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6-3-2014
The Effect of Population History on Hominoid
Intraspecific Cranial Shape Diversity: Combining
Population Genetic and 3D Geometric
Morphometric Data
Julia Marie Zichello
Graduate Center, City University of New York
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Genetic and 3D Geometric Morphometric Data" (2014). CUNY Academic Works.
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The Effect of Population History on Hominoid Intraspecific Cranial Shape Diversity:
Combining Population Genetic and 3D Geometric Morphometric Data
by
Julia Marie Zichello
A dissertation submitted to the Graduate Faculty in Anthropology in partial fulfillment of the requirements
for the degree of Doctor of Philosophy, The City University of New York
2014
©2014
JULIA MARIE ZICHELLO
All Rights Reserved
ii
This manuscript has been read and accepted for the Graduate Faculty in Anthropology in
satisfaction of the dissertation requirement for the degree of Doctor of Philosophy.
Michael E. Steiper
_______________
Date
_________________________________________
Chair of Examining Committee
Gerald Creed
_______________
Date
________________________________________
Executive Officer
Ryan Raaum
William Harcourt-Smith
Timothy D. Weaver
Supervisory Committee
iii
THE CITY UNIVERSITY OF NEW YORK
ABSTRACT
The Effect of Population History on Hominoid Intraspecific Cranial Shape Diversity:
Combining Population Genetic Data and 3D Geometric Morphometric Data
by
Julia Marie Zichello
Adviser: Michael E. Steiper
Cranial shape diversity within hominoids has been previously studied with the aim of
understanding how levels of diversity in extant species compare with extinct hominin specimens.
This dissertation addresses the question of why cranial shape diversity differs among extant
hominoids. Levels of intraspecific cranial shape diversity are highly varied among hominoids.
For example, Sumatran orangutan cranial shape diversity is more than twice that of all living
humans.
Here, the population history of each species, or sub-species, is considered as a force
potentially structuring phenotypic variation. It is already well established that population history
has shaped patterns of modern human cranial diversity across the world. Yet, to date, no one has
considered that the independent population histories of other apes may have also influenced their
cranial diversity through evolutionary time.
Genetic data from non-coding loci reveal the population history of each taxon. Nucleotide
diversity levels reflect non-selective evolutionary processes—such as mutation, drift, migration
or fluctuations in population size. For each taxonomic group in this study, genetic diversity and
the effective population size (Ne) are compared with cranial shape diversity to determine the
strength of the relationship between these two data-types. 3D cranial landmark data are divided
iv
into two separate analytic units, which represent independent developmental modules. Shape
variance of the cranial vault, and the face, are evaluated together, and then separately.
The following taxa are included in this work: Homo sapiens, Pan paniscus, Pan
troglodytes troglodytes, Pan troglodytes verus, Pan troglodytes scweinfurthii, Gorilla gorilla
gorilla, Pongo pygmaeus, Pongo abelii, Symphalangus syndactylus, Hylobates moloch,
Hylobates pileatus and Hylobates klossii.
Results show a strong positive correlation between intraspecific cranial shape diversity and
nucleotide diversity across all taxa. Species that are more genetically diverse, and have larger
effective population sizes, show more cranial shape diversity. The relationship between cranial
vault shape diversity and genetic diversity is stronger than in the face. Variation in the face is
likely driven by sexual dimorphism in certain species, which may overwrite any signal of
population history.
This work provides new evidence of the strength of non-selective pressures—such as
random mutation and genetic drift—on skeletal elements such as the cranium. Traditionally,
biological anthropologists have looked to adaptation by natural selection as the primary
explanation for patterns of skeletal diversity. The advent of large population genetic data-sets—
which document random evolutionary changes in the genome—have enabled genetic variability
to function as the null hypothesis for explaining phenotypic diversity. If phenotypic diversity
mirrors neutral genetic diversity, non-selective evolutionary forces may sufficiently explain
patterns of phenotypic diversity without invoking a selective explanation. This project increases
our understanding of extant hominoid cranial evolution and therefore elucidates the complex
framework within which extinct hominin species diversity should be evaluated.
v
ACKNOWLEDGEMENTS
8/1/00
Dear Dr. Delson,
I am currently attending Pratt Institute in Brooklyn New York. I will be finishing next semester
and receiving a B.F.A. in Communications Design. For graduate school, I have decided to drastically
change my course of study. I am very interested in what the NYCEP program has to offer. I am aware
that I will have to fulfill several requirements at the undergraduate level before applying to the
program. Please send me any information you have about preliminary requirements for the program.
Thank you for your time.
Upon completion of my art school degree, I found the website of the New York
Consortium in Evolutionary Primatology (NYCEP). Dazzled by such a potentially rich and
exciting intellectual world, I emailed Eric Delson the above message. Had he not graciously
responded, I may never have had the guidance to embark on what was a deeply enriching and
rewarding professional experience. I am grateful for Eric’s first email, and for the professional
and financial support from the New York Consortium in Evolutionary Primatology (NYCEP)
throughout my graduate career.
I would like to express my sincere gratitude to my adviser Michael Steiper for his
enduring patience, enthusiasm and guidance. I am especially thankful that he took a chance and
let an art school student volunteer in his genetics lab. It was through the quiet hum of lab work
that I learned to wonder about big scientific questions. My intellectual world expanded, and I
will never be the same. I am grateful to Ryan Raaum who has always provided extremely cogent
and thorough feedback throughout my graduate career. Will Harcourt-Smith deserves a special
thank you for unwavering professional support, and for taking an indoorsy New Yorker fossil
hunting. He said it would be good for me. He was right. Thank you to Tim Weaver for helpful
comments on this work, and for exploring hominin skeletal diversity in a novel way and
providing the foundations for this dissertation.
vi
Thank you to Jessica Rothman who has always provided extremely valuable and realistic
professional and personal guidance. Her great ambition and motivation is an inspiration. Thank
you to Karen Baab for patience and instruction in 3D morphometrics techniques and beyond.
Thank you to Kieran McNulty for providing important comparative data for this work. Don
Melnick provided gibbon DNA towards the completion of this project, and I am very grateful for
his generosity. Thank you to Mary Blair and Jason Hodgson for continuous sound and optimistic
scientific advice. Genetic data collection was a great deal more efficient, and fun, with lab
assistance from Valentine Kheng.
I have had the opportunity to connect with so many kind and brilliant colleagues who
have served as tremendous support and inspiration. Jennifer Parkinson and Scott Blumenthal, I
sincerely appreciate your friendship, laughs and coffee-science-talk throughout the years. Your
dedication and ebullient enthusiasm for your own work has always amazed me, and I look
forward to seeing your professional and personal lives blossom as we age. Maryjka Blaszczyk,
our shared tendency for dreaming big and falling hard has been a great comfort throughout this
journey. Lindsey Smith and Shahrina Chowdhury, I never knew that scientists could be so cool
and hilarious. Thank you for the much-needed reality checks, glasses of wine, and laughs.
Many people who I have worked with at the Anthropological Genetics Lab at Hunter
College deserve a thank you especially, Wai Kwan Chung, Fiona Walsh and Elaine Guevara.
There is simply no better way to say this: thank you for listening to me complain, and talk about
the orchids, it was a blast. Siobhan Cooke, Lissa Tallman and Lauren Halenar, it was a pleasure
working with you at Hunter, and in the NYCEP room.
To all of my new colleagues at the American Museum of Natural History, especially
Preeti Gupta and Ruth Cohen, thank you for your patience and encouragement while I completed
vii
this PhD. To the entire Youth Initiatives team, and to the students we serve, you cannot know
how much working with you has provided me with hope and enthusiasm for the future. A very
special thank you to Samara Rubinstein who has served as an amazing professional guide from
the moment I met her. Thank you to Joe Califf for dedication to primate evolution and outreach,
and for paving the way for NYCEP in the Sackler lab.
Many understanding and patient friends supported me, and provided vital breaks from
science, especially Alanna Perez, Aileen Parker and Kelly Smith. Thank you to the wonderful,
open-minded and accepting Roy-Lucido clan, and to the Strickletts near and far for steady
encouragement.
Thank you to my three older brothers, had it not been for their critical and overall nerdy
approach to life, it may never have occurred to me to be a scientist. To Mike, your software help
was of absolutely crucial importance to the completion of this project. You are the true scientist
in our family; your curiosity is a constant inspiration. To Chris, your clear advice and tough love
has been invaluable to this process. To Dan, your quiet rise to professional and personal
fulfillment has served as an uplifting and instructive model for me. To Dad, thank you for always
secretly thinking I could do it, I hope the jury is not still out on me. To Mom, thank you for
always being supportive of my pursuits, whether cheerleading, or art school, or graduate school
in anthropology. Thank you for understanding my perspective, even when it differed from your
own. Thank you for allowing me to grow, and growing together with me. I love you.
