Cladistic Analysis of Juvenile and Adult Hominoid Cranial Shape Variables

by

Thomas A. DiVito II

A Thesis Submitted to the Faculty of

The Dorothy F. Schmidt College of Arts and Letters

in Partial Fulfillment of the Requirements for the Degree of

Master of Arts

Florida Atlantic University

Boca Raton, Florida

August 2011

Copyright by Thomas A. DiVito II 2011

ii Cladistic Analysis ofJuvenile and Adult Hominoid Cranial Shape Variables

by

Thomas A. DiVito II

This thesis proposal was prepared under the direction of the candidate's thesis advisor, Dr. Robert C. McCarthy, Department of Anthropology, and has been approved by the members ofhis supervisory committee. It was submitted to the faculty of the Dorothy F. Schmidt College of Arts & Letters and was accepted in partial fulfillment of the requirements for the degree ofMaster ofArts.

SUPERVISORY COMMITTEE:

~ H~ Ctt·:-{·2 Michael S. Harris, Ph.D. Chair, Department ofAnthropology ~~ Heather Coltman, D.M.A. Interim Dean, The Dorothy F. Schmidt College ofArts & Letters 1?~.,-. ~~...... Barry T. Rdsson, Ph.D. Dean, Graduate College

111

Acknowledgements

Without the support of my thesis committee, department, museum staffs, friends, and family, I could not have completed this thesis. I am incredibly grateful for Dr. Robert

McCarthy‟s continued patience, knowledge, and guidance. You instructed me every step of the way, allowing me to complete this thesis with confidence. Conversations with Dr.

Clifford Brown were instrumental for deciding useful test statistics. Dr. Deborah

Cunningham‟s comments and revisions were invaluable. Dr. Doug Broadfield‟s assistance and support were crucial for completing this thesis and graduating.

I would also like to thank the remaining faculty, staff, and students of the

Anthropology Department at Florida Atlantic University for your concern, help, and encouragement during the completion of this thesis. A special thank you to Cynthia

Wilson, for her much welcomed encouragement, April Watson, for her thesis-related guidance, and James Wheeler – my colleague, roommate, and friend – for his unqualified assistance. Without continued support and funding from the Anthropology Department, I could not have completed this thesis. Thank you, Dr. Michael Harris and the Morrow

Research Fellowship in Anthropology Committee, for providing me with the ability to travel to collect my data.

I also extend deep gratitude to the museum collections staffs. Lyman Jellema of the Cleveland Museum of Natural History, Candace McCaffery of the University of

iv

Florida, and Darrin Lunde of the Smithsonian Institution National Museum of Natural

History, thank you for your assistance and camaraderie during my data collection.

I am very thankful for the generosity of new and old friends. Carl and Dorothy, thank you for welcoming me into your home during my thesis travels. My stay in

Cleveland could not have been more enjoyable, and I look forward to visiting again.

Rick, I couldn‟t have navigated D.C. without your help. It was great to see you again, and

I‟m very grateful that you shared your home with me. Thank you, Dr. Michael Noonan of

Canisius College, for your continued mentorship, as I move through the ranks of academia.

The love and support I receive from my family and friends are unparalleled.

Despite the geographic distance, my family, immediate and extended, never fails to provide encouragement. Mom, Dad, Gramma and Grampa, thank you for your endless patience and love, especially when it was most required. Your support was absolutely necessary. Gladys, your love and encouragement was vital. You are amazing. Thank you.

Without you and your family, I would not have completed this thesis. I am forever grateful and incredibly fortunate to have such a wonderful and supportive network of friends and family. Thank you all.

v

Abstract

Author: Thomas A. DiVito II

Title: The Role of Ontogeny for Reconstructing Hominid Phylogeny

Institution: Florida Atlantic University

Thesis Advisor: Dr. Robert C. McCarthy

Degree: Master of Arts

Year: 2011

Phylogenies constructed from skeletal data often contradict those built from genetic data. This study evaluates the phylogenetic utility of adult male, female, and juvenile hominoid cranial bones. First, I used geometric morphometric methods to compare the cranial bone shapes of seven primate genera (Gorilla, Homo, Hylobates,

Macaca, Nomascus, Pan, and Pongo). I then coded these shapes as continuous characters and constructed cladograms via parsimony analysis for the adult male, female, and juvenile character matrices. Finally, I evaluated the similarity of these cladograms to one another and to the genetic phylogeny using topological distance software. Cladograms did not differ from one another or the genetic phylogeny less than comparisons of randomly generated trees. These results suggest that cranial shapes are unlikely to provide accurate phylogenetic information, and agree with other analyses of skeletal data that fail to recover the molecular phylogeny (Collard & Wood, 2000, 2001; Springer et al., 2007).

vi

Dedication

I am eternally grateful for the love and support of my family. Mom, Dad, Gramma,

Grampa, and especially Gladys, this thesis is for you.

Cladistic Analysis of Juvenile and Adult Hominoid Cranial Shape Variables

List of Tables ...... ix

List of Figures ...... x

Introduction ...... 1

Historical Background and Overview ...... 4

Phylogenetic Systematics ...... 5

Character Coding...... 6

Molecular Phylogenetics and Fossil Taxa ...... 8

Geometric Morphometrics ...... 9

Hypotheses ...... 13

Materials and Methods ...... 14

Specimens...... 14

Landmarks ...... 16

Error Testing ...... 23

Character Identification ...... 24

Cladistic Analysis ...... 26

Cladogram Comparison Tests ...... 26

vii

Results ...... 31

Parsimony Analysis ...... 31

Topological Distance Tests ...... 34

Discussion ...... 35

Conclusions ...... 39

Appendices ...... 40

Appendix A: Specimens ...... 41

Appendix B: Cranial Bone Landmark Subsets ...... 45

Appendix C: Data Matrices ...... 51

References ...... 56

viii

List of Tables

Table 1. Pairwise comparisons of the four cladograms...... 13

Table 2. Specimens by genus and class...... 15

Table 3. Landmark descriptions...... 17

Table 4. Procrustes ANOVA results...... 24

Table 5. D‟Agostino Omnibus test statistics...... 30

Table 6. Frequency distribution for guided randomization analysis...... 30

Table 7. Frequency distribution for random randomization analysis...... 30

Table 8. Topological distances of pairwise cladogram comparisons...... 34

ix

List of Figures

Figure 1. Lateral view of cranial landmark locations...... 20

Figure 2. Anterior view of cranial landmark locations...... 21

Figure 3. Inferior view of cranial landmark locations...... 22

Figure 4. Conversion of one tree to another via the “splits” or “partition metric.” ...... 28

Figure 5. Hominoid genetic phylogeny...... 31

Figure 6. Maximum parsimony cladogram of adult male cranial shapes...... 32

Figure 7. Maximum parsimony cladogram of adult female cranial shapes...... 32

Figure 8. Maximum parsimony cladogram of juvenile cranial shapes...... 33

x

Introduction

The Hominidae is a family in the order Primates. The four extant genera in this family are Gorilla (gorillas, two species), Homo (humans), Pan (chimpanzees, two species), and Pongo (orangutans, two species) (Wilson and Reeder, 2005). Hominid relationships are central to understanding human evolution since they implicitly influence anthropological hypotheses by determining which hominid species are functionally and developmentally compared to human ancestors.

Unfortunately, cladograms constructed from skeletal characters of the five extant hominoid genera have not always matched those supported by genetic data (Van Valen,

1989; Collard & Wood, 2000, 2001). Skeletal characters did not universally yield the

Homo-Pan clade. Instead, cladistic analyses often recover a Homo-Pan-Gorilla trichotomy or a Pan-Gorilla clade (Collard & Wood, 2000, 2001). Ruvolo (1997) and

Satta et al. (2000) corroborated the Homo-Pan clade by using multiple lines of evidence from different DNA sequence sets to build their respective phylogenies.

Systematists have increasingly utilized DNA and other biochemical media to construct evolutionary relationships as the technology for molecular analyses becomes more efficient and accessible. This should come as no surprise, considering that DNA is directly inherited by offspring from their parents. It is therefore the most valid indicator of relatedness. Despite the strengths of DNA, paleontologists are forced to rely upon morphological characters to construct evolutionary relationships among organisms.

1

Though morphology is ultimately an extension of DNA, this approach is limited due to the effects of the environment and growth on bone development.

