 15.6 21.2 MOLOCH M 57.9 25.6 51.9 43.1 19 17.2 MOLOCH M 46.5 20.6 43 35.1 15.9 17.3 MOLOCH F 57.5 24.5 51.6 46.4 17.6 19.5 MOLOCH M 55.6 22.7 48.1 ND 16.6 16.9 MOLOCH F 53.9 24.5 46.2 41.6 15.7 18.6 MOLOCH M 49.7 25.9 46.7 39.8 17.4 19.7 MOLOCH F 55.1 25.5 50.3 41.8 16.2 21.3 MUELLERI M 54.5 21.7 47.2 36.6 18.5 16.6 MUELLERI ND 53.9 23.5 52.4 44.2 16 17.9 MUELLERI F 52.5 24.7 45.8 38.5 17.8 17 MUELLERI F 51.5 20.1 46.1 43.8 17.2 19.6 MUELLERI M 56.5 19.9 51.7 45.3 20.5 20.3 MUELLERI M 56.9 23.2 55 ND 20.5 18.9 MUELLERI M 56.7 22.1 49.9 ND 18.1 17.7 MUELLERI F 58.5 27.4 54.2 46.2 22.4 21.8 MUELLERI ND 54.3 24.6 50.4 40.4 19 16.6 MUELLERI M 52.6 21.5 48.9 43.1 17.2 17.3 MUELLERI F 51.3 24.9 48.7 ND 17.7 18.5 MUELLERI M 59.1 25.5 55.9 46.5 22.2 19.7 MUELLERI M 56.6 26.7 48.4 47.6 18.9 17.8 MUELLERI M 53 26.7 50.3 43.4 19.1 18.4 MUELLERI F 49.8 21.9 46.5 42.6 16.1 16.7 MUELLERI F 57.9 26.5 54.5 38.2 20.7 18.4 MUELLERI F 57 22.7 49.3 42.2 18.6 16.6 MUELLERI M 53.7 24.5 46.9 44.6 18.6 17.6 MUELLERI M 54.5 21 49.6 43.1 20.4 19 MUELLERI M 55.7 21.6 45.5 38.5 20 16.5 MUELLERI M 53.8 24.5 52.3 39.3 19.1 17.3 MUELLERI M 53.9 26.9 51.9 44.7 22.1 18.9 MUELLERI M 52.3 25 46.7 42:4 17.7 17.7 MUELLERI M 59.2 26.1 51.5 43.5 19.1 20.2 Species Sex 36 37 38 39 40 41 MUELLERI M 52.6 25.7 50.7 36 20.1 19.4 MUELLERI F 51.8 24.1 48.2 42.4 18.2 16.6 MUELLERI F 53.8 23.9 49.4 40.3 17.8 19.4 MUELLERI F 55.1 22.4 47 36.3 18.9 19.2 MUELLERI M 54.5 23.7 47.2 44.1 18.4 19 MUELLERI F 55.8 23.3 48.1 43.1 21.5 20.2 MUELLERI F 48.9 23.3 47.6 42.7 16 18.9 PILEATUS F 52.7 21.7 46.2 39.5 16.1 18.4 PILEATUS M 55.9 23.9 49.1 42.5 17.4 18.8 PILEATUS M 54.5 23.5 50.3 43.3 16.1 19.1 PILEATUS M 55.5 23.4 52.7 42.3 20.4 ND PILEATUS M 53.4 26.9 51.6 43.2 15.6 19.7 PILEATUS F 53.3 24.6 46.4 40.3 18.2 18 PILEATUS M 55.4 22.9 49.2 42.6 15.8 18.5 PILEATUS M 54.6 23.6 50.3 42.9 19 19.5 PILEATUS M 54.5 24.7 47.4 45 17.7 18.7 PILEATUS F 54.9 25.5 49.9 43.9 16.4 20.1 PILEATUS F 49.5 24.5 49.5 38.4 17.2 19.2 KLOSSII F 53.3 22 46.6 39.9 18.3 17.5 KLOSSII M 50.9 22.1 48.3 39.9 17.2 18.1 KLOSSII F 46.8 21.2 46.6 36.4 15.8 16.8 KLOSSII M 49.5 19.7 45.2 39.5 17.4 18.4 KLOSSII M 50.1 23.5 47.5 39.5 18.9 17.4 KLOSSII M 49.5 20.4 45 36.7 16.2 17.7 KLOSSII F 48.7 24 45.6 36.4 15.8 17.2 KLOSSII M 50.1 26.8 47.6 40.3 18.8 17.3 KLOSSII M 50.2 20.4 43.9 38.5 16.7 18 KLOSSII F 50.2 22.3 45.3 38.2 17.2 17.6 KLOSSII F 51.6 21.5 46.4 37.3 15.6 17.9 KLOSSII M 54.2 21.9 48.1 44.7 17.5 19 KLOSSII F 50.4 22.7 46.1 35.9 17.2 16.6 KLOSSII F 50.6 21.2 44.6 38:4 14.9 16 HOOLOCK F 59.4 30.6 55.8 51.6 23.3 19.9 Species Sex 36 37 38 39 40 41 HOOLOCK M ND ND ND ND ND ND HOOLOCK F 59.9 27.9 51.4 41.6 23 16.7 HOOLOCK F 58.7 29.2 54.3 44.9 22 19.3 HOOLOCK F 61.5 25.6 53.5 47.7 22.2 18.3 HOOLOCK M 63.8 28 61.6 45.5 24.9 21.5 HOOLOCK M 60.2 26.2 55.9 41.4 20 20.8 HOOLOCK M 62 27.3 55.7 43.5 23 21.5 HOOLOCK M 56.1 27.9 56.6 43.2 23.1 19.7 HOOLOCK F 60.5 26.8 51.1 43.1 22.2 20 HOOLOCK M 59.6 25 ND ND 24.2 20.4 HOOLOCK M 58.3 24.2 52.8 ND 21.7 21.2 HOOLOCK M 59.3 29.2 51.5 45.1 21.8 18.3 HOOLOCK ND 59.6 29.1 54.2 43.7 24.3 19.1 HOOLOCK M ND ND ND ND ND ND HOOLOCK F ND 26.2 ND ND 21.2 20.22 CONCOLOR F 59.8 24.1 54.4 44.7 22.2 17.9 CONCOLOR M 64.4 27.6 57.2 49.9 26.5 19.5 CONCOLOR F ND ND ND ND ND ND GABRIELLAE M ND ND ND ND ND ND GABRIELLAE M 55.49 21.19 51.29 47.06 18.44 ND GABRIELLAE F 55.13 22.37 49.4 46.82 22.27 ND GABRIELLAE F 51.77 20.57 48.84 42.58 16.08 ND GABRIELLAE M ND 24.96 52.18 48.59 ND ND GABRIELLAE M 48.4 26.8 45.6 30.63 14.5 ND GABRIELLAE F 55.3 28 51.5 45.8 21.1 18.2 GABRIELLAE M 51.4 23.3 48.5 45.8 18.8 17.4 GABRIELLAE