Most of all thank you to my darling Joe, who has been the most tremendous and selfless
supporter of me, and my work, for so many wonderful years. Thank you for waking me up on
time for the Paranthropus lecture, and for loving me with your whole beautiful heart. You are not
only my favorite hominin, but also my favorite hominoid.
viii
For Joe
&
Mom and Dad
ix
LIST OF TABLES and FIGURES
Table 1.1
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 4.1
Table 4.2
Table 4.3
Human Quantitative Genetics Studies Summary
Autosomal Loci Hominoids
Primer Sequences and Annealing Temperatures
Cranial Landmark Descriptions
Procrustes Distance – Sample Size Test
Average Pairwise Procrustes Distances
Centroid Size Variance
Phylogenetically Corrected Regression Results
15
27
29
35
38
41
43
47
Figure 1.1
Figure 3.1
Figure 3.2
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Figure 4.10
Figure 4.11
Figure 4.12
Figure 4.13
Figure 4.14
Figure 4.15
Figure 4.16
Figure 4.17
Figure 4.18
Figure 4.19
Figure 4.20
Figure 4.21
Figure 4.22
Figure 4.23
Figure 4.24
Figure 4.25
Figure 4.26
Geographic Distribution of Human Cranial and Genetic Variation
Map of Hylobates moloch genetic samples
Cranial Landmark positions on Hylobates cranium
PPD (34)
PPD (12)
PPD (22)
π and θw values
Ne from π and θw
Males + Females (34) - PPD/ Ne from π
Females (34) - PPD/Ne from π
Males (34) - PPD/Ne from π
Males + Females (22) - PPD/Ne from π
Females (22) - PPD/Ne from π
Males (22) - PPD/Ne from π
Males + Females (12) - PPD/Ne from π
Females (12) - PPD/Ne from π
Males (12) - PPD/Ne from π
Males + Females (34) - PPD/ Ne from θw
Females (34) - PPD/Ne from θw
Males (34) - PPD/Ne from θw
Males + Females (22) - PPD/Ne from θw
Females (22) - PPD/Ne from θw
Males (22) - PPD/Ne from θw
Males + Females (12) - PPD/Ne from θw
Females (12) - PPD/Ne from θw
Males (12) - PPD/Ne from θw
Males + Females (34) - Size/ Ne from π
Females (34) - Size/ Ne from π
Males (34) - Size/ Ne from π
12
25
34
42
43
44
48
49
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
x
TABLE of CONTENTS
ABSTRACT
iv
ACKNOWLEDGEMENTS
vi
DEDICATION
ix
LIST OF TABLES and FIGURES
x
CHAPTER 1: INTRODUCTION
1
PROJECT SUMMARY
1
MECHANISMS OF SKELETAL VARIATION
Detecting Natural Selection in Phenotypic Traits
Genetic Drift on Genotype and Phenotype
Mutations in Genes that Control Skeletal Morphology
Environmentally Driven Skeletal Change
4
4
7
9
10
ANALYZING MORPHOLOGY WITH GENETICS
Modern Human Population Genetics and Morphology
Quantitative Genetics and Primate Morphology
Non-Primate Population Genetics and Morphology
10
11
16
18
CHAPTER 2: RESEARCH DESIGN and HYPOTHESES
Central Questions
Hypotheses
19
20
20
CHAPTER 3: MATERIALS and METHODS
23
SUMMARY
23
I. GENETIC DATA, MATERIALS
Hylobates moloch
Homo, Pan, Gorilla, Pongo
24
24
25
II. GENETIC DATA, METHODS
PCR – Hylobates moloch
Sequence Alignment – Hylobates moloch
Bacterial Cloning – Hylobates moloch
Sequence Alignment – publicly available sequence data
Calculating Nucleotide Diversity (π, θw)
Calculating Effective Population Size (Ne)
28
29
30
31
31
31
III. MORPHOLOGICAL DATA, MATERIALS
Landmark Descriptions
31
35
xi
IV. MORPHOLOGICAL DATA, METHODS
Generalized Procrustes Analysis
Calculating Average Pairwise Procrustes Distance, bootstrapping
Combining datasets, Consistency Analysis
36
36
37
37
V. MORPHOLOGY and GENETICS
Phylogenetic correction of OLS regressions
38
38
CHAPTER 4: RESULTS and DISCUSSION
40
RESULTS
Average Pairwise Procrustes Distance Results
Nucleotide Diversity and Ne estimates
Ne against 34-landmark dataset: males + females, males, females
Ne against 22-landmark dataset: males + females, males, females
Ne against 12-landmark dataset: males + females, males, females
Ne against 34-landmark size dataset: males + females, males, females
40
40
46
50
52
52
53
DISCUSSION
Hypotheses Revisited
Genetic Diversity and Population History of Hominoids
75
75
77
LINKING HOMINOID GENETICS with CRANIAL MORPHOLOGY
Population based Analyses of Craniodental traits
Genetic Distances between Taxa versus Cranial Distances
Application of this Research to Questions in Paleoanthropology
Future Directions
81
82
82
83
87
APPENDIX
90
BIBLIOGRAPHY
99
xii
CHAPTER 1: INTRODUCTION
This project is part of a growing effort to understand how population history has shaped
human and primate phenotypic variation (Relethford 1994; Marroig and Cheverud 2004;
Ackermann and Cheverud 2004; Manica et al. 2007; Roseman and Weaver 2007; von CramonTaubadel 2012). The question of whether extant hominoid morphological diversity mirrors
neutral genetic diversity was raised at the outset of molecular evolutionary advances in the
primates (Ruvolo 1997). Despite this cogent analysis, subsequent studies of primate skeletal
diversity were largely separate from analyses of genetic diversity. This artificial dichotomy of
using either genes or morphology to understand primate evolution differs from how most nonprimate taxa are routinely studied from an evolutionary perspective (Hofreiter et al. 2002; Rasner
et al. 2004; Lougheed et al. 2006; Cardini et al. 2007; Dhuyvetter et al. 2007; Fulgione et al.
2008; Hi 2013; Sistrom 2013). Here, genetics and skeletal morphology are examined together to
determine if intraspecific cranial shape diversity in a sample of hominoid species reflects the
neutral population history across species. These results will clarify to what degree patterns of
intraspecific cranial variation in apes have been shaped by microevolution. Potentially, this work
can reveal why certain primate species are more cranially variable than others. This project was
designed to provide a new perspective on cranial evolution in extant hominoids and proposes to
shed light on other questions of anthropological relevance, such as species delimitation in the
hominin fossil record (Tattersall 1986, 1992; Delson 1990; Plavcan 1993; Shea et al. 1993;
Kimbel and White 1998; Plavcan and Cope 2001; Guy et al. 2003; Harvati 2003; Baab 2008).
There are four forces of evolutionary change: mutation, migration, genetic drift and
natural selection. All biological systems—including genes, skeletons and behavior—are
theoretically influenced by each of these forces to varying degrees. An inherent challenge in
1
evolutionary biology is disentangling the action of these forces to determine how evolution has
shaped traits (Fisher 1930; Wright 1931; Mayr 1983; Provine 1985). It is well accepted that some
portion of genetic variation is neutral with respect to fitness, and instead reflects the random
processes of drift, mutation and migration (Kimura 1968). Yet, there is considerable
disagreement among biologists as to whether phenotypic variation can also be neutral, and if that
neutrality can be quantified (Nei 1986; Lynch and Hill 1986; Bromham et al. 2002, 2003; Davies
and Savolainen 2006; Millstein 2007). The dominant force shaping phenotype is often taken to
be natural selection.
To study the impacts of selection, function and development on skeletal morphology,
investigators often look for environmental and ecological correlates—such as climate, feeding
mechanics or locomotor repertoire—to explain patterns of variation (Young 2006; Hubbe et al.
2009; Makedonska et al. 2012; Williams 2012). Skeletal variation between species, or
populations, is then interpreted as evidence of the differential impact of these selective pressures.
However, these approaches generally overlook the possibility that skeletal variation may instead
reflect demographic microevolutionary processes unique to each species. According to
evolutionary theory, natural selection is not the only force structuring diversity, either within or
between species (Fisher 1930; Gould and Lewontin 1979; Harris 2010). So, we expect some
portion of morphological variation to be shaped by the stochastic forces of drift and mutation, in
a manner analogous to genetic data (Lande 1976; Lynch and Hill 1986; Lynch 1990; Nei 2007).
This neutral theory of phenotypic evolution is not widely accepted among primate
morphologists. Yet, through the use of well-sampled population genetic datasets as a baseline,
there is growing evidence that cranial diversity in humans reflects neutral processes (Relethford
1994; Roseman 2004; Roseman and Weaver 2004, 2007; Manica et al. 2007; Relethford 2009;
2
Manica et al. 2010; Ricaut et al. 2010). The evolutionary history of a species, and the
population-level fluctuations that occur through time—population splits, bottlenecks, and
migration—can be estimated using genetic data. Genetic data from neutrally evolving loci
provide a window into stochastic evolutionary forces such as mutation, genetic drift or
fluctuations in population size. Skeletal traits, however, do not necessarily reflect population
history with the same resolution as neutral genetic markers. Instead, a suite of functional,
developmental and selective pressures can constrain and modify skeletal structures. These
morphological changes can act to overwrite the signal of population history, or phylogeny, from
skeletal elements. However, through comparing neutral genetic data with skeletal data we can
begin to decipher whether phenotypic variation follows neutral patterns.
Here, I test the strength of the relationship between genetic variation and phenotypic
variation across 12 hominoid taxa to address the central question: do taxa with more genetic
diversity also show greater intraspecific cranial shape diversity? To frame this as a
hypothesis, I posit that the effective population size (Ne)—which reflects the amount of drift a
population has experienced—will correlate with the magnitude of intra-taxic cranial shape
diversity. Rejection of the null hypothesis, and no positive correlation between data-types, would
demonstrate that the observed pattern of variation cannot be readily explained by neutral
processes. Instead, patterns of intraspecific cranial diversity may be influenced by selective or
developmental pressures. A positive correlation between data-types, and failure to reject the null
hypothesis, would suggest that stochastic factors are a sufficient explanation for the observed
patterns of cranial variation within each taxon.
It is well known that hominoids show different amounts of cranial diversity within
species, both in the neurocranium and the face (Uchida 1992, 1996; Ackermann 2001; McNulty
3
et al. 2006; Vioarsdottir and Cobb 2004; Jabbour 2008). It is also known that, because of their
independent population histories, each taxon possesses a different amount of genetic diversity in
both the mitochondrial and nuclear genomes (Ruvolo et al. 1994; Noda et al. 2001; Fischer et al.
2006; Prado-Martinez et al. 2013). In this project, I test if these differences are congruent. This is
one of the first attempts to contextualize broad patterns of hominoid intraspecific cranial shape
diversity within a population genetic framework.
MECHANISMS OF SKELETAL VARIATION
Skeletal diversity within a species can be shaped by both genetic and non-genetic
mechanisms. Genetic mechanisms include selection, drift and mutation. Non-genetic skeletal
change can be caused by plasticity in response to environmental or mechanical stress during an
individual’s lifetime. Development, which links genotype to the expression of phenotype,
operates though several of these mechanisms: genetic, epigenetic and environmental. Together,
these processes can affect variation at all temporal levels, leaving the skeleton an evolutionary
palimpsest for biologists to decipher.