Anthropologists have confronted the discrepancies between genetic and skeletal phylogenies with a variety of techniques. Strait and Grine (2004) conducted a traditional cladistic analysis with primarily discrete, qualitative characters and included fossil taxa to successfully resolve hominid evolutionary relationships. Lockwood et al. (2004) utilized multivariate shape analyses, called “geometric morphometrics,” of the temporal bone and phenetic neighbor-joining to build an accurate hominid phylogeny. Lastly, González-José et al. (2008) combined geometric morphometric techniques and parsimony analysis by codifying integrated cranial regions as shapes as continuous character states. Their results also appear to produce an accurate hominid phylogeny.

Others have proposed that skeletal characteristics fail to resolve hominid phylogeny because similar interaction with the environment during the growth and development of two organisms may also cause similar morphologies, regardless of degree of relatedness (Lieberman, 1997; Lycett & Collard, 2005; von Cramon-Taubadel, 2009).

These so-called homoiologies result from constant contact between the organism and environment. Lieberman (1997) comprehensively reviewed the effects of different environmental strains on bone growth and development. For example, mastication-related mechanical loading results in variation in hominid cranial characters (Lycett & Collard,

2005). Bones experiencing similar environmental strains will develop similarly.

However, despite the implications of Lockwood (1997), von Cramon-Taubadel (2009) explored the effects of homoiology on phylogeny, indicating that their effect is minimal.

2 However, ontogenetic factors, in addition to environmental factors, also affect skeletal morphology. Great morphological changes take place as organisms transform into adults, the most prominent changes occurring within highly sexually dimorphic species. Ponce de León and Zollikofer (2001) and Weaver (2009) suggest assessing infant morphology to evaluate the phylogenetic utility of a given trait. Character state differences between organisms at earlier life stages would indicate that said differences were not due to environmental strain (i.e., they are not homoiologies).

By combining the approaches of Lockwood et al. (2004) and González-José et al.

(2008), the following thesis examines the phylogenetic utility of cranial bone shapes when coded as continuous cladistic traits in a parsimony analysis. I also evaluated the impact of environmental interaction and ontogenetic growth, as suggested by Ponce de

León and Zollikofer (2001) and Weaver (2009), by collecting geometric morphometric shape variables from both adult and juvenile hominids.

3

Historical Background and Overview

In 1758, Carl von Linné diverged from the other naturalists of his time by including the genera for humans and non-human apes in the same order, which he named

"Primates" (Gyenis, 2002). Linné's insight was later recognized by Charles Darwin, in

The Descent of Man, and Selection in Relation to Sex, published in 1871. The hypothesized relationships among the extant hominoids have continued to change over the course of 150 years. These changes were motivated by the systematization of phylogenetic methods and acquisition of new (previously unavailable) biological media.

George Gaylord Simpson (1945, 1961) formalized the Linnean distinction between humans and non-human apes within the order Primates by placing the genus

Homo in the family Hominidae, and the other ape genera (Pan, Gorilla, Pongo, and

Hylobates) in the family Pongidae. The classification paradigm of Simpson's time,

"evolutionary systematics," coupled with the available morphological data, informed his decision to retain the separation between humans and other apes. Evolutionary systematics stresses the importance of both common ancestry and morphological similarity when defining taxonomic units (Mayr & Bock, 2002). In this case, the anatomical similarities among the non-human apes warranted their inclusion in a single family, while their differences from humans warranted a separate family for humans.

However, this interordinal division would not last past the mid-1960s, when

4

Goodman (1963) first utilized historically-novel techniques to measure the degree of similarity among primate serum proteins.

The results of the Goodman (1963) analysis justified rearranging the extant

Hominoidea into three groups. The distinctiveness of gibbon and orangutan serum warranted a group for each taxon (Hylobatidae and Pongidae, respectively), while the similarity between human, chimp, and gorilla protein serums corroborated an African ape subset (Hominidae). Despite the evidence for different hominoid groupings, the relationships among these groups remained unresolved. Biochemical analyses after

Goodman (1963) consistently supported these groupings, in addition to refining this initial trichotomy (Gyenis, 2002). However, morphological analyses and the influence of

"phylogenetic systematics" - more commonly known as "cladistics" - would first sharpen the methodology for evaluating hominoid relationships.

Phylogenetic Systematics

Willi Hennig‟s seminal work, Phylogenetic Systematics, was translated into

English from German in 1966. Hennig‟s phylogenetic systematics minimized the importance of overall similarity for inferring relatedness, which starkly contrasted with the methodology of evolutionary systematists (Kitching et al., 1998). Instead, Hennig emphasized the importance of common ancestry for determining relatedness, since similarity does not necessarily indicate recent shared ancestry. Hennig‟s system for identifying common ancestry and exposing these different types of similarity utilized

“characters” – morphological, physiological, behavioral, ecological, or molecular qualities of organisms – which were discretely coded as “present” or “absent” for a given

5 organism (Kitching et al., 1998). Hennig identified two basic types of states for a given character - “apomorphic,” meaning “away from the ancestral condition” or “derived;” and “plesiomorphic,” meaning “close to the ancestral condition” or “primitive” (Kitching et al., 1998). Although there are multiple strategies to polarize character states as either apomorphic or plesiomorphic, systematists frequently rely on the character states of

“outgroups,” taxa distantly related to the group under analysis, and to the ontogenetic development of the given character state (Watrous & Wheeler, 1981; Kitching et al.,

1998). The fundamental purpose of coding biological qualities into character states is to discover “sister group” relationships between taxa – the hypothesis that two taxa are more closely related to one another than either is to any other taxon (Kitching et al.,

1998).

To identify sister groups, taxa with shared apomorphic character states, called

“synapomorphies,” are linked together to form tree diagrams, called “cladograms”

(hence, “cladistics”). Cladistic algorithms cluster synapomorphies into a nested hierarchy by arranging the taxa on a cladogram topology that accounts for the greatest number of character states in the simplest way (i.e., the most parsimonious tree) (Kitching et al.,

1998). Guided by parsimony analysis, cladistic clustering algorithms help a systematist to select the most economical hypothesis for the intergroup relationships of a given set of taxa, while simultaneously falsifying other, initial hypotheses of homology.

Character Coding

Translating Hennig‟s phylogenetic theory into computer algorithms is relatively straightforward compared to the challenge of coding most biological qualities into

6 character states. Characters are described with reference to two dichotomies – qualitative vs. quantitative, and discrete vs. continuous (Kitching et al., 1998). Traditionally, cladists prefer qualitative to quantitative characters and discrete to continuous characters since coding continuous variation into distinct categories has proven difficult. However, the vast majority of biological variation is continuous, especially between lower taxonomic units (i.e., species and subspecies). Stevens (1991) asserts that many qualitative characters are actually quantitative in nature, and Macleod (2002) adds that even seemingly discrete characters, like color, are actually continuous. To cope with biological reality, cladists have devised methods to code these problematic continuums.

Since most biological attributes are continuous and quantitative in nature, ignoring these as characters does not seem reasonable. Therefore, rule-based methods for transforming this variation into more favorable character types have been devised

(Kitching et al. 1998; Macleod, 2002). Simply put, these methods, collectively described as “gap-coding,” help cladists to create gaps within a range of quantitative and continuous values (Macleod, 2002). For example, the “gap weighting” method presented by Thiele (1993) scores character states ordinally, according to the relative relationship of each state to one another and also by the size of the gap between each state. Alternatively, homogeneous subset coding utilizes tests of variance to identify gaps among distributions of quantitative measurements (Rae, 1998).

Physical anthropologists increasingly adopted cladistic methods during the 1980s

(Delson, 1983; Harrison, 1993). The rigor and scope of cladistic methodology helped researchers to not only substantially corroborate their phylogenetic hypotheses, but also to refine hominoid relationships. Regardless of the specific character coding utilized

7 during this time, the cladistic analyses of hominoid morphology generally converged upon the Homo-Pan clade as a sister group to Gorilla (Schwartz, 1984). However the continued prevalence of conflicting results, including Pan-Gorilla and Homo-Gorilla groupings, prevented a consensus. The primacy of molecular phylogenetic methods and novel modifications of traditional cladistic analyses during the late 20th and early 21st century would finally resolve this African hominid trichotomy.

Molecular Phylogenetics and Fossil Taxa

The advent of DNA sequencing allowed systematists to sidestep the problem of character coding. Nucleobase positions provided many new characters, each with qualitative, discrete states (e.g., A, T, G, or C for a given base position). While molecular analyses presented unique difficulties (e.g., sequence matching), the abundant characters and discrete character states of molecular sequences finally granted systematists a biological medium that could definitively resolve the relationships of extant taxa.

However, molecular phylogenetics has great limitations – it can only resolve the relationships among extant and recently extinct organisms. Even inclusion of fossil taxa onto genetic cladograms still requires inference based on morphology since fossil organisms are added to the phylogenetic tree according to their skeletal similarity the organisms already arranged by genetic data.