F 50.9 25.7 48.5 41.8 19.8 18.8 GABRIELLAE F ND ND ND ND ND ND GABRIELLAE M 56.5 24.7 50.9 45.9 19.2 18.9 GABRIELLAE M 55.2 25.1 48.9 44.1 18.5 18.4 GABRIELLAE M 57.2 23.1 51.8 48.3 17.6 18.3 GABRIELLAE M 54.2 24 49.6 44:2 21.1 ND GABRIELLAE F 52.5 25 52.4 43.8 20.6 ND Species Sex 36 37 38 39 40 41 LEUCOGENYS F 59.2 27.6 55.1 40.9 24.1 17.9 LEUCOGENYS M 61.2 28.9 55.5 45.5 20.1 19.9 LEUCOGENYS F 59.6 27 53.5 45.4 21.2 19 LEUCOGENYS F 52.4 25.6 51 45.6 19.5 16.4 LEUCOGENYS F 57.8 26.8 51.9 44.4 20.5 18.3 LEUCOGENYS M 60.9 27.4 53.2 48.9 23.4 20.6 LEUCOGENYS F 59.2 23.3 50.9 48 20.9 18.5 LEUCOGENYS F 60 27.2 50.4 43.8 20.1 17.7 LEUCOGENYS M 57.1 22.2 50.7 39.5 21.4 ND LEUCOGENYS F 55.4 29.4 53.3 44.3 19.7 20.2 LEUCOGENYS F 61.7 26 51 45.9 19.2 17.8 LEUCOGENYS F 62.4 25.5 49.9 49.4 19.9 18.5 SYNDACTYLUS M 67.9 34.4 64 50.6 25.7 21.2 SYNDACTYLUS M 71.5 30.9 58 53.3 23.8 22.3 SYNDACTYLUS F ND 34.5 60.9 52.6 ND 24.1 SYNDACTYLUS F 64.4 29.4 56 52 21.9 21.7 SYNDACTYLUS M 60.9 30.6 54.7 45.3 21.3 22.8 SYNDACTYLUS M 62.6 30.8 54.7 42.2 22.3 24.6 SYNDACTYLUS M 68.9 33.1 56.8 55.1 28.8 24.9 SYNDACTYLUS F 65.3 31.1 55.7 48 21 19.9 SYNDACTYLUS ND 66.5 34.3 57.5 44.9 24.6 20.4 SYNDACTYLUS ND 73.9 35.9 66.6 55.7 24.4 25.3 SYNDACTYLUS F 66.4 31.8 56.6 47 25 21.2 SYNDACTYLUS F 67.3 34.2 57.86 54.4 25.6 22.2 SYNDACTYLUS F 63.5 32.5 56.9 ND 25 23.9 SYNDACTYLUS M 74.7 37.8 62.8 54.3 24.6 23.4 SYNDACTYLUS F 60 30.1 52.4 48.8 23.6 ND SYNDACTYLUS F 65.1 33.2 55.5 49.3 25 24.5 SYNDACTYLUS M 68.1 32.5 58.5 45.9 23.6 23.4 SYNDACTYLUS F 67.3 35 61.4 46.2 25.1 22.5 SYNDACTYLUS M ND 36.7 61.5 ND 25.3 21.1 SYNDACTYLUS F 64.8 32.2 56.6 46.6 19.9 21.6 SYNDACTYLUS M 66 29.4 56 49.3 24.2 22.2 Cranial Variables Species Sex 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 36 37 HUMAN F 35.6 33.5 24.9 35.8 25.2 117.5 112.7 112.9 44 40.2 22.5 25.6 128 98.3 172 105 45.6 83 92.2 50.3 HUMAN F 36.2 30.6 24.7 43.4 24.4 122.5 117.1 121.7 45 42.1 28.420.9 129 99.17 177 112.5 58.6 85 110.8 65 HUMAN F 35.6 34.4 25.4 48.4 25.1 123.5 114.8 118 43.7 37.826.8 19.2 120 99.5 175 113.6 67.5 93.1 105.2 60.6 HUMAN M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND 110.4 60.8 HUMAN M 38.2 32.1 23 4428.8 121.9 114.8 121 49.9 44.2 26.6 23 142 112.5 185.5 108.9 84 104.2 ND ND HUMAN M 36.9 34.8 24 51.5 26.6 118.8 120.6 123.8 47.4 44.7 26.2 28.8 122 103 180.5 104.9 53 90.5 102.7 68.3 HUMAN F 35.6 32.3 25.1 42.4 25.4 116.3 118.2 114.9 42.541.6 23.5 26.4 127.5102.8 161.5 109.5 70.6 89.4 115.5 58.2 HUMAN M 38 32.821.1 47.8 26.4 124.1 119.8 118.6 46.9 42.6 27 24.2 130.5 108.4 182 115.5 64.5 77.8 110.7 68.3 HUMAN F 39.8 35.3 25.9 45.124.1 110.5 111.8 112.5 43.5 37.6 27.3 19.9 128.5 98.9 168.5 99.7 73 90.8 111.1 65.6 HUMAN M 37 31.3 21.8 45.3 23.2 123.2 114.6 113.9 43.1 37.9 24.3 22.5 120 100 178 112.4 69.6 90.3 111 62.3 HUMAN M 38.3 36.5 23.9 56 23.1 129 123.9 122.949.2 39 22.7 25.7 135 110.9 186 116 73.5 92.1 120.7 65.8 Species Sex 38 39 40 41 HUMAN F 80.4 92.6 32 43.2 K>u> HUMAN F 96.3 92.8 33.5 42.6 HUMAN F 91.9 90.4 32.3 45.4 HUMAN M 97.7 97.3 30.3 51.6 HUMAN M ND ND ND ND HUMAN M 94.3 95.3 33.7 47.7 HUMAN F 98.5 96.5 28.2 48.5 HUMAN M 94.7 92.7 35.7 42.8 HUMAN F 94.9 95.7 30.2 44 HUMAN M 92.8 93.933.5 44.3 HUMAN M 97.8 98.2 35.8 44.7 Postcranial Variables Species Sex 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 LAR ND 237 18.5 10.9 11.5 26.9 271 8.7 6.7 7.3 219.5 14.7 8.2 11.2 11.3 178.5 24.1 15.2 11.2 7.5 LAR F 210 16 7.1 7.7 25.3 225 9 6.4 4.6 185 13.6 8.7 7.3 7.4 159.5 21.4 14.8 8.3 5 LAR F 245 20.1 9.6 10.3 28.5 280 10.4 6.3 8.5 225 16.9 8.3 10 10.3 194 25.3 16.8 11.6 6.8 LAR M 180 13.8 