Detecting Natural Selection in Phenotypic Traits
When interpreting skeletal traits, physical anthropologists have traditionally favored
selective hypotheses over any others (Washburn 1963; Steegman 1970; Beals 1972; Carey and
Steegman 1981; Beals et al. 1983; Hubbe et al. 2009). The idea that a physical trait evolved for a
particular function, or in response to a distinct environmental pressure, is conceptually neat and
alluring. The theory of adaptation by natural selection was formulated prior to a deep
understanding of population genetics (Darwin and Wallace 1858). The Modern Synthesis then
4
provided a means of extending theory from genetic models to study phenotypic variation
(Simpson 1944). Yet, it was not until after The Synthesis that a quantitative framework for
measuring selection on phenotype was put into practice across a wide range of taxa (Lande 1976;
Lynch and Hill 1986; Endler 1986; Lynch 1989; Cheverud 1988).
In this new framework, neutral evolution, a concept forwarded by population genetics,
provides a set of testable predictions to which phenotypic diversity can be compared (Kimura
1968). Neutral evolution functions as the null hypothesis and only if falsified are selective
explanations considered. This is the reverse approach to “adaptationism”, or the tendency of
evolutionary biologists to attribute all phenotypic features to natural selection over other
evolutionary pressures (Gould and Lewontin 1979).
The field of quantitative genetics has contributed a set of models to measure variation in
quantitative traits and test for evidence of selection versus drift. This approach incorporates
neutral theory together with genetic parameters such as heritability and additive genetic variance.
For a trait to be able to be under selection, it needs to be heritable. All traits are not equally
heritable; some are more strongly influenced by environmental variance. This directly affects
their response to selection. Traits with higher heritability will respond more readily to selection
over successive generations. Broad sense heritability of a trait can be estimated by measuring the
proportion of total phenotypic variance that can be explained by genotypic variance, using the
equation, h2 = VG/VP. This estimate is more accessible in model organisms, or captive animals,
where selective breeding over successive generations can reveal patterns of inheritance.
Continuous phenotypic traits typically have a complex genetic architecture, with many genes
contributing independently to the final form. Additionally, two alleles at one locus may have
independent effects, and no pattern of dominance. This is additive genetic variance. In this case,
5
narrow sense heritability estimates the proportion of genetic variation that is due to additive
genetic variance, via this equation h2 = VA/VP. Additive genetic effects are the degree to which
each allele that independently contributes to a phenotype can alter the mean phenotype.
A confounding aspect of estimating selection on phenotype is that traits are often
correlated and not independent units that respond to selection in isolation (Olson and Miller
1956; West-Eberhard 2003). This integration can occur at different levels of trait expression:
from genetic to developmental. For example, one gene can control several traits. Therefore, if
one gene is selected for, other traits, which are not themselves the targets of selection, can be
swept along (Hlusko 2004). Developmental molecular biology offers a means of tracking how
pleiotropy can govern several basic aspects of body plan or serially homologous structures
simultaneously. Understanding this phenomenon is key in formulating trait-specific adaptive
hypotheses for skeletal evolution.
It is a challenge to empirically demonstrate, rather than informally imply, the signature of
adaptation by natural selection in both living and extinct species. The presence of a trait and an
understanding of its proximate function, does not alone demonstrate that it is an adaptation that
has evolved by natural selection. Quantitative models that measure trait variance offer a more
statistically robust approach to this problem, although the field is far from a consensus regarding
these issues. The application of these models to questions in physical anthropology has shifted
some previously held adaptive hypotheses in human evolution (Harvati and Weaver 2006;
Weaver et al. 2007, 2008; Roseman and Weaver 2007). Molecular geneticists have long debated
the strength and frequency of selection versus neutrality at the genetic level in what has been
called the “neutralist-selectionist” debate (Simpson 1964; Maynard and Smith 1968; King and
Jukes 1969). The neutral theory of molecular evolution states that the majority of evolutionary
6
changes at the molecular level are not caused by selection, but by the stochastic fixation of
selectively neutral alleles (Kimura 1968, 1991). Importantly, this approach allows geneticists to
detect deviations from neutrality, and determine whether functional loci are under selection. In
the case of phenotype, neutrality also offers a set of predictions in which to form hypotheses
about phenotypic evolution. For example, broad congruence between neutral genetic patterns and
phenotypic patterns suggest that the phenotypic aspect may also be a result of random drift and
mutation. This practice of quantifying evolutionary neutrality, and by extension the neutralistselectionist debate, now extend beyond molecular population genetics to the study of phenotypic
evolution.
The Effects of Genetic Drift on Genotype and Phenotype
How can non-selective evolutionary forces, like drift, affect skeletal variation? Genetic
drift is the change in allele frequency in a population from generation to generation that is due to
stochastic sampling. Drift reduces nucleotide diversity. The smaller the population size, the
stronger the effect of genetic drift. For example, humans, bonobos and West African
chimpanzees have had past population size reductions that have subsequently reduced
heterozygosity likely though the action of genetic drift (Prado-Martinez et al. 2013).
Neutral non-functional genetic markers are typically selected to detect population history,
but processes like random genetic drift and inbreeding are actually genome-wide phenomena
(Przeworski et al. 2000). This is different than selection, which we only expect to effect specific
genes linked to adaptive function. These patterns are becoming more well-understood as whole
genomes are sequenced and analyzed at the species level. For example, runs of homozygosity are
present throughout the genomes of bonobos and certain human populations. This pattern
7
indicates a genome-wide loss of diversity through inbreeding (Prado-Martinez et al. 2013).
Therefore, in addition to non-coding loci, protein-coding genes, or regulatory regions, that
underlie phenotypic traits are also subject to these stochastic forces. Alleles can become fixed
due to genetic drift. If their effect is neutral with respect to fitness, then we expect a certain
amount of genetically controlled phenotypic variation to be random and not the result of
adaptation by natural selection.
Reductions in population size reduce genetic diversity through sampling bias (Nei 1975).
Yet, there is not a clear relationship between reductions in population size and a concomitant
reduction in phenotypic diversity. Reed and Frankham (2001) tested the strength of the
relationship between molecular and quantitative traits by performing a meta-analysis across
many taxa. They find a weak relationship between phenotypic and genetic variance. It is
important to note that the traits chosen for this analysis were those linked to life history and
ecological adaptation. They did not set out to discover if a trait was neutral, but rather to
determine the strength of genetic diversity estimates as an effective tool for managing
populations from an ecological standpoint. The nature of meta analysis did not allow for
selection for homologous traits across all species, so the data were not partitioned on a trait-bytrait basis, but rather as a single estimate of morphological variance. Smaller-scale analyses in
other taxa, such as lizards, gophers and flowers have demonstrated a positive correlation between
molecular and morphological variance (Soulé and Yang 1973; Briscoe et al. 1992; Soulé and
Zegers 1996; Waldman and Andersson 1998). Whitlock and Fowler (1999) performed
experiments in Drosophila that demonstrate that reducing population size in successive
generations reduces phenotypic diversity. Similarly, Manica and colleagues (2007) present data
to support the hypothesis that bottlenecks throughout human population history have
8
successively reduced cranial diversity, mirroring the loss of genetic diversity. Although the
statistical strength of the results from Manica et al. (2007) have been debated. Distance from
Africa explains 87% of the variance in heterozygosity at genetic markers, but values for
morphological data are in the range of 6-9% (Roseman and Weaver 2007).
Genetic Mutations that Control Skeletal Morphology
How do we know mutation can affect skeletal change? Developmental genetics work has
demonstrated that cranial shape changes can be caused by mutations in the promoter regions of
certain genes (Willmore et al. 2006). This alters gene regulation and effects skeletal shape.
Cranial development is polygenic, with many genes epistatically interacting to achieve the adult
form. Work in domestic dogs has shown that small genetic changes can produce large effects on
cranial form (Schoenbeck et al. 2012). Therefore, it is plausible that the accumulation of constant
mutations over evolutionary time in hominoid lineages could affect the level of intraspecific
phenotypic variation we see today. Also, mutation can influence the ability to vary, or variability.
Experiments in Drosophila and mice show that mutant phenotypes vary more between
individuals than wild-type (Waddington 1942; Rendel 1967; Willmore 2006).
Mutation is the primary source of new genetic variants. The concept of ‘the molecular
clock’ posits that mutations are fixed at a constant rate over evolutionary time (Zuckerkandl and
Pauling 1962). The neutral theory of molecular evolution bolstered this concept by predicting
differential accumulation of mutations in functional versus non-functional genes (Kimura 1968).
Physiological factors like body-size and metabolic rate may impact mutation rate, although we
expect it to be relatively constant in closely related species (Steiper and Seiffert 2012). For
example, all hominoids share a similar slow mutation rate, relative to other primates (Goodman
9
1985; Steiper et al. 2004).
Environmentally Driven Skeletal Change
Non-genetic environmentally driven alterations to skeletal form can accrue during the
lifetime of an individual. Although these changes are not heritable, they can obscure what is
heritable. These changes can inflate variation within species across heterogeneous environments
(Roseman 2004). Osseous remodeling of bone can occur in response to mechanical stress such as
locomotion or mastication (Pearson and Lieberman 2004; Schaffler and Burr 2005). This is
referred to as Wolff’s law (Wolff 1986; Martin et al. 1998). Also, aspects of climate such as
temperature, humidity and altitude can affect skeletal form.
ANALYZING MORPHOLOGY with GENETICS
In an effort to address the multiplicity of pressures that shape phenotypic evolution
outlined above, evolutionary biologists have begun to compare genetic variation at neutral loci to
phenotypic diversity in a number of traits. Specifically, studies have investigated how genetic
drift has influenced primate cranial variation, both within and between species. One of the most
rigorously tested findings to emerge from this work is that modern human cranial variation is
governed mainly by drift and not selection. This has dramatic implications for adaptive
hypotheses and for how extant models of cranial variation relate to extinct diversity. For
example, if the cranium overwhelmingly reflects evolutionary history, and not environmentally
driven selection, then the utility of extinct cranial morphology as a proxy for genetic relatedness
between taxa is supported. Further, detecting selective patterns in the cranium should be
tempered by considering population history first.