The suggestion of a Homo-Pan clade may have began with the results of

Goodman (1963), but was not cemented until Ruvolo (1997). Since then the clade has been substantially and repeatedly supported by additional molecular analyses evaluating nuclear, mitochondrial, and protein structure (Gagneux, et al., 1999; Satta et al., 2000).

8 Anthropological research then began to focus on resolving the discrepancies between morphological and molecular analyses, no doubt motivated by a desire to resolve the relationships of fossil taxa. New characters and techniques have achieved success to varying degrees.

Strait and Grine (2004) illustrated that the inclusion of fossil taxa into a cladistic analysis of skeletal characters will yield phylogenies that coincide with those constructed from genetic characters. The cladistic analysis of soft tissue characters (e.g., muscles, nerves, vessels) by Gibbs et al. (2000) also yielded a cladogram that matched the genetic phylogeny. Still, some researchers conclude that skeletal morphology is incapable of constructing accurate phylogenetic hypotheses (Collard and Wood, 2000; Young, 2005).

Certainly, the incongruence between morphological and molecular characters has caused a much needed re-evaluation of morphological cladistics, especially the identification and integration of characters (Pilbeam, 2002). Others have utilized a novel technique among paleoanthropologists, called “geometric morphometrics,” to evaluate the skeletal morphology of hominids (e.g. Lockwood et al., 2004; González-José et al., 2008).

Geometric Morphometrics

Geometric morphometric methods differ from those of traditional morphometrics by comparing shapes instead of linear distances. While both methods utilize landmarks, the actual coordinates of the landmarks are important for shape analysis, while traditional morphometricians utilize landmarks as endpoints for distances (MacLeod, 2002). The comparison of corresponding landmark configurations allows for the comparison of shape, a previously difficult, yet intuitively important, biological entity. Shape

9 comparison not only allows for anthropologists to pursue new biomechanical and developmental hypotheses, but also novel phylogenetic hypotheses.

Geometric morphometrics is defined by Bookstein (1991) as a collection of approaches for the multivariate statistical analysis of Cartesian coordinate data, usually limited to landmark point locations. These methods allow one to compare complex shapes in ways virtually impossible to accomplish with linear measurements. Shape comparison is accomplished by a variety of superimposition methods, especially

Generalized Procrustes Analysis (GPA). GPA scales shapes by centroid size by summing the squares of the Euclidean distance between each landmark and the centroid (Zelditch et al., 2004). The centroids of each shape are then superimposed at the origin via translation (Zelditch et al., 2004). Finally, rotating shapes to minimize the sum of squares between corresponding landmarks decreases the differences between landmark configurations (Zelditch et al., 2004). The remaining information after translation, rotation, and scaling is defined as shape (Kendall, 1977). The set of vectors connecting the individual shapes to the consensus shapes, the Procrustes residuals, provide the data from which statistical analyses are then performed (Slice et al., 1998). These analyses provide systematists with the new morphometric data from which to construct phylogenetic hypotheses.

Zelditch et al. (1995) coded partial warp scores as discrete variables for a phylogenetic analysis. While their taxa of choice were fish, not hominids, their technique is an early attempt to combine cladistic analysis with shape analysis. They calculated partial warp scores by projecting the coordinate values of a given shape aligned by GPA onto the principal warps. The principal warps are a set of orthogonal variables that

10 describe the bending energy matrix as a series of spatially-ordered modes of shape variation from a consensus or reference shape (Bookstein, 1991; MacLeod, 1999). While partial warp scores were initially viewed as potential candidates for phylogenetic characters, their absolute dependence on the specific reference shape indicates that they are unreliable characters (Rohlf, 1998; MacLeod, 2010). In addition to the arbitrariness required to code continuous quantitative characters into discrete character states, the dependence of partial warp scores on references shapes contrasts with the vast majority of other multivariate analyses, which instead summarize variation between all shapes in the analysis. Responses to Zelditch et al. (1995) were, in general, unfavorable. Naylor

(1996), Rohlf (1998), and MacLeod (2002) criticized the theoretical and practical basis for treating partial warp scores as biologically meaningful characters.

Lockwood et al. (2004) combined geometric morphometrics and phenetic clustering methods to construct hominid phylogenies. They subjected configurations of twenty-two landmarks of the temporal bone for nine great ape taxa to GPA. Euclidean distances from the consensus shape of the GPA were then subjected to neighbor-joining and least squares clustering methods. While their analysis yielded phylogenies consistent with the consensus genetic phylogenies, these phenetic methods fail to distinguish between symplesiomorphies and synapomorphies. Lockwood et al. (2004) cite the inability to transform the quantitative, continuous Euclidean distances to discrete characters necessary for cladistic analysis as their justification for using phenetic clustering methods.

González-José et al. (2008) accounted for the criticisms levied against Zelditch et al. (1995) by transforming their GPA-aligned shapes into characters for a cladistic (not

11 phenetic) analysis via Principal Component Analysis (PCA). First, González-José et al.

(2008) separated the landmark configurations for each specimen into four hypothesized functional modules by creating subsets of landmarks, similar to an approach introduced by MacLeod (2002). After subjecting each group of shapes to a GPA, the Procrustes- aligned coordinates were subjected to a PCA (González-José et al., 2008). The principal components accounting for 75% of the variance were then used as individual characters.

Character states for each specimen were created by calculating the principal component scores for each shape from the PCA matrix. A total of 18 characters were created using this method. Character states, albeit continuous and quantitative in nature, were treated as such, by using the method of Goloboff et al. (2006) to treat the character states additively.

The analysis primarily consisted of fossil taxa, but Homo sapiens, Pan troglodytes, and

Gorilla gorilla were included as well. The resulting phylogeny was uncontroversial, agreeing with pre-existing hypotheses for fossil hominid evolutionary relationships.

Adams et al. (2011) criticized this method by illustrating the arbitrariness of subdividing crania into modular complexes and the contingency of PCA score dependent characters on the taxa analyzed (despite their stability compared to partial warp score characters).

12

Hypotheses

I tested two hypotheses for this study. These tests compared the topologies of hominid phylogenies built from different types of biological characters. To test the hypothesis that primate geometric morphometric cranial shape variables contain phylogenetic accuracy, I compared a cladogram constructed from such characters to the respective genetic phylogeny for those taxa. I did this for both male and female adult specimens of seven primate taxa. I tested the second hypothesis, that juvenile skeletal morphology contains phylogenetic accuracy, by comparing cladograms built from juvenile geometric morphometric shape characters to those previously built from male and female adult cranial shape variables to the genetic phylogeny. In total, I constructed three cladograms. Pairwise comparisons of each cladogram to one another and to the genetic phylogeny are outlined in Table 1. I assessed differences between the cladograms statistically using topological comparison software.

Table 1. Pairwise comparisons of the four cladograms.

Genetic  Adult male Adult male  Adult female

Genetic  Adult female Adult male  Juvenile

Genetic  Juvenile Adult female  Juvenile

13

Materials and Methods

Specimens

I collected landmark data from 80 specimens representing seven genera (Gorilla,

Homo, Hylobates, Macaca, Nomascus, Pan, and Pongo) and two ontogenetic levels

(adult and juvenile) from the Cleveland Museum of Natural History (CMNH), Florida

Museum of Natural History (FLMNH), and Smithsonian National Museum of Natural

History (NMNH) (see Appendix A). I used the dental eruption schedules presented by

Smith et al. (1994) and primate life history data of Harvey and Clutton-Brock (1985) to select sexually mature adult specimens. Specimens identified as adult exhibited complete permanent dentition (Smith et al., 1994). I selected three male and three female specimens for each species, when available.

Juvenile mammals do not exhibit the magnified sexual differences of their adult conspecifics since these differences occur later in life (for humans: Cox & Mays, 2000;

Baker et al., 2005). Therefore, sex discrimination was not a factor for juvenile specimen selection. I selected the youngest juvenile specimens available to limit environmentally induced and sexually influenced variation. The youngest specimens for Gorilla, Homo,

Macaca, Pan, and Pongo were identified by the absence of M1 and presence of primary dentition. The youngest specimens of Hylobates and Nomascus, available were specimens with M1 erupted and near complete deciduous teeth (Uchikoshi &

Matsuuzawa, 2007). I digitized all specimens identified as juvenile available in each 14

collection I visited. Complete maxillary dental eruption data for each specimen is provided in Appendix A. The number of specimens by genus and demographic class are listed in Table 2.

Table 2. Specimens by genus and demographic class.