6.5 6.8 23.3 ND ND ND ND 159.5 12.6 8.9 6.5 6.4 134 20.2 12.6 6.3 4.8 LAR M 244 18.1 10.1 11 29 272 8.1 7.9 7.1 210 16.1 9.7 10.4 10.6 186.5 26.1 15.5 11.4 7.6 LAR M 227.5 17.3 10.5 9.8 27.8 265 7.8 7.2 6.5 202 14.8 8.1 10.5 10.5 176.5 23.4 15.1 10.7 7.3 LAR F 227 18.8 8.9 9.2 26.9 268 7.7 5.9 6.1 206 14.8 9 9.4 9.7 177 22.7 14.8 9.8 6 LAR F 234.5 19.1 9.9 10.6 29.7 260 8.5 6.7 7.3 213 17.8 9.4 10.1 10.6 180.5 27.1 17.7 11.4 8.1 LAR M 227 19.1 10.3 10.2 27.5 260 9.8 8.3 7 203 16.3 7.2 10.8 11.2 180 25.5 18.7 12.8 8.2 LAR M 238.5 20.4 11.1 10.5 30.9 274.5 9.9 6.7 7.3 216 17 7.3 11 10.5 188.5 27.4 18.4 13.2 7.1 LAR F 238 17.5 10.1 11 26 267 7.2 7.6 5.9 211 15.6 10.4 10.2 10.2 172 24.2 15.1 10.5 6.1 LAR F 228.5 16.6 9.5 9.1 25.7 264 7.2 6.1 6 198 15.3 6.7 9.3 9.2 170 23.3 16 10.7 5.9 LAR M 251 19.5 9.9 10.3 31.6 ND ND ND ND 215 18.3 9.1 10.9 10.3 187 27.8 18.7 12.9 7 LAR ND 210 16.9 9.5 9.8 24.7 244 7.4 7.2 6.4 194 15.9 8.2 9.8 9.9 170 23.9 18.2 11.2 7.4 LAR M 239 18.9 10.7 10.2 25.7 267 6.9 8.5 6.6 197 15.4 6.8 9.7 9.2 180 25.1 17.5 10.8 7.4 AGILIS M 228.5 17.5 9.3 9.8 26.8 289 8 7.4 7 200 15.7 8.6 10.2 9.6 186 24.2 16.4 11.1 6.9 AGILIS M 234 17.9 9.1 9.6 26.6 267 7.7 7.9 6.4 202.5 15.5 8.9 9.9 10.2 178.5 25.8 15.7 10.9 6.7 AGILIS F 228.5 16.9 8.6 9.7 26.5 258.5 8.5 7.1 5.6 201 15.2 8.4 9.1 9.5 175.5 23.5 15.2 9 6.7 AGILIS M 229 17.6 9.3 10 26.8 289.5 8 7.4 7.4 198.5 15.8 8.4 10 9.5 186 24.1 15.9 11.3 6.8 AGILIS M 248 20.1 10.2 11 29.8 300 8.1 7.1 7.3 215 16.6 8.7 10.5 10.9 186 27.2 18.6 11.6 7.4 AGILIS M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND AGILIS M 221 16.5 8.7 9 26.4 253 6.8 6.9 5.3 195 14.3 7.9 9.5 8.9 165.5 24.5 15.6 9.6 5.8 AGILIS F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND AGILIS M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND AGILIS M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND AGILIS F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND AGILIS M 219 16 10.2 9.8 28.6 247 8.5 7.8 6.9 187 16.2 8.3 10 10 160 25.1 15.8 11.1 6.6 AGILIS M 233 18.1 10.3 10 27.2 262 7.7 8.2 6.6 204 15.6 7.8 10.4 10.7 181 24.1 15.1 11.5 7 MOLOCH F 193.5 15.5 8 8.1 25 223 6.5 5.1 4.9 ND ND ND ND ND 160 22.7 ND 7.4 5.1 MOLOCH M 193 15.2 7.1 7.2 25.5 202.5 6.9 5.3 4.3 176.5 14 8.8 8.1 8.4 148 22.1 14.6 8.1 5.9 MOLOCH M 240 18.1 8.9 9.4' 29.8 261 7.7 7.3 6.1 206.5 16.9 9.7 9.7 9.8 175 25.9 13.9 11.1 6.6 MOLOCH M 223 18.3 10 11.2 29.4 265 7.5 7.7 6.9 205 13.9 7.2 10.5 10.3 190 25.9 ND 12.4 8.2 Species Sex 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 MOLOCH F 217 17.6 10 10.7 27.4 249 6.7 6 6.7 ND ND ND ND ND 167 27.8 14.9 12.7 7.3 MOLOCH M 211 19.5 8.9 10.1 29.2 249 7.8 7.9 7 ND ND ND ND ND 175 26.2 16.8 11.3 6.7 MOLOCH F 244 17.3 10.1 10.8 28.6 271 7.3 7.7 6.8 ND ND ND ND ND 183 25.5 16.8 11.4 7.2 MUELLERI M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLER! ND ND ND 6.8 6.9 22.7 175 7.5 4.5 4.7 147 12.4 6.9 7.5 7.3 126 18.7 13.7 7.9 5.5 MUELLERI F 221.5 17.6 9.1 9.8 25.7 257.5 7.1 6.3 6.7 185.5 15.2 8.8 9.6 9.4 166 24.5 15.3 10 6.5 MUELLERI M 206 17.9 9.2 9.3 24.5 250 7.1 6 6.7 186 15.4 9.9 9 8.8 156 24.6 14.7 10.7 6.5 MUELLERI M 216 19.7 10.8 11.5 28.2 258 8.4 8.3 7.7 198 17.1 7.1 11 10.7 174 26.7 17.4 12.5 7.7 MUELLERI F 213 16.7 9.5 9.7 25.9 ND 8.4 ND 190.5 16.1 9.1 9.8 9.9 161 23.6 16.3 10.6 7.1 MUELLERI F 195 16 8.7 8.7 25.3 231 8.1 6.6 6.7 177 15.2 7.1 9.4 9.4 154 23 15.9 9.6 6.4 MUELLERI M 222 16.7 8.7 9.1 25.8 263 8.6 6.2 5.9 195 15.3 8.2 9.4 8.7 170 23.3 17.2 9.7 6.4 MUELLERI F 206 15.7 9.9 10.6 26.8 226 7.6 6.7 6 174 15.4 7.4 10.7 10 153 22.9 17.8 10.2 6.9 MUELLERI M ND 18.1 ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND MUELLERI F 233 17.6 9.4 9.6 28.2 272 7.6 6.3 6.8 182 16.3 7.6 9.6 10.1 152 24.4 15.41 12.1 6.5 PILEATUS M 218 19.4 10.9 11 30.8 250 7.6 6.7 6.4 196 18.1 8.3 11.5 11 179 28.5 17.2 12.6 7.2 PILEATUS M 209 16.7 9.6 10.3 31 238.5 8 7.6 6.3 203 16.2 8.6 10 9.6 183 25.1 16.6 11.4 7.5 KLOSSII F 206.5 17.3 9.4 10.6 25.3 250 8.6 8.2 6.9 187 15.4 7.2 10.4 10 160 24.1 16.8 11.4 7.4 KLOSSII M 193 15.8 8.8 9.8 25.3 228 7.6 6.7 5.8 172 13.9 9.2 9.9 9.7 ND ND ND ND ND KLOSSII M 181 14.2 8.2 8.8 25.5 212 7.6 6.1 5.7 171 13.4 7.6 8.6 8.8 145 20.8 13.4 8.2 6.6 KLOSSII M 190 15.6 8.2 8.7 26.7 217 6.7 5.9 5.6 177 15.2 9.1 8.6 8.7 155 24.5 16.6 9.8 6.6 KLOSSII F 198 16.1 8.6 9.1 24.6 242 6.8 6.5 6.3 178.5 15.2 7.6 9.5 9.9 153.5 23.5 15.8 10.6 7.2 KLOSSII F 183.5 14.4 7.3 7.7 24.3 233 6.6 6.8 5.7 171 14.1 9 8.2 7.8 150 22.2 15.8 9.4 6 KLOSSII F 183 14.6 7.5 7.8 24.4 ND 6.8 5.6 5 171 14 9.7 8.3 7.9 148.5 22.2 15.5 9.5 6 KLOSSII F 205 16.2 8.1 9.2 25 241 6.5 7.1 5.9 179 15.2 9.1 12.1 10.1 160 23.6 15.4 9.8 6.9 KLOSSII M 215 17.8 9.3 10.1 27.3 257 6.4 7.5 7.6 194 16.4 8.6 10.5 10.4 171 25.6 16 11.8 7.6 Species Sex 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 KLOSSII M 203.5 15.9 9.1 10 24.6 250 5.8 6.7 6.4 180 15.6 7.7 9.7 10.7 167 24 15.9 11.1 7.5 KLOSSII M 198 16.3 8.6 9.4 25.2 234 7.3 6.6 6.2 178.5 15.5 7.6 9.8 9.7 155 24.1 16 10.6 6.9 KLOSSII F 206 16.1 8.9 10.5 23.4 240 6.3 7.3 5.7 191 16 9 9.9 10.6 163 24.8 15.1 11.2 6.9 KLOSSII M 189.5 19.27 8.24 8.72 25.65 223 7.69 6.73 6.24 175 14.55 8.57 8.76 8.59 156 23.39 15.14 9.33 6.72 HOOLOCK M 210 17.5 10 10 26.4 241 7.6 6.7 7 189 14.8 9.7 9.4 10 167 26.6 17.8 11 7.1 HOOLOCK F ND ND ND ND ND ND ND ND ND 212 15.4 8.1 9.8 9.9 190 25.9 15.8 10.6 6.1 HOOLOCK F 220 19.6 10.2 10.3 28.1 257.5 8 8.3 6.5 192 16.2 9.1 10.1 10.7 168 26.2 18.2 11.3 7 HOOLOCK F 217 19.3 11 11.2 27.8 253.5 8.2 7.9 7.9 187.5 15.7 9.2 10.5 11.7 166 25.6 16.7 12.1 7.3 HOOLOCK F 230 19.2 10.3 10.8 ND 256 8.5 8.2 7.3 ND ND ND ND ND 176 28.6 19.2 11.6 6.8 HOOLOCK M 219.5 16.3 9.4 9.4 26.2 238 7.8 7 6.4 192 16.1 10.6 8.6 9.6 168.5 24.4 15.9 11.1 6.6 HOOLOCK F ND ND ND ND ND 265 7.8 7.4 6.6 194.5 15.5 9.5 9.6 11.2 174 25.8 16.8 10.7 6.4 HOOLOCK ND 217.5 19.1 9.82 10.62 27.16 247 9.46 6.9 6.57 196 16.13 8.57 9.75 10.18 168 25.59 17.46 11.35 7.09 CONCOLOR F 228.5 20.1 11.7 12.4 30.5 263 7.7 8.5 8.3 193 17.9 8.7 11.7 12.2 166 27.8 19.4 14.4 7.1 CONCOLOR M 223 18.9 9.5 11.1 29.7 284.5 8.3 6.1 7.6 195.5 15.6 8.2 10.3 10 174 25.6 18 12.5 6.1 CONCOLOR F ND ND 9.61 10.54 26.99 245 6.87 6.76 5.58 196.5 16.45 8.06 8.96 9.11 157.5 23.78 10.24 5.63 CONCOLOR ND 161.5 13.83 7.08 7.53 23.4 193 6.05 5.91 4.65 139.5 12.09 9.99 7.02 6.82 126 19.59 13.6 7.11 4.65 GABRIELLAE M 252 19.1 9.4 9.6 27.4 295 8.8 6.6 6.5 190 15.3 9.5 9.7 9.6 167 23.9 18.2 10.1 6.5 GABRIELLAE F 237 19.9 11.3 10.5 31.3 275 8.1 8.1 7.4 195.5 17.1 9.9 10.2 10.3 167 27.8 18.4 12.1 7 GABRIELLAE F ND ND ND ND ND 299 8.5 8.7 6.1 ND ND ND ND ND 175 26.9 17.8 10.6 6.2 GABRIELLAE F ND ND ND ND ND 286 10 8.7 7.2 ND ND ND ND ND 175 26.5 18.4 12.2 6.7 GABRIELLAE F 225 19 9.3 9.9 26.3 265.5 6.7 7 6.9 184 15.3 7.7 8.9 9.4 160 24.3 16.6 9.3 6.1 GABRIELLAE M 239 18.6 9.8 9.4 27.2 276 7.3 7.2 7.7 200 15.9 8.4 9.1 9.5 170 24.1 17.3 10 6.4 GABRIELLAE M 246 19.4 9.7 10.7 29.2 288 9.2 8.4 8.2 198 15.7 7.8 10.6 10.7 172 25.7 17.7 10.8 7.4 GABRIELLAE M 237 19.3 10.1 10.3 27.6 285 7.4 6.9 7.5 191 16.5 7.7 10 10.4 173 25.1 17.5 11.1 7.6 GABRIELLAE M 238.5 18.4 9.7 9.3 27.3 295 6.9 8.9 7.2 210 16.9 9.2 9.7 10 178 25.8 16.7 12.5 6.3 GABRIELLAE F 234 18.6 9.5 9.6 26.9 277 8 7.8 6.8 198 