10
Modern Human Population Genetics and Morphology
Prior to a comprehensive understanding of human genetic variation, human cranial shape
variation was largely attributed to climatic adaptation affected by rainfall, humidity and latitude
(Boas 1912; Koertvelyessy 1972; Hylander 1972; Guglielmino-Matessi 1979; Carey and
Steegmann 1981; Beals 1983; Franciscus and Long 1991). This concept was based on the
principles of Bergman’s and Allen’s rules that brachycephaly (higher width to length ratio)
would be advantageous in cold climates. Today, results from quantitative analyses that merge
cranial data with population genetic data largely contradict these findings. Instead, a nonselective hypothesis for the apportionment of worldwide human cranial variation is supported.
Several independent studies support the hypothesis that human cranial variation mirrors patterns
of genetic diversity across the world selection (Relethford 1994; Roseman and Weaver 2004;
Manica et al. 2007; Smith et al. 2007; Roseman and Weaver 2007; von Cramon-Taubadel and
Lycett 2008; Betti et al. 2009; Relethford 2009; Smith 2009; von Cramon-Taubadel 2009; Betti
et al. 2010; Manica et al. 2010; Ricaut et al. 2010; Smith 2011). Two main types of analyses are
used to compare genotypic and phenotypic data in human populations: isolation by distance and
FST /QST comparisons.
11
Figure 1.1 Geographic Distribution of Human Cranial (a) and Genetic Variation (b)
(Manica et al. 2007)
Human genetic diversity best fits a serial founder effect model (Deshpande et al. 2009).
This explains the gradient of diversity across the world through successive sub-sampling from a
larger population as humans migrated to new lands. As a result of this, with increasing distance
from Africa, both genetic and cranial diversity decline (Ramachandran et al. 2005; Manica et al.
2007). This pattern is attributed to modern human populations emerging in Africa approximately
200,000 years ago and expanding rapidly to other parts of the world. This apportionment of
12
genetic variation was first observed using mtDNA (Caan 1987). Subsequent studies have refined
their genetic sampling approach and solidified the existence of this pattern to support the Out of
Africa migration of modern humans (Cavalli-Sforza 1994; Harpending et al. 2000; Rosenberg et
al. 2002; Ramachandran et al. 2005; Manica et al. 2005; Prugnolle et al. 2005; McEvoy 2011;
Henn 2012). Unlike founder effect models, FST / QST comparisons do not necessarily have a
spatial component, but are measures of population differentiation. FST measures differentiation
between populations. QST is the analogous measure for quantitative traits. Then, if FST calculated
from neutral genetic data is proportional to QST, random genetic drift without selection is
sufficient to explain the variation for quantitative traits (Spitze 1993). In humans, the congruence
between FST and cranial QST has been compared with other complex traits that are known to be
under selection. For example, selection has acted on skin color to strongly differentiate between
populations (Relethford 2002). Here FST > QST. This pattern significantly departs from neutral
expectations and from those results using cranial data (Roseman 2004).
Different methods have demonstrated that the distribution of human cranial diversity can
be explained by drift, although, specific cranial regions show different degrees of strength in
matching genetic patterns. For example, the temporal bone and basicranium follow geographic
patterns similar to neutral genetic loci but facial and nasal shape less so (Smith et al. 2007; Smith
2009; von Cramon-Taubadel 2009). The density and geographic distribution of human cranial
datasets also allows researchers to test for climate as a variable influencing cranial form.
According to this work, climate does not have a significant influence on the apportionment of
human cranial shape diversity across the world. One exception may be in extremely cold adapted
regions, evident in facial morphology of the Siberian population (Roseman 2004).
13
Similar tests have also been applied to study postcranial diversity in humans. The pelvis,
like the cranium, shows declining diversity with increasing distance from Africa (Betti et al.
2012; Betti et al. 2013). However, long bones do not show the same pattern (Betti et al. 2012).
Instead, climatic adaptation to temperature is evident in the tibia and femur. In this case, the
signature of population history is obfuscated by climatic adaptation. This synthetic approach to
measuring human phenotypic diversity is possible in recent years because of the accumulation of
large genetic datasets.
14
TABLE 1.1
Human Quantitative Genetics and Skeletal Morphology
15
Author
Skeletal Region
Method
Genetic Loci
Tests
Results
Relethford 1994
cranium
linear measurements
microsatellites
Fst
gene/cranium congruence
Relethford 2004
cranium
linear measurements
microsatellites
IBD
gene/cranium congruence
Roseman 2004
cranium
linear measurements
microsatellites
Fst/Qst
climatic selection, Siberia
Roseman and Weaver 2004
cranium
linear, shape
RFLP
Fst, compared to skin
climatic selection, nasal
Harvati and Weaver 2006
cranium
3D GM
microsatellites
Mantel tests
climatic selection, face
Smith et al. 2007
temporal bone
3D GM
STR
Matrix correlations
temp,latitude and size
Manica et al. 2007
cranium
linear measurements
microsatellites
Distance models
diversity declines outside of Africa
Betti et al. 2009
cranium
linear measurements
microsatellites
Distance models
von Cramon-Taubadel, 2009
cranium
3D GM
microsatellites
Distance Matricies
no role for climate
temporal, sphenoid, frontal, parietal fit
neutral model equally
Betti et al. 2010
cranium
linear measurements
no genetic data
IBD
climatic selection
Smith et al. 2009
cranium
3D GM
microsatellites
Distance correlations
vault no correlation
Betti et al. 2012
pelvis/long bones
linear measurements
no genetifc data
Iterative founder model
long bones show temp adaptation
Betti et al. 2013
pelvis
3D GM
no genetic data
Distance correlation
pelvis fits neutrality
Quantitative Genetics and Primate Morphology
Despite the rigorous testing of human cranial data against neutral models, it remains
unknown if intraspecific congruence between genes and morphology is also the case in nonhuman primates. Thus far, the majority of work in non-human primates has been done using
quantitative genetics models to test for drift on cranial diversification between species, not
within. Cranial diversity between fossil hominins and in New World monkeys has been tested for
evidence of drift using quantitative genetics models. Quantitative genetics theory posits that if
drift has shaped trait variation, the ratio of its variance within a species should be proportional to
its variance between species (Lande 1979; 1980). If the within group variance deviates from the
between group variance, selection, not drift may be shaping diversity. This approach enables
testing for the evidence of drift with morphological data without direct genetic data. This method
showed that cranial diversity in platyrrhines and australopiths cannot be solely due to genetic
drift and instead that ecological adaptations to different dietary niches may have driven
diversification between species (Ackermann and Cheverud 2002; Ackermann and Cheverud
2004; Marroig and Cheverud 2004).
The work of Ackermann and Cheverud (2004) marked one of the first attempts to
quantify fossil hominin craniofacial variation within a quantitative genetics framework by testing
for drift versus selection. Through the analysis of variance covariance matrices between taxa, the
authors demonstrated that craniofacial diversity between robust and gracile australopiths could
not be explained by a neutral model of drift alone. The differences in patterns of variance
between these two groups suggest that dietary adaptation played a key role in their
morphological differentiation. It was also demonstrated that the differences between
australopiths and the genus Homo requires a non-random adaptive explanation. On the other
16
hand, patterns of diversity between fossil crania of early Homo do not exceed what would be
expected under a neutral model of drift. While all analyses of fossil hominin diversity are
intrinsically hampered due to small sample sizes, this approach models diversity from a
microevolutionary perspective, treating the morphology of each specimen as representative of a
dynamic and variable population. Differences between cranial morphology of Neanderthals and
modern humans have been one of the most frequently cited hominin examples of adaptive
differences driven by climate (Coon 1962; Franciscus 2003; Finlayson 2004; Holton and
Franciscus 2008). Neanderthals are characterized by robust, long, low crania, wide nasal
openings and large para-nasal sinuses. This suite of features has been posited as an example of
adaptation to the cold climate of Pleistocene Europe. By applying the same approach as
Ackermann and Cheverud, Weaver and colleagues (2007) showed that morphological
differences between Neanderthals and modern humans in fact did not exceed what would be
expected under a neutral model of drift. The aforementioned quantitative research is beginning to
upend previously held adaptive hypotheses for fossil taxa and is forging a new view of
phenotypic change, particularly for well-represented fossil genera such as Australopithecus and
Homo.
17
Non-Primate Population Genetics and Morphology
Illuminating patterns of morphological evolution using population genetics extends
beyond humans, primates and hominins. This approach has been applied to a variety of other
taxa, some of which are more conducive to these types of analyses due to highly restricted
geographic ranges, short generation times and the accessibility of genetic and phenotypic
samples from the same individual. Many studies in non-primate taxa that combine population
genetics and morphology are performed with the goal of clarifying species boundaries as
conservation units. These analyses are done from the perspective of estimating ecological
biodiversity, however the theoretical basis for this work is the same as in modern humans.
Population genetics serves as a foundation for inferences about how phenotypic variation reflects
species history and adaptation. In rodents, amphibians, insects and fish, ecologically relevant
traits such as coloration, body size, vocalizations and behavior have been measured against
genetic markers (Hofreiter et al. 2002; Rasner et al. 2004; Lougheed et al. 2006; Cardini et al.
2007; Dhuyvetter et al. 2007; Fulgione et al. 2008). This work integrates aspects of
biogeography to estimate migration, gene flow and environmental factors that drive speciation.
In many cases, neutral genetic distances between populations (FST) are compared to phenotypic
distances (QST) as part of a holistic effort to describe meaningful taxonomic units (Leinonen et al.
2006; Wang and Summers 2010). Overall, this work underscores the widespread utility of using
population genetics to elucidate the microevolutionary causes of complex phenotypic traits.
18
CHAPTER 2: RESEARCH DESIGN and HYPOTHESES
The cranium has been the focus of morphological studies in primate and human evolution
for decades, both from a functional and evolutionary perspective. Myriad evolutionary,
developmental and environmental forces affect shape variation in the cranium. Therefore, the
utility of the cranium to reflect phylogenetic relationships has been questioned (Collard and
Wood 1999). This project aims to clarify the mechanisms that generate intraspecific cranial
shape variation in hominoids. It is this intraspecific variation that evolution acts upon to drive
diversification between species (Darwin and Wallace 1858). A large part of extinct hominin
species delimitation relies on cranial morphology as a marker for evolutionary relationships
between species and among populations. Therefore, it is of crucial importance to physical
anthropology, and more broadly to evolutionary biology, to quantify the nuanced differences
between individuals of the same species in order to detect and explore evolutionary change at its
origin.