Genus Demographic class Specimens

Gorilla Adult male 4

Gorilla Adult female 4

Gorilla Juvenile 5

Homo Adult male 3

Homo Adult female 3

Homo Juvenile 4

Hylobates Adult male 2

Hylobates Adult female 2

Hylobates Juvenile 6

Macaca Adult male 1

Macaca Adult female 3

Macaca Juvenile 5

Nomascus Adult male 2

Nomascus Adult female 2

Nomascus Juvenile 3

Pan Adult male 3

15 Genus Demographic class Specimens

Pan Adult female 3

Pan Juvenile 9

Pongo Adult male 7

Pongo Adult female 6

Pongo Juvenile 3

Total 80

Landmarks

I collected sixty coordinates of sixty ectocranial landmarks with a MicroScribe

MX digitizer (Immersion Corporation, San Jose). Of the 60 landmarks, 16 were Type I and 44 were Type II (Table 3; Figures 1-3). Type I landmarks are defined as identifiable using only local structures, such as the junction of tissues, while Type II landmarks also require geometric definitions, such as the “edge,” of foramina (O‟Higgins, 2000). I recorded ten landmarks along the sagittal plane (e.g., nasale, basion, opisthion), while the remaining 50 were actually 25 sets of paired landmarks. I selected landmarks that best defined the boundaries of the individual bones of the skull. The bones I selected to outline were the frontal, maxilla, nasal, occipital, palatine, parietal, temporal, and zygoma. The landmark subsets used to outline these bones are provided in Appendix B.

All specimens were digitized twice, with eight specimens digitized four times, to assess error.

16 Table 3. Landmark descriptions. Letters in the “Plane” column represent superior (S), inferior (I), lateral (L), anterior (A), and posterior (P). Letters in the “Bones” column represent which bones or tissues the described landmark outlines or falls within. These bones are parietal (P), frontal (F), temporal (T), occipital (O), nasal (N), maxilla (M), sphenoid (S), zygoma (Z), palatine (Pa), lacrimal (L), incisor (I), canine (C), and molar

(Mo). Landmarks with right and left locations have two character numbers.

Landmark Type Plane Bones Description

1 I S FP (Bregma) Fronto-parietal suture in the midline

2 I P/S OP (Lambda) Parieto-occipital suture in the midline

3, 4 I L OPT (Asterion) Parieto-occipito-temporal junction

(Pterion) Lateral fronto-parieto-sphenoid/temporal 5, 6 I L FP junction

(Pterion) Lateral parieto-temporo-sphenoid/frontal 7, 8 I L PT junction

9, 10 I L FSZ (Pterion) Lateral fronto-spheno-zygomatic junction

Lateral temporo-zygomatic junction at the superior 11, 12 II L TZ edge of the zygomatic arch

Lateral temporo-zygomatic junction at the inferior 13, 14 II L TZ edge of the zygomatic arch

(Nasale) Tip of nasal bones in the midline (Landmark 15 II A N 16 of Collard and O'Higgins, 2001)

16 II A M Maxillary suture at the inferior margin of the nasal

17 Landmark Type Plane Bones Description

aperture in the midline (Landmark 17 of Collard and

O'Higgins, 2001)

Anterior tip of maxillo-nasal junction at the edge of 17, 18 II A MN the nasal aperture

Nasofrontal suture in the midline (Landmark 15 of 19 I A FN Collard and O'Higgins, 2001)

20, 21 I A FMN Fronto-maxillo-nasal junction

22, 23 I A FLM (Dacryon) Fronto-maxillo-lacrimal junction

Zygomatico-frontal suture at the lateral aspect of the

24, 25 II A FZ orbital aperture (Landmarks 4 and 22 of Collard and

O'Higgins, 2001)

Zygomatico-maxillary suture at inferior orbital

26, 27 II A MZ margin (Landmarks 6 and 24 of Collard and

O'Higgins, 2001)

Zygomatico-maxillary suture at root of zygomatic

28, 29 II A MZ arch (Landmarks 9 and 27 of Collard and O'Higgins,

2001)

30 I I MPa Maxillo-palatine suture in the midline

Middle point at the anterior edge of the lacrimal 31, 32 II A LM foramen (Landmark 34 of Marcus et al., 2000)

Middle of posterior external edge of infraorbital 33, 34 II A M foramen (Landmark 22 of Marcus et al., 2000)

18 Landmark Type Plane Bones Description

Middle of posterior edge of incisive foramen (Marcus 35 II I M et al., 2000)

36, 37 II I Pa Middle of posterior edge of greater palatine foramen

38, 39 II I T Middle of posterior edge of carotid canal

40, 41 II I OT Middle of posterior edge of jugular foramen

42, 43 II I ST Middle of posterior edge of foramen ovale

Middle of superior edge of auditory canal (Landmark 44, 45 II I/L T 27 of Marcus et al., 2000)

(Basion) Anterior edge of foramen magnum 46 II I O (Landmark 16 of Marcus et al., 2000)

(Opisthion) Posterior edge of foramen magnum 47 II I O (Landmark 17 of Marcus et al., 2000)

48, 49 II A F Middle of superior edge of optic foramen

(Prosthion) Maxillary suture at alveolar margin 50 II A IM (Landmark 18 of Collard and O'Higgins, 2001)

Alveolar margin of the maxilla at the posterior aspect 51, 52 II A IM of the last incisor

Alveolar margin of the maxilla at the medial aspect of 53, 54 II A/L CM the canine

Alveolar margin of the maxilla at the posterior aspect 55, 56 II A/L CM of the canine

19 Landmark Type Plane Bones Description

Alveolar margin of the maxilla at the anterior aspect 57, 58 II L MoM of the first cheek tooth (premolar/molar)

Alveolar margin of the maxilla at the posterior aspect 59, 60 II L MoM of the last cheek tooth (premolar/molar)

• •

•

Figure 1. Lateral view of cranial landmark locations. Skull image from eSkeletons (n.d.). 20

Figure 2. Anterior view of cranial landmark locations. Skull image from eSkeletons

(n.d.).

21

Figure 3. Inferior view of cranial landmark locations. Skull image from eSkeletons (n.d.).

22 Error Testing

Geometric morphometric methods analyze shape. Therefore I assessed intra- observer error by performing an analysis in MorphoJ 2.0 (Klingenberg, 2011), called a

Procrustes Analysis of Variance (ANOVA; Klingenberg & McIntyre 1998; Klingenberg et al. 2002). This method allows one to compare the magnitudes of different shape variation sources (Klingenberg & McIntyre 1998; Klingenberg et al. 2002). I compared three sources of variation from the sample of eight specimens that was digitized four times each. The sources of variation were trial (e.g., the first, second, or third landmark collection for a given specimen), specimen (e.g., “Specimen No. 396934”), and demographic class (e.g., “male adult Gorilla”). Presented below are the results of the

Procrustes ANOVA (Table 4). Trial was a negligible source of variation, but was also the smallest source of variation. Sides of each specimen and the interaction of individual with side are automatically included in the Procrustes ANOVA analysis, as the original use of this test was to assess asymmetry (Klingenberg & McIntyre 1998; Klingenberg et al.

2002). This suggests shape digitization was essentially unaffected by measurement error.

Demographic class and specimen are each significant sources of variation, as expected.

23 Table 4. Procrustes ANOVA results.

Sum of Mean Degrees of Effect F statistic p-value Squares Squares freedom

Class 2.88E-01 6.33E-04 455 2.14 <.0001

Specimen 2.69E-02 2.96E-04 91 78.34 <.0001

Side 1.71E-01 2.08E-03 82 0.05 <.0001

Individual * Side 1.86E-03 3.77E-06 492 - 1.0000

Trial 2.65E-01 7.30E-05 3633 - -

Character Identification

I coded geometric the morphometric shape variables as quantitative and continuous characters. As suggested by MacLeod (2002) and executed by González-José et al. (2008), I separated the landmark configuration for a given specimen into subsets

(Appendix B). Unlike González-José et al. (2008), I divided the configuration into shapes that mirrored the individual bones of the skull, instead of modularized functional complexes.

To code a cranial bone shape as a quantitative, continuous cladistic character, I first averaged all landmark configurations by specimen, since landmark coordinates were collected for each specimen at least twice. I then divided each averaged specimen shape into landmark subsets that mirrored individual cranial bones. Incomplete landmark configurations were accommodated by using only the right side coordinates for each specimen, and reflecting those with only left side coordinates (Mardia et al., 2000).

24 Therefore, for all cranial bone shapes, only one side was used (except for the palatine shape, since it was composed of only three coordinates).