16.4 8.6 9.4 10 168 25 17.9 9.9 6 GABRIELLAE F 228 18.9 9.5 10.2 27.4 274 6.9 7.9 7.1 189.5 16.2 8 9.5 10.6 167.5 24.7 16.7 10.5 6.5 GABRIELLAE M 230 18.7 10.2 10.3 26.5 280 8.3 8.1 6.9 190.5 15 7 10.2 10.6 167 23.7 15.8 10.7 7 GABRIELLAE M 236.5 20.6 10 10.3 30.8 281.5 9.5 8.4 7.5 197.5 17.3 8.5 10.2 10.6 ND ND ND ND ND LEUCOGENYS M 245 18.9 9.3 9.4 29.4 280 8.1 7.1 6.8 198 17.1 8.3 10.5 10.1 168 25 18.3 10.6 5.8 LEUCOGENYS F 237 18.2 10.3 10.9 29 282.5 7.5 8.1 7.4 ND ND ND ND ND 171 26.1 18.9 10.9 7.5 LEUCOGENYS ND ND ND ND ND' ND ND ND ND ND 200 16.3 8.5 10.2 9.8 ND ND ND ND ND LEUCOGENYS F 230.5 18.9 11.6 11.5 27.9 281 8.3 8.6 7.1 ND ND ND ND ND 166 26.2 17.1 10.8 7.4 Species Sex 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 SYNDACTYLUS F 230 18.5 9.7 8.7 29.1 253 8.9 7.8 5.6 196.5 17.8 11.3 9 9.8 170 26 19.9 9 6.4 SYNDACTYLUS M 275.5 24.3 12.8 14.1 35.9 298.5 8.9 10.4 9.4 226 23.6 13.5 13.5 13.6 184 33.8 23.7 14.1 9.2 SYNDACTYLUS F 294.5 22.3 10.8 9.7 30.4 334.5 9.9 8.8 6.7 230.5 20 11.6 10 11 195 28.1 20.8 10.3 7.3 SYNDACTYLUS M 242 21.6 11.4 9.2 31.6 276.5 9.8 7.8 7.1 201 19.9 11.1 10.7 10.2 179.5 28.9 21 10.1 6.6 SYNDACTYLUS F 249.5 22 12.5 12.5 31.7 286 8 9.2 7.4 197 19.6 9.8 12.1 11.4 176.5 30.4 21.4 11.9 8 SYNDACTYLUS F 228 19.5 10.3 9.2 34.1 258 8.8 8.4 7 188 19 12.7 9.4 9.6 ND ND ND ND ND SYNDACTYLUS ND 261 20.6 12.4 11.6 31.8 297 8.2 8.3 7.2 209.5 18.9 13.1 10.6 11.1 183.5 29.1 21.2 12 7.5 SYNDACTYLUS F 270 20.8 10.8 10.7 29.9 312 6.8 10 6.9 208 18 11.7 10.7 10.8 175 27.5 19.5 10.7 7 SYNDACTYLUS F 254 21.4 10.7 10.9 30.8 ND ND ND ND 193 19.3 11.2 10.9 10.8 168 27.3 19.1 11.5 7.5 SYNDACTYLUS M 270 21.7 11.4 10.4 32.3 301 8.3 9.1 8.5 207 20.2 12.4 11.6 11.4 177 28.6 21 11.8 8.6 SYNDACTYLUS M 275 23.8 11.6 12.3 32.3 304 8.5 9.5 7.7 209 21.3 10.5 11.4 12.2 178 30 22 13.6 7.5 SYNDACTYLUS M 265 23.5 11.5 10.9 30.2 312 9.7 10.6 8.4 202 19.9 11.8 11.1 11.4 174 30.9 20.9 11.6 8 SYNDACTYLUS F 257 21.9 13.7 12.3 30.1 297 10.4 9.3 7.8 203 19.4 13.2 10.9 11.4 170.5 28.5 20.1 11.2 7.6 SYNDACTYLUS M 285 24.5 12.6 12.4 33.6 335 8.9 10.2 8.7 222 22.8 11.9 12.6 12.5 195 30.9 21.1 12.4 7.5 SYNDACTYLUS M 255.5 22.8 11.8 10.4 32.5 292 9.9 9 8.5 200 19.6 10.6 11.4 11.5 177 29.8 19.9 13.1 8.3 SYNDACTYLUS F 269.5 22.1 12.4 11.3 35 305.5 8.7 9.5 8.1 213 20.1 9.8 12.2 12.4 183 29.7 20.7 13.1 8.1 SYNDACTYLUS F 261 22.4 12.3 11.7 32.6 300 8.9 9 7.8 207 20 12.6 11.4 11.8 ND ND ND ND ND u>w Postcranial Variables Species Sex 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 LAR ND 18.7 24.7 69.6 75.4 17 24.2 26 32.9 7.4 31.9 91.8 33.1 29.9 16.3 19.9 LAR ND ND ND ND ND ND ND ND ND ND 73.4 30.1 22 14.2 19 LAR 25 30.5 64.4 88.4 17.5 25.9 25.7 ND ND ND 90.5 32.5 31.3 18.1 21.5 LAR M ND ND ND ND ND 13.2 19.5 27 10.3 67.3 25.5 20.8 13.8 15.6 LAR M 23.7 22.5 72.8 87.3 20 ND ND ND ND ND 88.1 38.5 28.4 22.3 20.9 LAR M 16.3 22.9 63.3 77.5 15.5 22 22.5 29.3 9.4 21.7 84.8 30.9 26.9 19.3 18.7 LAR 19.5 26.7 62.9 ND Nd 19.5 26.7 34.7 11.7 17.4 87 35 31.8 15.9 20.9 LAR 25.6 24.1 66.7 91.5 18 ND ND ND ND ND 91.9 37 33.8 16.8 23.9 LAR M 23.6 30.5 65.7 80.6 18 ND ND ND ND ND 91 36.2 32.4 18.2 19.9 LAR M 29.4 27.5 70.3 94 19.5 ND ND ND ND ND 96.6 45.7 30 18.9 21.2 LAR 20.9 27.7 64.9 82.9 19 24.3 29.1 32.1 11.1 16.8 93.3 48.1 32.8 18.9 21.2 LAR 25.5 20.6 64.5 87.1 18.5 ND ND ND ND ND 90.5 48.9 29.9 15.5 20.5 LAR M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND LAR ND 20.3 28.7 57.2 82.7 20 18.2 19.5 27.7 12.5 ND 88.5 40.9 28.8 18.8 19.6 LAR M 22.1 26.1 69 90.9 18.5 ND ND ND ND ND 98 52.1 31.3 19.4 21.1 AGILIS M 23.6 31.4 60.4 84.4 20 23.5 21.4 32.2 10.5 13.6 90 38 