The central goal of this thesis is to provide a novel population-based perspective on the
evolution of cranial form in apes. This project is focused on intraspecific diversity; therefore
many individuals from each taxon were required for robust estimates of variation. To maximize
the amount of taxa in this analysis, data collection and compilation for this project were designed
using comparable methods as previous researchers for both morphology and genetics. Then, new
data and previously collected data were combined to span 12 hominoid taxa, with several
individuals in each group.
19
Through the joint analysis of genetic and morphological data from these 12 taxa the following
questions will be addressed:
1) Does the magnitude of neutral nucleotide variation within taxa correlate with the
magnitude of cranial shape variation?
2) How does intraspecific cranial shape diversity in the great apes compare to hylobatids?
3) Does sexual dimorphism inflate cranial diversity within taxa?
4) Do certain regions of the hominoid cranium reflect neutral evolutionary processes more
than others?
5) Can a population-based approach shed light on questions of species delimitation in the
hominin fossil record?
HYPOTHESES
The central objective of this project is to assess if the magnitude of cranial shape
variation within taxa mirrors the magnitude of their genetic diversity. Here, neutral population
genetic data will anchor the analysis of morphological variation in a historically relevant context
for each species. Cranial and genetic diversity will be measured in 12 taxa, representing 6
different genera.
Hypothesis I: The population history can explain the magnitude of intraspecific cranial
diversity across a range of hominoid taxa.
Predictions: Species with larger effective population sizes (Ne) assessed through studies of
neutral genetic markers will show higher levels of intraspecific diversity in the cranium, as
20
assessed by average pairwise Procrustes distances. When comparing variation across multiple
hominoid taxa, those species with higher neutral genetic diversity will also show more cranial
shape diversity. This would indicate compatibility with neutral evolution on cranial evolution,
and would mirror the pattern seen in humans.
Alternative: Lack of correspondence between relative levels of cranial and genetic diversity
indicates that a neutral model cannot account for the morphological diversity in some or all taxa.
A plausible alternative is that selection is acting on cranial diversity in some or all taxa.
Hypothesis II: Within each taxon, both the neurocranium and the face will show patterns
consistent with drift, not natural selection. Previous work has shown that these two cranial
regions are developmentally separate and therefore potentially subject to the action of
evolutionary forces in different ways (Leamy et al. 1999).
Predictions: Variation in the neurocranium will be consistent with the action of drift. In contrast,
variation in the face will show patterns that deviate from neutrality and therefore indicate
selection.
Alternative 1: Both the neurocranium and face for all three species will show patterns indicating
the action of selection.
Alternative 2: There will be an inconsistent pattern of cranial and genetic diversity among all
species, and therefore a non-significant relationship between datatypes.
21
Hypothesis III: Cranial shape variation within each taxon is driven by sexual dimorphism.
Therefore when male and female cranial data are analyzed separately the average pairwise
Procrustes distance will shift significantly from the value for the combined sex dataset.
Predictions: Species with higher average Procrustes distances will also be the most sexually
dimorphic in size and shape (i.e. Gorilla and Pongo).
Alternative: When males and females are looked at separately for each taxon, the relationship
between the magnitudes of diversity will not change, indicating that diversity within a taxon is
maintained even within each sex.
22
CHAPTER 3: MATERIALS and METHODS
This chapter is divided into the following five sections:
I. Genetic Data, Materials
II. Genetic Data, Methods
III. Morphological Data, Materials
IV. Morphological Data, Methods
V. Combined Analysis of Genetic and Morphological Data
The broad methodological goals of this project were to estimate intraspecific nucleotide
diversity and effective population size (Ne) in the following hominoid taxa: Homo sapiens, Pan
paniscus, Pan troglodytes troglodytes, Pan troglodytes verus, Pan troglodytes schweinfurthii,
Gorilla gorilla gorilla, Pongo pygmaeus, Pongo abelii, Symphalangus syndactylus, Hylobates
moloch, Hylobates pileatus and Hylobates klossii. Then, in the same 12 taxa, estimate average
pairwise Procrustes distances (PPD) based on 3D cranial landmark data. The final combined
analyses are phylogentically corrected regressions to determine if a positive correlation exists
between these two measures of diversity.
New 3D cranial landmark data were collected on the 3 hylobatid taxa. New genetic
sequence data were collected on 13 individuals of Hylobates moloch. Cranial landmark data from
the great apes is from McNulty (2003). Data on genetic diversity of members of the Hominidae
were gathered from Fischer et al. (2006) and Chan et al. (2013). 3D geometric morphometric
methods were used to quantify shape differences within taxa, while correcting for size. Cranial
landmark data were divided into two distinct sets based on developmental modules. In order to
address sexual dimorphism as a confounding aspect of variation, males and females were
considered both together and separately. Genetic and morphological datasets were culled to
ensure reasonable geographic overlap of individuals within each sample set. All cranial landmark
data were taken on museum specimens from wild caught individuals; zoo specimens and
23
juveniles were excluded. The temporal range of the cranial specimens extends back to 1897. The
genetic data are from the last 10 years. Populations have undoubtedly shifted during that time,
however the patterns that I am examining are unlikely to be affected by that degree of time
difference.
Genetic data used here consist of the same neutral, non-coding autosomal loci across all
taxa. These regions were initially studied to reveal the population histories of humans,
chimpanzees, gorillas and orangutans by Fischer et al. (2006). My genetic data collection was
designed to sequence the same loci in Hylobates in order to facilitate comparison between
species. Recently, Chan et al. (2013) had the same approach in several members of the
Hylobatidae; this study provided several additional genetic data points for this study.
The same cranial specimens were microscribed by both researchers and compared to
address any interobserver error. Incongruence of landmark collection was corrected by reducing
the landmark dataset to include only landmarks homologous between researchers.
I. GENETIC DATA, MATERIALS
To estimate nucleotide diversity (π, θW) and effective population size (Ne) for 12
hominoid taxa, newly collected sequence data were combined with publicly available data. Here,
new nuclear autosomal sequence data were collected for 13 Hylobates moloch individuals. To
facilitate comparison between this species and publicly available hominoid population genetic
data, 13 independent non-coding loci were selected based on their homology with regions
already collected in other apes (Fisher et al. 2006). A total of 92,053 base pairs were sequenced
for this project, 7,081bp from each of 13 H. moloch individuals. DNA samples for H. moloch
were provided by Don Melnick, Columbia University. These samples were initially collected
24
from Java, Indonesia for a study of H. moloch population structure using mtDNA (Andayani et
al. 2001). The samples were obtained from four geographically separate localities from Western
and Central Java: Gunung Slamet, Gunung Masigit-Simpang-Tilu, Gunung Gede-Pangrango and
Gunung Halimun (Figure 3.1). The previous mtDNA study showed that these individuals form
two separate mtDNA clades: Western and Central.
Figure 3.1 Location of H. moloch populations.
(Andyani et al. 2001)
Publicly available sequence data were collected for the following taxa (Table 3.1): Homo
sapiens (Fischer et al. 2006), Pan troglodytes troglodytes (Central chimpanzees) (Fischer et al.
2004; 2006), Pan troglodytes schweinfurthii (Eastern chimpanzees) (Fischer et al. 2006), Pan
troglodytes verus (Western chimpanzees) (Fischer et al. 2006), Pan pansicus (bonobos) (Fischer
et al. 2006), Gorilla gorilla gorilla (Western lowland gorilla) (Thalmann et al. 2007), Pongo
pygmaeus pygmaeus (Bornean orangutan) (Fischer et al. 2006), Pongo abelii (Sumatran
25
orangutans) (Fischer et al. 2006), Symphalangus syndactylus (Siamang) (Chan et al. 2013),
Hylobates klossii (Kloss’s gibbon) (Chan et al. 2013), Hylobates pileatus (Pileated gibbon)
(Chan et al. 2013) and Hylobates moloch (Javan gibbon) (Chan et al. 2013).
To estimate Ne from π and θW, the following standard equation was used:
Ne = π/(4µ) or Ne = θW /(4µ) which is based on θ = 4Neµ. The mutation rate (µ) was derived from
the average pairwise differences (π) between two species calculated from sequence alignments
using the program SITES. The average pairwise differences between species (π) were then
divided by the generations that have passed between the divergence of the two species. To
calculate the number of generations that have passed between two species, the estimated
divergence time was multiplied by two, to account for evolution of both lineages, and then
multiplied by the average generation time of the two species. Generation times were collected
from Elango et al. 2006 and Whittaker 2005 (Homo sapiens 20 years) (Pan 15 years) (Gorilla 11
years) (Pongo 16 years) (Hylobatids 9 years). Divergence times were from Steiper and Young
2006 and Israfil et al. 2010 (Homo-Pan 6.6 mya) (Pan-Gorilla 8.6 mya) (Gorilla-Pongo 18.3
mya) (Pongo-Symphalangus 22 mya) (Pongo-Hylobates 22 mya).