I organized the averaged cranial bone shapes of each specimen by cranial bone, taxon, and demographic class (male adult, female adult, or juvenile), for a total of 168 groups (three demographic classes, seven taxa per demographic class, eight cranial bone shapes per taxon). I then subjected each group to a Generalized Procrustes Analysis

(GPA) to create 168 consensus shapes. I grouped these consensus shapes according to the bone modeled and demographic class, leaving 24 groups (three demographic classes, eight cranial bone shapes per demographic class). Each group now included one cranial bone shape per taxon, for a total of seven different shapes for a given bone in each group.

For example, one group included the consensus shapes of the palatine bone for the adult male of each taxon. I subjected the shapes in each of these groups to another GPA, followed by a Principal Component Analysis (PCA) of their covariance matrix. Unlike

González-José et al. (2008), I recognized all principal components that accounted for more than 5% of the variance for each shape character (Zelditch et al., 2004). The absolute values of principal component scores for individual shapes were then used as character states for each principal component character, following González-José et al.

(2008) and Macleod (2010). This method established 27 characters for the adult female and juvenile character matrices and 26 characters for the adult male set. The number of characters differs between classes because the amount of variance explained by each principal component was unique to the PCA for each class. The character matrices are located in Appendix C. I conducted the shape analysis (GPA and PCA) in MorphoJ 2.0

25 (Klingenberg, 2011). The GPA of MorphoJ, in addition to scaling, rotating, and translating coordinates to minimize differences, also reflects shapes if necessary.

Cladistic Analysis

I treated these principal component characters as additive character states for the cladistic analysis to avoid the arbitrariness associated with gap-coding quantitative and continuous characters (Goloboff et al., 2006). A feature of Tree analysis using New

Technology 1.1 (TNT) (Goloboff et al., 2000), this coding method allows one to analyze a character matrix with character state values between 0 and 65, up to three decimal places (Goloboff et al., 2006). I submitted the three character matrices as unweighted and continuous to the cladistic analysis via the “Implicit enumeration” option, to find the shortest tree, with Macaca mulatta as the outgroup.

Cladogram Comparison Tests

I compared trees using the „Split‟ method in the computer program TOPD/FMTS

(Puigbo et al., 2007). TOPD/FMTS is new software for comparing phylogenetic trees that combines the TOPD (TOPological Distance) program, which compares two trees with the same taxa or two pruned trees (pruned trees are trees with shared taxa that have been

“pruned” to contain only the taxa that they share), and the FMTS (From Multiple To

Single) program, which converts multi-gene family tees to single-gene family trees

(Puigbo et al., 2007).

Originally described by Robinson and Foulds (1979; 1981) and reviewed by

Penny and Hendy (1985), the „Split‟ method, or “partition metric,” compares two trees

26 first by unrooting the trees and then by converting one tree into the other by removing an

“edge,” joining adjacent vertices, and then reinserting “edges” (Figure 4; Penny &

Hendy, 1985). An edge is a branch between two internal nodes. Summing the number of deletions required to convert one tree into the other scores the difference between the two trees (Penny & Hendy, 1985). TOPD/FMTS can then implement two randomization methods to evaluate whether the similarity between the two trees is better than random

(Puigbo et al., 2007). To generate random trees and the differences between them,

TOPD/FMTS rearranges the taxa on the preserved tree topology (guided), or randomly changes the tree topology but preserves the taxa (random; Puigbo et al., 2007). A critical point is created from this randomization analysis to determine if the difference between the two originally compared trees is less than that expected when comparing two randomly generated binary trees (Puigbo et al., 2007).

27 I " .

a cd • .> v (, . , .

:> v <: , 'I. . }-l4,

• Figure 4. Conversion of one tree to another via the “splits” or “partition metric.”

Reproduced from Penny and Hendy (1985).

28 I suspected that the distribution of pairwise tree differences from the randomization analysis was not normal based on the results of Steel and Penny (1993), which meant that I was unable to calculate a critical point from TOPD/FMTS with a standard z-score calculation. I determined if the distribution was normal by constructing my own frequency distributions from 133 randomly constructed trees and 131 random comparisons. I followed the randomization procedures of TOPD/FMTS by first rearranging six taxa on the preserved genetic tree topology (guided) and then by preserving the six taxa order but rearranging the tree topology (Random). I generated randomized sequences using the program available at Random.org (Haahr, 2011).

I tested the frequency distributions for normality using the D‟Agostino omnibus test in StatPlus v2009 (AnalystSoft Inc.). The D‟Agostino omnibus test quantifies the skewness and kurtosis of the sample distribution and calculates how far each of these values differs from the value expected with a Gaussian distribution (GraphPad Software,

Inc.). It then computes a single p-value from the sum of the squares of these discrepancies (GraphPad Software, Inc.). I did not use the Shapiro-Wilk W and

Anderson-Darling tests for normality because both are affected by ties in the data, and the tested distributions have many identical values (GraphPad Software, Inc.). The

D‟Agostino omnibus test statistics for the distributions indicate that the distributions are not normal (Table 5). Thus, I calculated my own critical values from the frequency distributions to determine the significance of the results. A difference of two or less between a pair of topologies has a cumulative probability of occurring less than 0.0382 and 0.0305 for the guided and random randomizations, respectively (Tables 6-7).

29 Table 5. D‟Agostino omnibus test statistics.

Guided Random Test Type Test Statistics p-value Test Statistics p-value

D'Agostino Skewness 5.91 3.45E-09 5.95 2.71E-09

D'Agostino Kurtosis 2.79 5.26E-03 4.03 5.46E-05

D'Agostino Omnibus 42.70 5.35E-10 51.66 6.05E-12

Table 6. Frequency distribution for guided randomization analysis.

Difference Count Cumulative Count Percent Cumulative Percent

- - - - -

2 5 5 3.82% 3.82%

4 29 34 22.14% 25.95%

6 97 131 74.05% 100.00%

Table 7. Frequency distribution for random randomization analysis.

Difference Count Cumulative Count Percent Cumulative Percent

0 2 2 1.53% 1.53%

2 2 4 1.53% 3.05%

4 43 47 32.82% 35.88%

6 84 131 64.12% 100.00%

30

Results

Parsimony Analysis

The cladograms of the parsimony analyses do not reflect the topology of the genetic phylogeny, illustrated in Figure 5. These results suggest that geometric morphometric cranial shape variables are unable to recover accurate hominoid evolutionary relationships, at least those dependent on the landmarks used in this study

(Figures 6-8). Furthermore, these cladograms each appear to support different hypotheses for hominoid phylogeny.

,------Macaca ,---- Nomascus ,------1 L-- Hylobates ,----- Pongo ,---- Gorilla ,---- Pan L...---..1 L-- Homo

Figure 5. Hominoid genetic phylogeny.

31

,------Macaca .----- Pongo .--- Homo "------l l-- Gorilla .----- Hylobates .--- Pan L-...j L-- Nomascus

Figure 6. Maximum parsimony cladogram of adult male cranial shapes.

,------Macaca .------Pongo

,------Hom0 ,------Gori II a ,----- Nomascus .--- Hylobates L-...j L-- Pan

Figure 7. Maximum parsimony cladogram of adult female cranial shapes.

32 ,------Macaca ,------Homo .------Gori lIa ,------Pon go ,------Pan ,------Nomascus '----I L-- Hylobates

Figure 8. Maximum parsimony cladogram of juvenile cranial shapes.

33 Topological Distance Tests

The results of the topological comparisons in TOPD/FMTS confirmed the initial assessment of cladogram similarity. None of the cladogram topologies constructed from the geometric morphometric shape variables were significantly similar to the topology of the genetic phylogeny (Table 8). The adult male cladogram topology was significantly similar (p = 0.0382 or p = 0.0305) to that of the juvenile cladogram topology. However, this result is potentially due to the problem of multiple comparisons. If I applied the

Bonferroni correction (p-value = 0.05/6 = 0.0083), even this result is not significant. The adult female cladogram topology was not significantly similar to the juvenile or adult male cladogram topologies.

Table 8. Topological distances of pairwise cladogram comparisons.

Comparison Difference Comparison Difference

Genetic  Adult male 6 Adult male  Juvenile 2*

Genetic  Adult female 6 Adult female  Juvenile 4

Genetic  Juvenile 4 Adult male  Adult female 4

*indicates p < 0.05

34

Discussion

The results of the topological comparisons indicated that neither adult male, female, nor juvenile hominid geometric morphometric cranial shapes are capable of constructing accurate phylogenetic relationships. The cladogram topologies constructed from each group of characters were no more similar to the topology of the genetic phylogeny for this group of taxa than were randomly generated topologies. Furthermore, the three cladograms constructed in this study were not even similar to one another, with the exception of the male adult and juvenile tree topologies, which was possibly due to the problem of multiple comparisons. Thus, not only do cranial shapes not produce the same relationships among the hominids as genetic data, but these shapes do not even produce the same relationships at different ontogenetic stages or for different sexes.