30.7 17.6 19.8 AGILIS M 21.9 32.5 59 94.9 18 19.4 21.8 30.9 13.2 26.4 92.3 46.8 25.5 17.7 21.1 AGILIS F 20.9 26.1 57.3 86.4 18 18.9 18.6 31.6 7.9 32 85.9 40 31.6 17.8 20.4 AGILIS M 20.9 32.3 61.3 85.8 20.5 23.9 23.9 28.8 10.8 15.9 90.3 45.3 30 18.5 20.8 AGILIS M 25.8 33 66.1 103 20.5 23.7 26.4 39.4 10.3 32.1 96.3 40 33 22.5 20 AGILIS M 29.1 34.6 66 98.4 22.5 30.1 23.9 29.9 16.2 23.3 98.7 50.3 29.8 19.4 23.6 AGILIS M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND AGILIS F 29.1 32.1 57.2 91.2 20.5 24.6 28.9 31.7 12.3 24.3 89.2 47 29.4 17.4 20.6 AGILIS M 28.5 28.1 66.4 92.8 20 26.1 27.4 32.1 8.7 21.7 95 54.2 31.4 21.9 20.1 AGILIS M 24.3 26.6 61.2 96 21 20.3 23.5 34.1 8.8 25.1 88.9 39.9 28.2 22.1 19.5 AGILIS F 27.9 26.2 65.9 91.9 19.5 23.6 24 35 7.9 18.5 98.5 46.7 29.9 20.9 20.1 AGILIS M 22.2 31.7 63.2 89.4 ND 22 21.5 28.1 10.4 11.7 91.6 41.5 30 17.2 20.9 AGILIS M 23.2 31.3 61.2 92.6 ND ND ND ND ND ND 97.7 43.4 36.8 17.8 21.3 MOLOCH F 17.4 20.7 50 70.6 13.5 14.3 21.2 21.8 12.1 27.8 74.8 38.7 26 12.2 16.2 MOLOCH M 19 21.8 47.7 69.4 13 14.5 21.4 25.5 9.8 15.6 75.4 34.2 21.2 13.2 16.2 MOLOCH M 25.3 25.4 66.1 86.2 18 26 28.6 27.5 18.9 ND 99.3] 46.1 30.3 20.5 21.3 MOLOCH M 21.6 29.6 60.8 85.4 12.4 21.9 26.2 37.4 11.1 21 94.3 41.5 33.8 20.6 20.8 Species Sex 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 MOLOCH F 24.7 26 60.6 89.3 12.3 19.1 19.7 30.6 11.1 25.4 86.2 45.5 35.3 15.1 21.3 MOLOCH M 25.7 28.2 58.6 84.2 12.4 ND ND ND ND ND 88.8 37.5 33.1 19.2 21.3 MOLOCH F 24.1 29.7 62.6 94 12.3 23.9 21 29.7 12 30.4 105 46.9 40 20.1 20.9 MUELLERI M 24.8 28.3 59.3 80.2 18 ND ND ND ND ND 97.3 46.5 30 19.8 19.8 MUELLER! ND 19 22.1 38.8 64.1 14 13.6 14.5 20.7 8.2 21.7 68.3 28.3 18.9 12.4 16.4 MUELLERI F 27.4 24.6 56.9 88.4 18 21.9 22.3 24.3 14.7 29.3 88.4 44.5 30.9 15.7 19.7 MUELLERI M 22.1 25.4 59.4 82.9 18 22.2 23.5 28.8 11.3 22.4 82.1 40.6 28.3 16.5 20.6 MUELLERI M 29.2 26.7 61.8 86.4 23 28.3 21.5 26.6 10.7 42 93.3 40.7 28.2 19.5 20.1 MUELLERI F 23.2 22.2 62.8 82.6 18 19.2 22.5 26.5 11.9 ND 95.3 49.3 31.8 14.8 20.5 MUELLERI F 23.5 24 61.2 86.3 18 23.5 21.5 26.6 9.8 42 95.1 38.1 32.3 ND 19.2 MUELLERI M 19.3 27 62 92.5 17.5 24.4 18.4 26.1 11.2 40.4 88.7 43.1 29.2 19.5 19.4 MUELLERI F 17.8 26 60.1 76.4 19.5 27.5 21.5 25.3 11.4 18.9 80.1 49.1 31.1 19.2 19.1 MUELLERI M 22.1 27.7 61.1 80.1 20 28.2 24 27.8 11.8 22.7 87.2 43 28 14.2 19.5 MUELLERI F 23.2 27.8 59 87.3 21 ND ND ND ND ND 91 53 34.6 21.6 19.9 MUELLERI M 23 28.2 53.7 83.7 20 21.7 26.7 30.7 11.6 17.3 90.2 46.3 28.7 16.4 19.3 MUELLERI M 23.9 31 66.3 91.9 22 26 29.8 36.1 10.7 17.7 95.1 48.3 32.9 21 21.8 MUELLERI F 24.6 30.8 57.3 81.4 20 25.9 25 ND ND 20.9 92.4 44 30.3 17.9 19 MUELLERI M 24 31.2 58.7 88.2 17.5 25.1 24.5 30.4 13.1 15 87.3 36.2 29.4 17.8 20.2 MUELLERI F 21.1 25.4 57.5 90.5 17 22.5 24.2 32.7 10.7 29.4 90.5 37.3 32.5 17.2 19 MUELLERI F 20 26.9 64.8 81.5 18.5 ND ND 26.1 9.8 17.6 92.1 33.6 29.1 18.4 20.3 MUELLERI F 22.5 29.4 60.6 83.5 19 25.2 23.6 21.7 12.5 22.5 87.6 47 33.1 18.4 19.5 MUELLERI F 26.2 29.2 57.2 85.1 12.1 23.4 22.2 27.8 11 31 89 35.8 36.5 19.4 21.7 PILEATUS M 24.8 33.6 61.1 87.41 21 20.1 28.5 33.5 16.1 37.6 93.1 39.1 30.1 16.4 23.3 PILEATUS M 23 26.3 53.5 77.9 17.5 ND ND ND ND ND 83 33.6 26.3 16.2 23 KLOSSII F 21.6 33.4 55 83.4 19.5 22.1 24.4 29.2 9.9 21 86.9 49.7 30.2 19.3 20.1 KLOSSII M 20.2 29.8 53.5 73.9 18 ND 22.2 ND 11.8 21.2 87.6 49.1 33.4 17.6 17.2 KLOSSII M 15.9 22.4 47.8 69.1 15.5 15.5 18.4 23.9 9.2 16.7 79.4 41.2 26.4 17.3 18 KLOSSII M 17.2 28.4 47.7 65.8 14.5 ND ND ND ND ND 76.5 42.6 23.9 15 21.2 KLOSSII F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND KLOSSII F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND KLOSSII F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND KLOSSII F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND KLOSSII M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND Species Sex 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 KLOSSII M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND KLOSSII M 22 32.9 55.1 83.6 18 20 21.5 23.3 11.4 24.2 85.4 47 29.8 22.2 19 KLOSSII F 19.4 30.8 52.8 76.2 18 ND 21.9 12.6 33.3 87.1 51.5 31.8 16.8 18.9 KLOSSII M ND ND ND 73.1 ND ND ND ND ND ND ND ND ND ND ND HOOLOCK M ND ND ND ND ND 28.8 20.8 29.8 13 41.6 89.2 41.7 30.5 16.1 19.5 HOOLOCK F 21.6 35.8 62.1 97.4 18.5 ND ND ND ND ND 99.4 41.2 34.9 19 20.6 HOOLOCK F 27.1 32.3 58.2 90.7 19.5 26.4 26.1 31.7 13.4 31.3 ND ND 36.7 15.5 20.5 HOOLOCK F 24.1 38.8 61.3 93.3 20 25.9 27.9 30.3 18.6 33.7 90 44.5 36 19.1 20.3 HOOLOCK F ND ND ND ND ND ND ND ND ND ND 104.8 42.4 36 18 21.6 HOOLOCK M ND ND ND ND ND 21.4 19.3 24.6 13.5 22.8 87 38.8 26 14.8 21.6 HOOLOCK F 21.5 37 59 ND ND 25.7 23.4 30.6 18.7 29.2 95.5 47.1 31.7 18.1 20.9 HOOLOCK ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND CONCOLOR F 27.5 33.6 63.6 92 19 31.8 ND 41.4 9.4 ND 94.4 48.6 29.1 18.2 21.8 CONCOLOR M 28.4 35 59.3 95.7 17.5 22.7 ND ND ND ND 91.2 42.2 27.5 17.8 20.9 CONCOLOR F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND CONCOLOR ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND GABRIELLAE M 22.2 28.4 62 89.8 19 20.4 20.7 47.1 11.4 13.4 80.4 33.8 27.9 17.8 20.7 GABRIELLAE F 28.6 40.3 60.9 98.7 23 27.1 23.8 38.8 11.9 26.6 94.5 48.6 32.5 15.7 22.9 GABRIELLAE F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND GABRIELLAE F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND GABRIELLAE F 21.6 29.5 56.7 87.2 19.5 ND ND ND ND ND 87 43.5 29.6 12.6 19.6 GABRIELLAE M 20 29.1 55.9 85.7 18 ND ND ND ND ND 84.4 40.1 27.6 14.6 21 GABRIELLAE M 22.7 35.2 61.1 91.9 21.5 ND ND ND ND ND 93.2 45.3 37.6 19.2 20.7 GABRIELLAE M 26.3 33.6 55.8 99.8 21 ND ND ND ND ND 91.4 49.2 29 15.6 21.6 GABRIELLAE M 23.7 32.4 59.7 95.8 21 ND ND ND ND ND 88.4 35.5 28.6 14 21.9 GABRIELLAE F 17.9 34.1 54.7 92.8 19 ND ND ND ND ND 90.9 42.7 32 14.8 21.3 GABRIELLAE F 25 28.1 59.2 95.3 20 24.9 20.5 39 10.1 32.7 94.7 43.1 33.4 16.6 21.4 GABRIELLAE M 26.3 30.4 58.9 93.4 20.5 21.6 25.9 41.6 9.3 38.2 ND ND ND ND ND GABRIELLAE M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND LEUCOGENYS M 26.6 43.8 52.4 97 20 30.6 22 39.4 10.2 28 98.8 45.1 38 14.4 22.3 LEUCOGENYS F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND LEUCOGENYS ND 26.6 30.1 64.1 94.4 20.5 24.7 23.9 40.9 8.6 25.8 97.3 47.7 36.9 17.6 19.8 LEUCOGENYS F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND Species Sex 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 SYNDACTYLUS F 20.8 26.1 60.6 89.3 19 22.4 18.2 15.7 15.2 39.1 92.2 48.6 30 14.7 23.2 SYNDACTYLUS M 31.4 44.1 65.8 108 25 ND ND 27.7 19.9 59 ND ND 42.2 20.7 30.6 SYNDACTYLUS F ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND SYNDACTYLUS M 25.7 32.7 67.1 98.1 19 21 24.1 23.2 13.2 37.7 103.9 48.2 38.5 17.9 25.3 SYNDACTYLUS F ND 35.1 73 108 21 25.4 27 21.1 12.1 ND 106.9 64.8 37.8 19.1 24.5 SYNDACTYLUS F 19.9 30.5 64 90.4 17.5 ND ND ND ND ND 104.5 51.1 35.7 16 23.4 SYNDACTYLUS ND 26.9 36.1 69.6 103.1 19 ND ND ND ND ND 112.1 63 37.1 16.5 25.2 SYNDACTYLUS F 18.6 32.9 70.1 110 22 24.3 ND ND ND ND 108.1 61.7 35.9 16.7 24.9 SYNDACTYLUS F 27.4 31.6 71 94.5 22 19.6 27.8 29.4 14 ND ND ND ND ND ND SYNDACTYLUS M 29.9 36.1 74.8 109.4 23 22.6 31.6 Nd ND ND 116.5 69.7 39.5 21.4 23.9 SYNDACTYLUS M ND ND ND