26
TABLE 3.1
Autosomal Loci Hominoids
N
# of Base Pairs (per indv.)
Base Pairs (per region)
# Loci
27
Fischer et al. 2006
Homo sapiens
45
16,001
800
19
Fischer et al. 2006
Pan paniscus
9
22,401
850
26
Fischer et al. 2006
Pan troglodytes troglodytes
10
22,401
850
26
Fischer et al. 2006
Pan troglodytes verus
10
22,401
850
26
Fischer et al. 2006
Pan troglodytes schweinfurthii
10
22,401
850
26
Fischer et al. 2006
Gorilla gorilla gorilla
15
14,017
850
16
Fischer et al. 2006
Pongo pygmaeus
10
16,001
850
19
Fischer et al. 2006
Pongo abelii
6
16,001
850
19
Chan et al. 2013
Symphalangus syndactylus
12
11,501
821
14
Chan et al. 2013
Hylobates klossii
2
11,501
821
14
Chan et al. 2013
Hylobates pileatus
8
11,501
821
14
Chan et al. 2013
Hylobates moloch
8
11,501
821
14
Zichello 2014
Hylobates moloch
13
7,081
545
13
II. GENETIC DATA, METHODS
PCR - Hylobates moloch
DNA was extracted by Andayani et al. (2001) from whole blood samples. To increase
stock DNA volume for this analysis, 1 µL of purified DNA from each of the 13 samples was
amplified using the GenomiPhi V2 DNA Amplification kit (GE Healthcare, Buckinghamshire,
UK). Polymerase chain reactions (PCR) and bacterial cloning were carried out to amplify
specific regions and confirm heterozygotes. PCR was carried out using primers designed
specifically for this project. The new primers were based on sequence similarity across an
alignment of Homo sapiens, Pan troglodytes, Gorilla gorilla and Nomascus leucogenys. The
sequences from the Northern White-cheeked gibbon (Nomascus leucogenus) were obtained from
the Ensembl Genome browser (Flicek et al. 2012), all other species were obtained from
GenBank based on studies published by Yu et al. (2002; 2003; 2004). Primers were designed to
match the regions flanking the target regions. The following regions were amplified in H.moloch,
names consistent with loci amplified in other apes (Table 3.2): NT787, NT812, NT813, NT864,
NT953, NT1386, NT1469, NT1636, NT2019, NT2064, NT2352, NT2563, NT2986.
Amplification was done in 25 µL reactions using Promega 5 PRIME Kits, with 10x PCR
extender buffer with Magnesium, 200 µM of dNTPs , 0.5 µM of each forward and reverse
primers (Fisher Oligos), 1.25 U PCR extender polymerase mix (5 PRIME), and approximately
100 ng DNA template, at the following conditions: 95°C for 2 minutes, then 35 cycles of 94°C
for 20 seconds, 50-60°C for 20 seconds, and 72°C for 40 seconds. Primer sequences and
annealing temperatures are listed in Table 3.2.
PCR products were purified using forensic grade Microcon DNA Fast Flow (Merck
Millipore Ltd., Carrigtwohill, Ireland). Purified DNA samples were sent to Genewiz (Plainfield,
28
NJ) for sequencing in both forward and reverse directions to ensure complete coverage and to
confirm SNPs and heterozygotes.
TABLE 3.2
Primer Sequences
Region
Forward Primer Sequence
Reverse Primer Sequence
Temperature (Cº)
NT787
F1 5’ ATGCTGGGACAGGCCTATTA 3’
R2 5’ CTCCTGGACTCAAGCAATCC 3’
58.8
NT812
F1 5’ CATCCGCTTTGCTGTGCTGCAC 3’
R2 5’ TTTAATGGCACTCTGGTCTG 3’
57.9
NT813
F1 5’ GTTCCTGGTGTTTGTTCTATG 3’
R2 5’ TTGGATGCTGGTAATTGGCT 3’
58.8
NT864
F1 5’ GCCACAGAAAGGTCTTAGCT 3’
R2 5’ AAAGATGCATAAATAGAGT 3’
51
NT953
F2 5’ AGATGTCCACTCTCGCAGGG 3’
R1 5’ AGCTAACTAGGAGTCGGTAG 3’
55.4
NT1386
F1 5’ GAATAGACTTAGGGGAGTAA 3’
R1 5’ GAGAAAACCAAACCTCAAGAAG 3’
52.8
NT1469
F1 5’ CACAAAGAAGTGGTTGGGAAA 3’
R2 5’ TGTGTCCTCTTATTTCTCCCA 3’
57
NT1636
F2 5’ AGCACAGCTCTCTTTCTTGG 3’
R1 5’ AGCTGAGGATTTGGAGGAAG 3’
58.8
NT2019
F1 5’ CCACCAAAAAGTGAGTTCCAA 3’
R1 5’ TTAATGCAGCCCCTCTCAAT 3’
58.8
NT2064
F2 5’ CAGACTAACAGTATGAGGAAG 3’
R2 5’ GTATCTTGTTGGCTTGTGGCT 3’
58
NT2352
F2 5’ CAATTATTCACCCCATGCTCA 3’
R2 5’ GCTTGCTTGCTTGGTTGATTC 3’
57.9
NT2563
F1 5’ ATGGCTATATTCTCAACTTA 3’
R1 5’ AGCAGCAAATAGTAGGAGTTA 3’
56.7
NT2986
5’ GAATCTGGTGAAATTAACCT 3’
R2 5’ GAAGGACCATAATTCCAAA 3’
53
Sequence Alignment- H.moloch
Contigs from forward and reverse sequences were aligned for each region in the program
MacVector. The electropherograms were systematically checked by eye for the presence of
ambiguous bases and heterozygotes. If heterozygotes were found, the region was sent for resequencing for confirmation. In several cases, sequencing primers were not able to accurately
capture repeat regions and some heterozygotes. These regions were flagged and later cloned into
a bacterial vector to isolate single-stranded pieces and confirm the presence of heterozygotes.
From forward and reverse sequences, consensus sequences were exported from the contig
alignment and ambiguity codes were manually input to indicate the location of heterozygotes.
29
For each heterozygote position, the specific bases and locations were recorded. From this
information, files were built for the program PHASE. This software estimates the most probable
haplotypes given a set of SNPs and their chromosomal locations. The haplotype output data from
PHASE were then formatted for the program SITES (Hey and Wakeley 1997), which estimates
population parameters such as π, θW.
Bacterial Cloning - H.moloch
Certain loci were difficult to sequence because of the presence of repeat regions. For
these loci, we cloned the PCR products into a bacterial vector. We also cloned certain regions to
confirm heterozygotes that we observed examining the electropherogram peaks obtained from
sequencing the PCR products. We cloned the PCR product directly into competent E. coli using
the TOPO TA Cloning Kit for Sequencing (Invitrogen). We grew the bacterial colonies
overnight at 37°C on plates treated with Kanamycin antibiotic as a selective agent. We then
picked 10 colonies per sample and performed a clone test PCR using M13F-20
(5’ GTAAAACGACGGCCAG 3’) and M13R (5’ CAGGAAACAGCTATGAC 3’) to determine
whether the molecular cloning was successful. Then, the clones containing the insert DNA were
grown in separate tubes of 3 mL LB medium with 50 µg/mL kanamycin agitated at 37°C
overnight, to yield more clones of bacteria. On the following day, 1.5 mL of bacterial culture
were purified using the FastPlasmid Mini Kit (5 PRIME) to be sent out for sequencing. This
cloning procedure was done twice for each individual sample to confirm that both alleles were
obtained and no mutations were introduced via cloning.
30
Sequence Alignment – publicly available Hominoid data
All data were downloaded from NCBI and aligned using Clustal W. For H.moloch, newly
collected data were included, although Chan et al. 2013 also sequenced H.moloch. Nucleotide
diversity was calculated on each of these two H.moloch datasets, results were comparable.
Publicly available sequence data were duplicated to represent two alleles and then entered into
PHASE for haplotype estimation. Calculating Nucleotide Diversity (π, θW)
Genetic diversity was estimated within Homo sapiens, Pan troglodytes troglodytes,
P. t. verus, P.t. schweinfurthii, Pan paniscus, Gorilla gorilla gorilla, Pongo pygmaeus, P. abelii,
Symphalangus syndactylus, Hylobates moloch, Hylobates klossii, Hylobates pileatus. Genetic
diversity (π), an estimator of θw (theta), the neutral population parameter, was calculated within
each species or subspecies, for nuclear autosomal datasets on a per locus basis using the software
SITES (Hey and Wakeley 1997). Genetic variation per locus was measured (π), which calculates
the pairwise nucleotide diversity between several individuals within a species. An average of all
π values from each locus was calculated. From this average π value, effective population size
(Ne) was calculated. Ne was calculated per locus, given the standard equation Ne = π/(4µ) for
nuclear DNA. µ is the mutation rate, which was derived from the average pairwise differences
between two species, divided by the generations that have passed between the estimated
divergence of the two species. The average Ne was taken across all loci within a dataset, perspecies (Table 4.2). As a means of correcting for differences in sample sizes between species, θw
was also calculated.
31
III. MORPHOLOGICAL DATA, MATERIALS
Morphological data used for this analysis included 34 Type I, II and III homologous x, y,
z cranial landmarks capturing shape differences in morphology of the entire cranium. Landmark
definitions are provided in Table 3.3. Landmarks were analyzed in three sets: 34 landmarks for
the entire cranium, 12 landmarks for the cranial vault and 22 landmarks for the face. New
landmark data were collected from the American Museum of Natural History, NY, the National
Natural History Museum, London, Smithsonian National Museum of Natural History, DC and
the Naturalis Biodiversity Center, Leiden for the following Hylobatids: Symphalangus
syndactylus syndactylus, Symphalangus syndactylus continentis, Hylobates moloch, Hylobates
pileatus, Hylobates klossii, Hylobates lar vestitus, Hylobates lar lar, Hylobates lar enteloides,
Hylobates lar concolor, Hylobates lar albimanus. For the purpose of the final analysis, certain
taxa were excluded because of the lack of a reasonably congruent sample of genetic and
morphological data. All Hylobates lar were excluded from the final analysis because it could not
be confirmed from what subspecies the genetic data originated.
Previously collected 3D landmark data (McNulty 2003) were included for the following
taxa: Homo sapiens, Pan paniscus, Pan troglodytes troglodytes, Pan troglodytes verus, Pan
troglodytes scweinfurthii, Gorilla gorilla gorilla, Pongo pygmaeus, Pongo abelii (see Appendix).
The McNulty dataset was edited to include only 34 homologous landmarks that were also
collected on the Hylobatid crania.