Possible explanations for the topological dissimilarity between the genetic phylogeny and these geometric morphometric shape variable phylogenies are plentiful.

Methodological factors likely contributed to this discrepancy. For example, parsimony analysis, while credited for distinguishing between synapomorphy and symplesiomorphy, is susceptible to the number of taxa and characters, in addition to the selection, coding, and polarization of characters (Kitching et al., 1998).

Perhaps increasing the number of taxa and characters without altering the other methods would result in tree topologies that coincide with the genetic phylogeny.

Including more extant (e.g., Symphalangus syndactylus, Papio papio) and fossil (e.g.,

35

Homo neanderthalensis, Australopithecus africanus) taxa may prove instrumental (Strait

& Grine, 2004). Specific level, instead of generic level, terminal taxa may also increase the accuracy of this method. Since gibbon juvenile specimens were slightly older than the other specimens, obtaining all specimens at the same dental eruption stage, or even selecting specimens based on different ageing methods (e.g. absolute age) may lead to different results. Increasing the number and type of skeletal elements (e.g., mandibles, teeth, postcrania) to digitize would provide more shape variables to code as characters.

The phylogenetic signal of these morphologies may be as important for resolving phylogenetic relationships as those of the crania.

Alternatively, the shape variables selected as characters might not hold phylogenetic value. Selecting landmarks that captured the shapes of modularized traits,

(e.g., González-José et al., 2008) instead of cranial bones would probably lead to different results. Cheverud (1995) illustrated that functional and developmental integration leads to the co-inheritance of character complexes, which are constrained to evolve dependently. Parsimony analysis assumes character independence, but this assumption is clearly not applicable to biological morphology. Modular shape variables may hold phylogenetic value, while discrete biological structures, like individual bones, do not. However, modular shape variables are hypotheses themselves. Adams et al.

(2011) revealed the arbitrariness of such modular units by illustrating that even randomly-selected hominid landmark subsets yield tree topologies similar to those of

González-José et al. (2008). At least individual cranial bones are actual biological entities.

36 Adding more landmarks to better model bone shapes would certainly impact cladogram topologies. However, landmarks that correspond to biological homology,

(Type I) are rare, while those that correspond to geometric homology (Type II) are more common (MacLeod, 2002). This is problematic for phylogenetic shape comparison since

Type II landmarks are not biologically homologous (MacLeod, 2002). Gunz et al. (2005) introduced “sliding semilandmarks,” multiple landmarks that outline a form but are allowed to slide along the edge of the shape to minimize the dissimilarity between configurations. Semilandmarks reduce the importance of Type II landmarks, and are anchored by Type I landmarks. This method allows a researcher to digitize curves, which may confound a shape analysis. Unless landmarks register the actual curvature of the structure, the shape of it is very crudely modeled, occasionally ignoring even concavity and convexity. For example, adult male Gorilla and Pongo have crests along their lambdoidal and sagittal sutures. I collected landmarks along these sutures, for bregma, lambda, and asterion, as was done for all other species. Failure to adequately digitize the actual contours of these cranial crests is likely responsible for a Homo-Gorilla-Pongo clade on the adult male cladogram (Figure 6).

While Adams et al. (2011) argue not to code characters from principal component scores, a different method for coding shape variables than the one used in this thesis might resist their criticisms. MacLeod (2002) proposes graphing the first two principle component scores for each shape to code for character states. Coding character states in this manner, albeit as discrete instead of continuous states, may yield different cladogram topologies. Naylor (1996), Rohlf, (1998), and MacLeod (2002) criticized the use of partial warp scores as characters by Zelditch et al. (1995), despite the cladogram resulting

37 from those characters coinciding with the genetic phylogeny for the group of taxa analyzed.

Lastly, a parsimony analysis of skeletal characters may not yield phylogenies that reflect genetic relationships. Springer et al. (2007) showed that a parsimony analysis of skeletal characters for 44 mammalian taxa was incapable of returning a phylogeny that coincided with the genetic phylogeny. DNA phylogenies continue to surprise paleontologists with unexpected evolutionary relationships, especially those that directly conflict with well-supported, morphologically based phylogenies (e.g. Ruvolo, 1997;

Springer et al., 1997; Harris & Disotell, 1998; Gaubert & Veron, 2003; Koepfli et al.,

2007). These results appear to indicate that skeletal characteristics may not indicate evolutionary relationships.

38

Conclusions

The aim of this research was to evaluate the phylogenetic utility of adult male, female, and juvenile hominoid cranial bone shapes. While the evolutionary hypotheses of genetic phylogenies often contradict those constructed from skeletal characteristics, the continued evaluation of different types of skeletal characters and methods for quantifying skeletal characters is useful for paleoanthropologists, since genetic material does not fossilize. In this case I used geometric morphometric techniques to capture and compare the shapes of cranial bones for seven primate genera. I coded these shapes as quantitative and continuous characters, which were then subjected to a parsimony analysis. The comparison of the resulting cladogram topologies to one another and to the topology of the genetic phylogeny suggests that phylogenies built from cranial bones not only differ from the genetic phylogeny, but differ according to the sex and ontogenetic stage of the specimens evaluated. Regardless of whether or not the discovery of methodologies to produce phylogenies that coincide with genetic phylogenies is possible, this study will aid paleoanthropologists by thinning the number of possible solutions to this problem.

Fortunately, the combination of geometric morphometric techniques and cladistic methodology is still novel. Perhaps future geometric morphometric techniques will provide solutions to these current problems and elucidate the phylogenetic utility of skeletal anatomy.

.

39

Appendices

40

Appendix A: Specimens

Specimens reside at the Cleveland Museum of Natural History (CMNH), Florida

Museum of Natural History (FLMNH), and Smithsonian Museum of Natural History

(NMNH). Eruption sequence indicates the maxillary dental eruption sequence.