ND ND ND ND ND ND ND ND ND ND ND ND SYNDACTYLUS M 26.8 32.6 70.6 94.7 21 ND ND ND ND ND 104 60.7 40 17.7 22.9 SYNDACTYLUS F 32.3 39.8 69.6 114.4 25 22.9 30.3 29.5 14.2 23.5 105.8 66.4 41.5 20.4 25.1 SYNDACTYLUS M 31.9 42.6 81.6 ND ND 27.5 31.6 32.2 13.9 ND 122.3 69.8 39 21.8 27.7 SYNDACTYLUS M 26.7 32.9 71.2 ND ND ND ND ND ND ND 105.4 61.4 35.4 19.5 22.9 SYNDACTYLUS F 30.3 32.2 75.1 111.9 22 23.9 31.4 27.6 17.4 31.7 111.6 64.8 40.7 18.1 22.9 u> SYNDACTYLUS F 26.7 28.8 74.6 98.5 23 ND ND ND ND ND 108.1 64 40.1 21.1 26.3 ut U t Postcranial Variables Species Sex 42 43 44 45 46 47 4849 50 51 52 53 HUMAN F 323 45.1 19.5 19.8 58.1 253 16.1 11.2 14.4 442 43.3 26.5 HUMAN F 319.5 42.1 20.8 21.7 64.9 261 17.3 15.8 12.8 459 51.2 28.6 HUMAN F 323.5 42.4 19.7 19.8 63.2 255.514 13.7 12.8 438 42.8 19.4 HUMAN M 329.5 41.3 18.8 19.7 60.5 271 14.3 11.9 13.6 447 45 29.1 HUMAN F 323 43.9 22.920.7 64.9 26913.9 14.6 14.9 450 45.1 26.3 HUMAN M 306.5 41.5 20.3 20.7 61.4 262.5 14.7 17.9 12.2 453.5 42.1 22.6 HUMAN F 288.5 41.2 18.8 16.3 58.2 244.5 1415.3 10.4 413.5 41.9 22.4 HUMAN M 315 44.3 22 19 60.5 275.5 16.3 13.1 14.2 429.5 43.225.8 HUMAN F 312 41.6 19.2 18.1 60.9 273.5 14.7 13 12 436.5 42.219.6 HUMAN M 327 45.3 20.9 20.5 60.8 265 16.913.6 13.9 429.5 49.1 22.7 HUMAN M 351 44.1 20.3 18.1 60.4 300.5 15.1 12.4 15.9 415 42 27.9 Species Sex 54 55 56 57 58 59 60 61 62 63 64 65 HUMAN F 28.8 26.3 38372.1 45.9 32.4 18.7 36.8 113.4 99.2 153 43 HUMAN F 28.9 29.9 369.5 79.9 59.822.7 31 45.9 131.1 104.5 155 45 HUMAN F 25.6 25.7 363 68 49.2 29.2 22.8 41.8 112.8 96.7146.6 45 HUMAN M 28.5 24.2 380.5 71 48.928.6 20.7 42.8 117 102.6 150 44 HUMAN F 32.7 29.7 380 76 51 32.4 21.1 42.7 118.6 103.6 137.7 45 HUMAN M 29.2 24.9 399.5 66 45.7 29.120.7 35.6 107.8 98.1 143.8 36 HUMAN F 22.7 22.4 336 64 50 24.9 24.4 46.9 104 95.5 132 36.5 HUMAN M 26.9 24.5 370.5 73.5 49.8 29.2 24.5 44.5113.4 105.9 152.3 42 HUMAN F 28 23.4 363 68.5 48.3 26 20 36.5 120.8 98.5 143.1 40 HUMAN M 25.7 24 368.5 67.5 48.6 25.1 18.7 47.1 115.8 96.4140 39.5 HUMAN M 23.7 23.3 378 64 45.7 27.8 19 46 121.4111.5 153.3 40 Postcranial Variables Species Sex 66 67 68 69 70 71 72 73 74 75 HUMAN F 51.9 55.7 89.7 43.1 18.8 121 156 72.9 44 48.4 HUMAN F 53.5 49.7 93.4 30 32.5 137.5 165 63.7 43.2 55.2 HUMAN F 49.6 51.9 97.4 28.9 18.6 124.5 128 67.5 40.8 54.4 HUMAN M 39 44.5 99.6 36.1 29.7 128 129 59.3 42.9 52.4 HUMAN F 50.2 59.2 98.7 32 21.9 137.5 127 73.6 48.8 51 HUMAN M 57.3 43.6 70 40.2 38.9 120 143.5 60.3 46 49.8 HUMAN F 51.4 51.4 88 28.3 36.3 114 144 66.7 45.4 48.3 HUMAN M 64.4 57.3 95.3 30.1 31.9 124 145.5 63.3 55.9 49.1 HUMAN F 47.2 42.6 88.4 27.1 31.7 120 140 66.1 48 52.3 HUMAN M 42.4 39.1 101.5 28.1 33.1 125.5 144.5 64.7 41.5 50.6 HUMAN M 50.6 44.4 100 26.7 29.8 137.5 158.5 69.3 50.5 51.6 u> w Craniodental Variable Numbers 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Mean 3.9 4.7 4.0 3.9 5.5 7.1 14.5 23.2 22.4 12.3 26.0 13.4 69.3 63.676.8 39.820.3 18.4 11.3 53.5 54.0 81.5 60.5 17.5 30.6 Standard Deviation 0.4 0.5 0.5 0.4 0.7 0.8 2.6 1.3 1.3 2.1 3.3 1.6 5.8 5.3 7.4 4.6 2.0 2.1 1.5 3.5 6.2 5.4 4.9 3.5 3.3 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 4.1 5.9 4.3 5.1 5.06.3 5.6 6.8 5.56.4 56.3 26.0 51.2 43.3 19.5 19.0 0.4 0.8 0.4 0.6 0.5 0.7 0.6 0.80.8 1.0 5.0 3.5 4.0 4.3 2.7 2.0 Postcranial Variable Numbers 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 Mean 227.7 18.5 9.8 10.1 2Ô.0 264.3 8.07.5 6.8 195.0 16.4 9.1 10.0 10.1 169.9 25.417.2 10.9 6.9 23.8 30.0617 89.0 Standard Deviation 24.8 2.3 1.4 1.2 2.8 28.5 1.0 1.2 1.0 15.6 2.1 1.6 1.2 1.2 13.3 2.5 2.1 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