Landmark sets were derived from well-established developmental modules in the
mammalian cranium. The ontogenetic trajectory of the skull is a multifactorial process involving
the coordination of genes, cells, bones, organs and environment. Developmental integration
causes ontogenetic and evolutionary change to occur in localized groups, which share a common
32
developmental pathway, rather than each region changing independently (Olson and Miller 1958;
Cheverud 1982; Pigliucci and Preston 2004; Klingenberg 2005). The mammalian cranium is
often divided into three partially independent developmental modules: the basicranium, the
neurocranium and the face (de Beer 1937; Moss & Young 1960; Enlow 1968; Leamy et al.
1999). There is evidence that within-module evolutionary changes are constrained due to this
integration (Goswami and Polly 2010). Each of these modules develops at a different rate. First
the basicranium reaches adult morphology, followed by the neurocranium and then later the face
(Enlow 1968; Bastir & Rosas 2004; Bastir 2006). The timing of development is important when
determining the relative contribution of environment to adult form. For example, the
basicranium, because it develops first, is less affected by bone remodeling due to environmental
stress. Therefore, it is plausible that this contributes to the pattern that the basicranium and the
temporal bone reflect phylogeny and population history more closely than facial morphology
(Hall 1978; Thorogood 1993; Lockwood et al. 2004; Harvati and Weaver 2006; Smith 2009).
33
Figure 3.2 Landmarks, solid circles are neurocranial set, open circles are facial set. 34
TABLE 3.3
Landmark Descriptions Landmark
Description
Facial/Cranial
Bregma
junction of coronal and sagittal sutures
C
2
Post toral sulcus
point of maximum concavity behind supraorbital torus
in the midline
C
3
Glabella
most anterior midline point on the frontal bone
C
4, 5
Midtorus superior
most superior point on the supraorbital torus
C
x
6, 7
Midtorus inferior
midline point on the superior margin of the orbit
C
x
8
Inion
point at which the superior nuchal lines merge in
sagittal plane
C
9
Opisthion
midline point at the posterior margin of the foramen
magnum
C
10
Basion
midline anterior margin of the foramen magnum
C
11, 12
Porion
central point on the upper margin of the external
auditory meatus
C
x
13, 14
Zygoorbitale
junction of zygo-maxillary suture and the orbital rim
F
x
15
Anterior Attachment of
Nasal septum
most anterior insertion of cartilaginous nasal septum
F
16, 17
Jugale
deepest point in the notch between temporal and
frontal processes of the zygomatic bone
F
x
18, 19
Infraorbital foramen
superior margin of the largest infraorbital foramen
F
x
20
Rhinion
most inferior point of inter-nasal suture
F
21, 22
Inferior premaxillomaxillary suture
most inferior point on premaxillo-maxillary suture
F
23
Alveolare
most inferior midline point on the maxilla
F
24
Staphylion
most posterior midline point on the hard palate
F
25, 26
Distal Molar
most distal molar point on M3 projected laterally to the
alveolar margin
F
27
Midline Anterior Palatine
junction of palato-maxillary and inter-palatine sutures
F
28
Incisivion
most posterior point of (oral) incisive foramen
F
29, 30
Pre-molar-molar contact
P/M contact projected laterally to the alveolar margin
F
x
31, 32
Canine premolar contact
C/P contact projected laterally to the alveolar margin
F
x
33, 34
Lingual canine margin
most lingual point on the canine alveolar margin
F
x
1
35
Bilateral
x
x
IV. MORPHOLOGICAL DATA, METHODS
Three-dimensional landmark data were collected using a Microscibe digitizer. For each
cranium, data were collected in two orientations: dorsal and ventral. To combine these two
orientations into a single 3D-landmark shape, a program called DVLR (Dorsal, Ventral, Left,
Right) (http://pages.nycep.org/nmg/programs.html) was used. This software uses landmarks that
were collected in both orientations as reference points to merge the two orientations. The merged
data were then exported in Morphologika format for the next step. At this step, newly collected
data and previously collected data, were all grouped together in one file. Using Morphologika
(O’Higgins and Jones 1998) (version 2.5), all landmark data were subjected to a generalized
Procrustes Analysis (GPA) together in the same shape space. The GPA scales and rotates all
specimens around a centroid. Differences in size and orientation are eliminated during this step,
so that only differences in shape remain between specimens. Following the GPA, data were
exported to SAS (version 9.2) statistics software package. A routine was written in SAS to iterate
though the dataset for each taxon and calculate pairwise Procrustes distance between specimens
(McNulty 2003). The Procrustes distance measures the shortest distance between all landmarks
after superimposition; this captures overall shape differences between two specimens. Two
individuals from the same species were sampled and the Procrustes distance between them was
calculated. This process was then repeated 10,000 times within every taxon, each time selecting
two individuals at random from the sample. From this, a distribution of average pairwise
Procrustes values was created and the average pairwise Procrustes distance was extracted for
each taxon. Analyses were run within each taxon only. First males and females were included
together, then males alone, then females alone. The analysis was run on all 34 landmarks
together, then on 22 and 12-landmark subsets, which correspond to developmentally independent
36
craniofacial modules. The 12-landmark set captures the shape of the cranial vault, while the 22landmark set is limited to the face only.
To determine if sample size significantly influenced the mean Procrustes distance within
each taxon, several tests were performed. The largest sample size was 75 individuals for Pan
troglodytes troglodytes. From these 75 individuals, random subsets were taken of 50, 20, 10, 5
and 2 individuals. For each of these subsets, average pairwise Procrustes distances were
calculated using the same 10,000 replicates approach described above. See Table 3.4 for results.
No significant difference was found between the different sample sizes. These tests confirmed
that small sample sizes could effectively be used to capture intraspecific cranial shape diversity
that reflect a larger species-wise trend.
In order to assess landmark congruence between newly collected data with previously
collected data, identical landmarks were collected on the same 12 Symphalangus syndactylus
specimens from AMNH by McNulty and Zichello. These two datasets were grouped and
compared using principle components analysis. An average pairwise Procrustes distance was
calculated for each group of samples from both researchers. From this test and visual
confirmation of landmark overlap in Morpologika, five landmarks that were not congruent
between observers were removed to arrive at the final 34 landmarks. After the removal of these 5
landmarks, pairwise Procrustes distance values between both datasets overlapped.
37
TABLE 3.4
Sample Size Test
Pan troglodytes troglodytes
N=2
N=5
N = 10
N = 20
N = 50
N = 75
0.00700
0.00660
0.00654
0.00651
0.00643
0.00641
0.00650
0.00658
0.00652
0.00646
0.00643
0.00641
0.00650
0.00652
0.00650
0.00643
0.00642
0.00641
0.00640
0.00651
0.00648
0.00642
0.00641
0.00641
0.00639
0.00650
0.00648
0.00642
0.00641
0.00641
0.00633
0.00639
0.00646
0.00641
0.00641
0.00641
0.00623
0.00637
0.00645
0.00638
0.00640
0.00641
0.00623
0.00631
0.00637
0.00636
0.00640
0.00641
0.00621
0.00626
0.00636
0.00636
0.00640
0.00641
0.00605
0.00620
0.00634
0.00635
0.00639
0.00641
RANGE
0.00099
0.00040
0.00019
0.00016
0.00004
0.00000
MEAN
0.00640
0.00642
0.00645
0.00641
0.00641
0.00641
V. MORPHOLOGY AND GENETICS
Finally, to determine the strength of the relationship between genetic and morphological
diversity, the average Ne from each taxon was regressed against the matched average pairwise
Procrustes distance from 3D cranial landmark data. A total of 36 regressions were performed.
This was done for three different landmark sets: 34, 22 and 12. For each landmark set, there is a
combined sex regression and then males and females separately. Ordinary least squares
regressions were performed. Each regression was then phylogentically corrected (PGLS) to
account for patterns potentially skewed by close evolutionary relationships between species. All
results showed that the relationship between Ne and PPD was not a result of close evolutionary
38
relationships between the taxa sampled here, with lambda values at or close to 0. A tree file of all
species used in this analysis was generated from the website 10ktrees.fas.harvards.edu. This file
was loaded into “R” and using the packages (caper) and (ape), phylogenetically corrected least
squares regressions (PGLS) were performed. The results for PGLS regressions versus OLS
regressions were overlapped on the same chart, PGLS regression lines shown in blue, OLS in
black. See results in Figures 4.6-4.23. Sample sizes for morphological data in each taxon varied,
as did sample sizes for genetic data. Therefore, error exists in both variables. In order to mitigate
sample size differences in the morphological values, bootstrapping was performed to arrive at an
average PPD value after 10,000 pairwise replicates. This error could be addressed in future work
through adjusting the parameters of the regression analysis to account for differentially weighted
data points.
39
CHAPTER 4: RESULTS and DISCUSSION
Average Pairwise Procrustes Distance Results
Pongo abelii shows the highest average pairwise Procrustes distance (PPD) of all 12 taxa
in this analysis, both in the combined sex sample and in males and females separately. This
species also shows the most shape variation across all landmark sets, the face being the most
variable region (Table 4.1). Conversely, Pan paniscus consistently falls among the lowest PPD
values of the great apes, for combined sex and in males and females separately. PPD values for
chimpanzee subspecies consistently show that Pan troglodytes troglodytes (Central African
chimpanzees) are more cranially variable than either Pan troglodytes verus (West African
chimpanzees) or Pan troglodytes schweinfurthii (East African chimpanzees). P.t.verus and
P.t.schweinfurthii PPD values alternate as to which value is greater depending on the landmark
set and sex analyzed. For example, P.t. verus is less variable than P.t. schweinfurthii in both the
face and in the cranial vault, but in the whole 34-landmark dataset, P.t. schweinfurthii is slightly
more variable than P.t.verus. Although because these values are averages generated from 10,000
replicates of pairwise comparison, this degree of difference is not necessarily biologically
meaningful.
In all landmark sets, both Pongo and Gorilla have higher PPD values than all other taxa.
Also in all taxa, the face shows higher average PPD than the cranial vault.