Specimen No. Species Sex Eruption Sequence Location

396934 Gorilla beringei ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

545029 Gorilla beringei ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

239884 Gorilla beringei ♂ di1 di2 dc1 dp3 dp4 NMNH

176209 Gorilla gorilla ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

176216 Gorilla gorilla ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

176217 Gorilla gorilla ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

252577 Gorilla gorilla ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

252579 Gorilla gorilla ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

582726 Gorilla gorilla ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

1799 Gorilla gorilla - di1 di2 dc1 dp3 dp4 CMNH

1858 Gorilla gorilla - di1 di2 dc1 dp3 dp4 CMNH

2824 Gorilla gorilla ♀ di1 di2 dc1 dp3 dp4 CMNH

176214 Gorilla gorilla ♂ di1 di2 dc1 dp3 dp4 NMNH

1693 Homo sapiens ♂ I1 I2 C1 P3 P4 M1 M2 M3 CMNH

1703 Homo sapiens ♂ I1 I2 C1 P3 P4 M1 M2 M3 CMNH

1774 Homo sapiens ♂ I1 I2 C1 P3 P4 M1 M2 M3 CMNH

2165 Homo sapiens ♀ I1 I2 C1 P3 P4 M1 M2 M3 CMNH

2494 Homo sapiens ♀ I1 I2 C1 P3 P4 M1 M2 M3 CMNH

2939 Homo sapiens ♀ I1 I2 C1 P3 P4 M1 M2 M3 CMNH

1074 Homo sapiens ♀ di1 di2 dc1 dp3 dp4 CMNH

1115 Homo sapiens ♀ di1 di2 dc1 dp3 dp4 CMNH

41 Specimen No. Species Sex Eruption Sequence Location

1768 Homo sapiens ♂ di1 di2 dc1 dp3 dp4 CMNH

2141 Homo sapiens ♀ di1 di2 dc1 dp3 dp4 CMNH

1074 Hylobates agilis - di1 di2 dc1 dp3 dp4 M1 CMNH

49656 Hylobates klossii ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

49657 Hylobates klossii ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

121680 Hylobates klossii ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

122176 Hylobates klossii ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

252308 Hylobates klossii ♂ di1 di2 dc1 dp3 dp4 M1 NMNH

252311 Hylobates klossii ♂ I1 di2 dc1 dp3 dp4 M1 NMNH

9944 Hylobates lar ♀ di1 di2 dc1 dp3 dp4 M1 FLMNH

307761 Hylobates lar carpenteri ♂ I1 di2 dc1 dp3 dp4 M1 NMNH

196780 Hylobates muelleri funereus ♂ I1 di2 dc1 dp3 dp4 M1 NMNH

537233 Macaca mulatta ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

537260 Macaca mulatta ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

537274 Macaca mulatta ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

537267 Macaca mulatta ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

419 Macaca mulatta - di1 di2 dc1 dp3 dp4 CMNH

421 Macaca mulatta - di1 di2 dc1 dp3 dp4 CMNH

1498 Macaca mulatta - di1 di2 dc1 dp3 dp4 CMNH

1500 Macaca mulatta - di1 di2 dc1 dp3 dp4 CMNH

5994 Macaca mulatta - di1 di2 dc1 dp3 dp4 CMNH

320789 Nomascus concolor ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

257995 Nomascus gabriellae ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

257996 Nomascus gabriellae ♂ di1 di2 dc1 dp3 dp4 M1 NMNH

240490 Nomascus leucogenys ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

240491 Nomascus leucogenys ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

42 Specimen No. Species Sex Eruption Sequence Location

240493 Nomascus leucogenys ♂ di1 di2 dc1 dp3 dp4 M1 NMNH

296921 Nomascus leucogenys ♀ I1 di2 dc1 dp3 dp4 M1 NMNH

176228 Pan troglodytes ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

236971 Pan troglodytes ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

282763 Pan troglodytes ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

395820 Pan troglodytes ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

481803 Pan troglodytes ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

481804 Pan troglodytes ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

410 Pan troglodytes - di1 di2 dc1 dp3 dp4 CMNH

753 Pan troglodytes - di1 di2 dc1 dp3 dp4 CMNH

793 Pan troglodytes - di1 di2 dc1 dp3 dp4 CMNH

1395 Pan troglodytes - di1 di2 dc1 dp3 dp4 CMNH

1848 Pan troglodytes - di1 di2 dc1 dp3 dp4 CMNH

3399 Pan troglodytes - di1 di2 dc1 dp3 dp4 CMNH

31228 Pan troglodytes - di1 di2 dc1 dp3 dp4 FLMNH

176237 Pan troglodytes - di1 di2 dc1 dp3 dp4 NMNH

143966 Pan troglodytes - di1 di2 dc1 dp3 dp4 NMNH

267325 Pongo abelii ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

270807 Pongo abelii ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

143590 Pongo abelii ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

143593 Pongo abelii ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

143594 Pongo abelii ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

143596 Pongo abelii ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

143600 Pongo abelii ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

142197 Pongo pygmaeus ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

153806 Pongo pygmaeus ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

43 Specimen No. Species Sex Eruption Sequence Location

153823 Pongo pygmaeus ♂ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

153828 Pongo pygmaeus ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

153830 Pongo pygmaeus ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

197664 Pongo pygmaeus ♀ I1 I2 C1 P3 P4 M1 M2 M3 NMNH

292562 Pongo pygmaeus ♀ di1 di2 dc1 dp3 dp4 NMNH

317197 Pongo pygmaeus ♂ di1 di2 dc1 dp3 dp4 NMNH

396920 Pongo pygmaeus ♀ di1 di2 dc1 dp3 dp4 NMNH

44 Appendix B: Cranial Bone Landmark Subsets

Landmark subsets. Letters of the “Plane” column represent superior (S), inferior (I), lateral (L), anterior (A), and posterior (P). Letters of the “Bones” column represent which bones or tissues the described landmark outlines or falls within. These bones are parietal

(P), frontal (F), temporal (T), occipital (O), nasal (N), maxilla (M), sphenoid (S), zygoma

(Z), palatine (Pa), lacrimal (L), incisor (I), canine (C), and molar (Mo). Landmarks with right and left locations have two numbers.

Frontal landmarks.

Landmark Plane Bones Description

1 S FP (Bregma) Fronto-parietal suture in the midline

5, 6 L FP (Pterion) Lateral fronto-parieto-sphenoid/temporal junction

9, 10 L FSZ (Pterion) Lateral fronto-spheno-zygomatic junction

Nasofrontal suture in the midline (Landmark 15 of Collard and 19 A FN O'Higgins, 2001)

20, 21 A FMN Fronto-maxillo-nasal junction

22, 23 A FLM (Dacryon) Fronto-maxillo-lacrimal junction

Zygomatico-frontal suture at the lateral aspect of the orbital

24, 25 A FZ aperture (Landmarks 4 and 22 of Collard and O'Higgins,

2001)

48, 49 A F Middle of superior edge of optic foramen

45 Maxilla landmarks.

Landmark Plane Bones Description

Maxillary stuture at the inferior margin of the nasal aperture in 16 A M the midline (Landmark 17 of Collard and O'Higgins, 2001)

Anterior tip of maxillo-nasal junction at the edge of the nasal 17, 18 A MN aperture.

20, 21 A FMN Fronto-maxillo-nasal junction

22, 23 A FLM (Dacryon) Fronto-maxillo-lacrimal junction

Zygomatico-maxillary suture at inferior orbital margin 26, 27 A MZ (Landmarks 6 and 24 of Collard and O'Higgins, 2001)

Zygomatico-maxillary suture at root of zygomatic arch 28, 29 A MZ (Landmarks 9 and 27 of Collard and O'Higgins, 2001)

30 I MPa Maxillo-palatine suture in the midline

Middle point at the anterior edge of the lacrimal foramen 31, 32 A LM (Landmark 34 of Marcus et al., 2000)

Middle of posterior external edge of infraorbital foramen 33, 34 A M (Landmark 22 of Marcus et al., 2000)

Middle of posterior edge of incisive foramen (Marcus et al., 35 I M 2000)

(Prosthion) Maxillary suture at alveolar margin (Landmark 18 50 A IM of Collard and O'Higgins, 2001)

Alveolar margin of the maxilla at the posterior aspect of the 51, 52 A IM last incisor

46 Landmark Plane Bones Description

Alveolar margin of the maxilla at the medial aspect of the 53, 54 A/L CM canine

Alveolar margin of the maxilla at the posterior aspect of the 55, 56 A/L CM canine

Alveolar margin of the maxilla at the anterior aspect of the 57, 58 L MoM first cheek tooth (premolar/molar)

Alveolar margin of the maxilla at the posterior aspect of the 59, 60 L MoM last cheek tooth (premolar/molar)

Nasal landmarks.

Landmark Plane Bones Description

(Nasale) Tip of nasal bones in the midline (Landmark 16 of 15 A N Collard and O'Higgins, 2001)

Anterior tip of maxillo-nasal junction at the edge of the nasal 17, 18 A MN aperture.

Nasofrontal suture in the midline (Landmark 15 of Collard and 19 A FN O'Higgins, 2001)

20, 21 A FMN Fronto-maxillo-nasal junction

47 Occipital landmarks.

Landmark Plane Bones Description

2 P/S OP (Lambda) Parieto-occipital suture in the midline

3, 4 L OPT (Asterion) Parieto-occipito-temporal junction

40, 41 I OT Middle of posterior edge of jugular foramen

(Basion) Anterior edge of foramen magnum (Landmark 16 of 46 I O Marcus et al., 2000)

(Opisthion) Posterior edge of foramen magnum (Landmark 17 47 I O of Marcus et al., 2000)

Palatine landmarks.

Landmark Plane Bones Description

30 I MPa Maxillo-palatine suture in the midline

36, 37 I Pa Middle of posterior edge of greater palatine foramen

Parietal landmarks.

Landmark Plane Bones Description

1 S FP (Bregma) Fronto-parietal suture in the midline

2 P/S OP (Lambda) Parieto-occipital suture in the midline

3, 4 L OPT (Asterion) Parieto-occipito-temporal junction

5, 6 L FP (Pterion) Lateral fronto-parieto-sphenoid/temporal junction

48 Temporal landmarks.

Landmark Plane Bones Description

3, 4 L OPT (Asterion) Parieto-occipito-temporal junction

7, 8 L PT (Pterion) Lateral parieto-temporo-sphenoid/frontal junction

Lateral temporo-zygomatic junction at the superior edge of the 11, 12 L TZ zygomatic arch

Lateral temporo-zygomatic junction at the inferior edge of the 13, 14 L TZ zygomatic arch

38, 39 I T Middle of posterior edge of carotid canal

40, 41 I OT Middle of posterior edge of jugular foramen

42, 43 I ST Middle of posterior edge of foramen ovale

Middle of superior edge of auditory canal (Landmark 27 of 44, 45 I/L T Marcus et al., 2000)

49 Zygoma landmarks.

Landmark Plane Bones Description

9, 10 L FSZ (Pterion) Lateral fronto-spheno-zygomatic junction

Lateral temporo-zygomatic junction at the superior edge of the 11, 12 L TZ zygomatic arch

Lateral temporo-zygomatic junction at the inferior edge of the 13, 14 L TZ zygomatic arch

Zygomatico-frontal suture at the lateral aspect of the orbital 24, 25 A FZ aperture (Landmarks 4 and 22 of Collard and O'Higgins, 2001)

Zygomatico-maxillary suture at inferior orbital margin 26, 27 A MZ (Landmarks 6 and 24 of Collard and O'Higgins, 2001)

Zygomatico-maxillary suture at root of zygomatic arch 28, 29 A MZ (Landmarks 9 and 27 of Collard and O'Higgins, 2001)

50 Appendix C: Data Matrices

Adult male character matrix.

Bone Frontal Maxilla Nasal ______Character 1 2 3 4 5 6 7 8 9

Principal Component PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC1 PC2

Gorilla 0.133 0.038 0.076 0.011 0.116 0.038 0.020 0.129 0.004

Homo 0.097 0.142 0.036 0.016 0.057 0.148 0.063 0.187 0.025

Hylobates 0.108 0.041 0.042 0.022 0.193 0.007 0.020 0.141 0.021

Macaca 0.078 0.081 0.076 0.044 0.117 0.072 0.100 0.123 0.009

Nomascus 0.014 0.070 0.018 0.049 0.117 0.023 0.091 0.011 0.165

Pan 0.086 0.023 0.020 0.063 0.131 0.011 0.033 0.004 0.049

Pongo 0.078 0.065 0.077 0.064 0.125 0.079 0.002 0.084 0.075

Bone Nasal Occipital Parietal ______Character 10 11 12 13 14 15 16 17 18

Principal Component PC3 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4

Gorilla 0.021 0.040 0.085 0.032 0.028 0.101 0.070 0.050 0.020

Homo 0.004 0.067 0.068 0.036 0.027 0.084 0.099 0.031 0.033

Hylobates 0.046 0.011 0.028 0.036 0.005 0.129 0.105 0.028 0.002

Macaca 0.091 0.202 0.004 0.038 0.003 0.105 0.130 0.009 0.034

Nomascus 0.029 0.010 0.017 0.106 0.020 0.043 0.018 0.023 0.060

Pan 0.076 0.018 0.040 0.010 0.062 0.025 0.016 0.057 0.013

Pongo 0.016 0.076 0.099 0.046 0.033 0.068 0.020 0.039 0.025

51 Adult male character matrix (continued).

Character Palate Temporal Zygoma ______Bone 19 20 21 22 23 24 25 26

Principal Component PC1 PC1 PC2 PC3 PC4 PC1 PC2 PC3

Gorilla 0.143 0.138 0.038 0.040 0.055 0.164 0.032 0.130

Homo 0.271 0.141 0.049 0.086 0.010 0.216 0.156 0.037

Hylobates 0.064 0.041 0.093 0.019 0.004 0.146 0.062 0.030

Macaca 0.056 0.060 0.112 0.080 0.005 0.094 0.032 0.122

Nomascus 0.206 0.015 0.137 0.011 0.008 0.058 0.209 0.033

Pan 0.105 0.002 0.010 0.023 0.019 0.075 0.022 0.015

Pongo 0.094 0.118 0.022 0.007 0.074 0.087 0.137 0.061

Adult female character matrix.

Bone Frontal Maxilla Nasal ______Character 1 2 3 4 5 6 7 8 9

Principal Component PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1

Gorilla 0.148 0.065 0.047 0.003 0.137 0.024 0.012 0.048 0.104

Homo 0.073 0.088 0.067 0.031 0.068 0.136 0.033 0.031 0.112

Hylobates 0.119 0.032 0.004 0.005 0.153 0.009 0.033 0.025 0.006

Macaca 0.001 0.038 0.055 0.076 0.063 0.041 0.114 0.022 0.108

Nomascus 0.106 0.054 0.005 0.061 0.178 0.024 0.046 0.030 0.225

Pan 0.100 0.026 0.018 0.027 0.127 0.000 0.048 0.018 0.003

Pongo 0.052 0.074 0.076 0.028 0.062 0.086 0.008 0.062 0.128

52 Adult female characters (continued).

Bone Nasal Occipital Parietal ______Character 10 11 12 13 14 15 16 17 18

Principal Component PC2 PC3 PC1 PC2 PC3 PC4 PC1 PC2 PC3

Gorilla 0.049 0.047 0.033 0.069 0.019 0.022 0.080 0.059 0.045

Homo 0.005 0.026 0.115 0.010 0.035 0.015 0.140 0.037 0.068

Hylobates 0.038 0.029 0.018 0.057 0.002 0.024 0.241 0.011 0.023

Macaca 0.051 0.008 0.073 0.036 0.027 0.030 0.018 0.093 0.047

Nomascus 0.011 0.009 0.022 0.024 0.014 0.020 0.090 0.007 0.005

Pan 0.079 0.047 0.022 0.039 0.014 0.016 0.091 0.000 0.012

Pongo 0.065 0.041 0.017 0.001 0.080 0.006 0.038 0.053 0.009

Bone Palate Temporal Zygoma ______Character 19 20 21 22 23 24 25 26 27

Principal Component PC1 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4

Gorilla 0.071 0.117 0.026 0.045 0.004 0.224 0.015 0.070 0.000

Homo 0.134 0.149 0.022 0.055 0.012 0.132 0.174 0.064 0.042

Hylobates 0.095 0.043 0.054 0.034 0.000 0.170 0.099 0.026 0.024

Macaca 0.016 0.018 0.095 0.069 0.029 0.073 0.039 0.082 0.079

Nomascus 0.075 0.013 0.115 0.012 0.026 0.091 0.119 0.050 0.061

Pan 0.070 0.038 0.024 0.040 0.024 0.074 0.013 0.014 0.000

Pongo 0.053 0.042 0.002 0.026 0.071 0.022 0.110 0.089 0.083

53 Juvenile character matrix.

Bone Frontal Maxilla Nasal ______Character 1 2 3 4 5 6 7 8 9

Principal Component PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 PC1

Gorilla 0.074 0.086 0.057 0.050 0.216 0.063 0.065 0.026 0.122

Homo 0.075 0.067 0.028 0.072 0.014 0.145 0.006 0.035 0.130

Hylobates 0.111 0.032 0.008 0.020 0.164 0.022 0.037 0.010 0.114

Macaca 0.120 0.118 0.025 0.030 0.045 0.026 0.069 0.079 0.065

Nomascus 0.096 0.068 0.001 0.034 0.106 0.048 0.010 0.061 0.146

Pan 0.052 0.034 0.014 0.004 0.100 0.031 0.071 0.019 0.077

Pongo 0.035 0.031 0.103 0.003 0.016 0.069 0.084 0.034 0.126

Bone Nasal Occipital Parietal ______Character 10 11 12 13 14 15 16 17 18

Principal Component PC2 PC3 PC1 PC2 PC3 PC4 PC1 PC2 PC3

Gorilla 0.015 0.066 0.114 0.009 0.013 0.018 0.012 0.031 0.044

Homo 0.008 0.000 0.034 0.036 0.043 0.017 0.198 0.010 0.053

Hylobates 0.089 0.016 0.018 0.071 0.004 0.005 0.163 0.024 0.035

Macaca 0.011 0.005 0.059 0.027 0.006 0.005 0.027 0.108 0.045

Nomascus 0.057 0.022 0.024 0.014 0.016 0.012 0.087 0.002 0.034

Pan 0.066 0.041 0.002 0.017 0.033 0.030 0.015 0.041 0.005

Pongo 0.038 0.026 0.050 0.069 0.015 0.018 0.002 0.070 0.027

54 Juvenile character matrix (continued).

Bone Palate Temporal Zygoma ______Character 19 20 21 22 23 24 25 26 27

Principal Component PC1 PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4

Gorilla 0.044 0.027 0.082 0.083 0.015 0.216 0.009 0.082 0.034

Homo 0.162 0.156 0.097 0.033 0.020 0.076 0.054 0.160 0.063

Hylobates 0.191 0.085 0.044 0.066 0.028 0.135 0.077 0.076 0.010

Macaca 0.043 0.159 0.098 0.002 0.020 0.038 0.191 0.032 0.064

Nomascus 0.149 0.029 0.011 0.005 0.037 0.081 0.088 0.042 0.029

Pan 0.115 0.037 0.043 0.013 0.057 0.058 0.042 0.016 0.026

Pongo 0.149 0.047 0.016 0.069 0.025 0.096 0.060 0.088 0.105

55

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