40
TABLE 4.1 Average Pairwise Procrustes Distances Homo sapiens
(34) M+ F (34) F (34) M (12) M + F (12) F (12) M (22) M+F (22) F
(22) M 0.00609
0.00629
0.00598
0.01420
0.01511
0.01344
0.00404
0.00399
0.00414
0.00986
0.01033
0.00794
0.00561
0.00506
0.00625
0.01041
0.01000
0.01050
0.00558
0.00489
0.00639
0.01024
0.01002
0.00974
0.00503
0.00299
0.00590
0.00988
0.00655
0.01051
0.00453
0.00468
0.00458
0.00913
0.00893
0.00870
0.00896
0.00615
0.00842
0.01397
0.01103
0.01347
0.01488
0.01110
0.01577
0.01868
0.01737
0.01569
0.01120
0.00909
0.01192
0.01139
0.00854
0.01308
0.00721
0.00661
0.00803
0.00884
0.00997
0.00794
0.00779
0.00698
0.00882
0.01219
0.01190
0.01198
0.00903
0.00898
0.00856
0.01677
0.01786
0.01635
0.00347
0.00400
0.00219
0.01061
0.00955
0.01071
0.01068
0.01243
0.00945
N
38
18
20
Pan paniscus
0.00585
0.00602
0.00501
41
21
17
0.00678
0.00624
0.00705
107
63
44
0.00640
0.00594
0.00678
74
48
26
0.00625
0.00363
0.00714
11
3
8
0.00661
0.00690
0.00610
22
12
10
0.01035
0.00724
0.00935
70
28
42
0.01720
0.01292
0.01598
18
8
10
0.00912
0.00671
0.00921
35
20
15
0.00504
0.00512
0.00506
21
10
11
0.00751
0.00590
0.00850
15
6
9
0.00979
0.00935
0.00962
42
20
20
0.00513
0.00487
0.00474
12
6
6
Pan troglodytes
Pan troglodytes troglodytes
Pan troglodytes schweinfurthii
Pan troglodytes verus
41
Gorilla gorilla gorilla
Pongo abelii
Pongo pygmaeus
Hylobates klossii
Hylobates moloch
Symphalangus syndactylus
Hylobates pileatus
Figure 4.1 42
Figure 4.2 43
Figure 4.3
44
TABLE 4.2
Centroid Size Variance
45
(34) M+F
(34) F
(34) M
Homo sapiens
949.5180
672.0265
768.2663
Pan paniscus
488.6118
376.7499
512.3594
Pan troglodytes troglodytes
1895.9512
999.5480
1663.3560
Pan troglodytes verus
842.2246
715.3696
376.0852
Pan troglodytes schweinfurthii
2022.8615
1699.4152
1348.5840
Gorilla gorilla gorilla
11434.3702
1004.4841
5011.1607
Pongo pygmaeus
8505.5467
1613.5679
3965.9139
Pongo abelii
10942.2075
1175.1569
4874.7495
Symphalangus syndactylus
191.3344
100.8263
220.0267
Hylobates klossii
14.2161
12.4285
17.0825
Hylobates pileatus
17.5732
2.0029
26.3392
Hylobates moloch
37.4953
40.1864
29.5092
Nucleotide Diversity, θ W and Ne estimates
Nucleotide diversity was estimated using both π and θW. These two summary statistics
estimate diversity using different models. π estimates diversity using a pairwise approach,
averaging the number of nucleotide differences between any two sequences in the sample.
Watterson’s theta estimates nucleotide diversity by calculating the number of segregating sites.
Under a neutral model we expect π and θw to be equivalent. Estimating Ne from π, and also from
θ, had only a small affect on the final results. For this project, π may be a more appropriate value
to compare to average pairwise Procrustes distance because of the pairwise nature of both
estimates. π values ranged from 0.06 for Hylobates pileatus to 0.35 for Pongo abelii. Theta
values ranged from 0.07 for Hylobates pileatus to 0.32 for Pongo abelii, see Table 4.2. The
importance of including θ values, in addition to π, is that they correct for sample size variation
between taxa. Although the same loci were used to generate these estimates across all species,
the sample sizes differed, ranging from 6 Pongo abelii individuals to 45 humans, see Table 3.1.
The highest Ne values are found in Pongo abelii, this is consistent with results from several other
studies that estimate this parameter using a variety of different models.
46
TABLE 4.3
Nucleotide Diversity
π (%)
θ
Ne (from π)
Ne (from θ)
Homo sapiens
0.12
0.14
23,770
27,826
Pan paniscus
0.10
0.12
21,046
23,763
Pan troglodytes troglodytes
0.20
0.24
39,616
45,361
Pan troglodytes verus
0.08
0.09
15,846
17,446
Pan troglodytes schweinfurthii
0.16
0.17
31,693
33,398
Gorilla gorilla gorilla
0.15
0.14
42,157
37,452
Pongo pygmaeus
0.27
0.27
78,429
67,543
Pongo abelii
0.35
0.32
101,667
88,968
Symphalangus syndactylus
0.17
0.19
49,246
55,039
Hylobates klossii
0.08
0.08
23,174
23,174
Hylobates pileatus
0.06
0.07
17,380
20,277
Hylobates moloch
0.17
0.16
49,246
46,349
Hylobates moloch (JMZ)
0.18
0.17
52,142
49,246
47
Figure 4.4
π and θw (%)#
0.35
0.3
0.25
48
0.2
0.15
0.1
0.05
0
π"""
θw"
Figure 4.5
Ne from π and θw
120,000
100,000
80,000
60,000
49
40,000
20,000
0
Ne (from π)
Ne (from θw)
Ne/PPD Regression Results
A total of 36 regressions were performed for the final analyses. Eighteen used a linear
model. Another 18 used a phylogenetically corrected model to account for the possibility that a
statistically significant relationship between these two estimates was the result of close
evolutionary relationships between all taxa. Average pairwise Procrustes distances (PPD) were
regressed against Ne estimates, derived from both π and θw (Table 4.3). Results from Ne derived
from π versus Ne derived from theta were comparable (Table 4.2). Males and females were
combined in one sample for each taxon, and males and females were also separated to address
the confounding issue of sexual dimorphism. All regressions were highly statistically significant,
with the exception of the female-only facial dataset and the female-only whole cranium dataset.
Ne against entire cranium, 34-landmark set
Regressions from Ne against the 34-landmark set of the whole cranium were highly
significant for all analyses, with the exception of the female-only dataset. For the combined sex
sample, using Ne from π, the adjusted R2 was 0.59 with a p-value of 0.0006 for the
phylogenetically corrected model. For the linear model, 34-landmark dataset, Ne from π, adjusted
R2 was 0.59 with a p-value of 0.002. No phylogenetic structure was detected in this relationship,
or in any other regressions performed for this project. For males only, Ne from π, linear model,
R2 was 0.71 and the p-value was 0.0002. By contrast, for females only, Ne from π, linear model,
R2 was 0.15 and p-value was 0.09. This result for the 34-landmark dataset in females was likely
driven by the female-only facial dataset, because the female only cranial vault dataset was
statistically significant.
50
TABLE 4.4
Regression Results
PPD/Ne
(34) Males + Females OLS
R
2
p-value
(34) Males + Females PGLS
R
2
p-value
(34) Females OLS
R
2
p-value
(34) Females PGLS
R
2
p-value
(34) Males OLS
R
2
p-value
(34) Males PGLS
R
2
p-value
(22) Males + Females OLS
R
2
p-value
(22) Males + Females PGLS
R
2
p-value
(22) Females OLS
R
2
p-value
(22) Females PGLS
R
2
p-value
(22) Males OLS
R
2
p-value
(22) Males PGLS
R
2
p-value
(12) Males + Females OLS
R
2
p-value
(12) Males + Females PGLS
R
2
p-value
(12) Females OLS
R
2
p-value
(12) Females PGLS
R
2
p-value
(12) Males OLS
R
2
p-value
(12) Males PGLS
R
2
p-value
51
Ne (from π)
Ne (from θw)
0.5926
0.5814
0.002065
0.002381
0.5926
0.5814
0.0006059
0.0007161
0.1594 *
0.1879 *
0.1095 *
0.08912 *
0.1594 *
0.1879 *
0.09038 *
0.06861 *
0.7195
0.7206
0.000299
0.000293
0.7195
0.7206
6.66E-05
6.51E-05
0.4079
0.4696
0.01507
0.008327
0.4079
0.4696
0.006771
0.003234
0.1158 *
0.1909 *
0.1493 *
0.08716 *
0.1158 *
0.1909 *
0.137 *
0.06659 *
0.4178
0.5369
0.01375
0.004052
0.4178
0.5369
0.006033
0.001348
0.8725
0.8147
5.41E-06
3.60E-05
0.8725
0.8147
8.80E-07
6.58E-06
0.6847
0.6788
0.0005467
0.0006012
0.6699
0.6652
0.0001714
0.0001862
0.9088
0.8394
9.99E-07
1.74E-05
0.9053
0.8394
1.85E-07
3.03E-06
Ne against face, 22-landmark set
The facial dataset also showed a significant relationship between Ne and PPD for all
except the female-only dataset. The relationship between Ne and the facial landmarks is the
weakest of the three landmark sets in this analysis. For the combined sex sample, Ne from π,
linear model, R2 was 0.40 and p-value was 0.01. Males only, Ne from π, linear model, R2 was
0.41 and p-value was 0.01. Females only, Ne from π, linear model, R2 was 0.11 and p-value was
0.14. The face exhibits more shape variation and therefore a weaker relationship with Ne . This
fits with predictions from modern humans and from other comparative cranial work in primates.
The face is the final cranial region to reach adult morphology during development and therefore
more environmentally influenced than the cranial vault or base.
Ne against cranial vault, 12-landmark set
The cranial vault showed the strongest relationship between Ne and PPD. This fits with
results from analyses in humans that have identified the vault as a less plastic region than the
face, and therefore more tightly reflective of genetic and demographic patterns. For the
combined sex sample, Ne from π, linear model, R2 was 0.87 and p-value was 5.411e-06. Males
only, Ne from π, linear model, R2 was 0.90 and p-value was 9.992e-07. Females only, Ne from π,
linear model, R2 was 0.68 and p-value was 0.0005.
The female cranial vault results are significant, but the female face and whole cranium
are not. An overall trend in this dataset and all others is that females show a weaker relationship
with Ne than males. It is important to note that the 22-landmark facial dataset and the
12-landmark cranial vault datasets do not overlap. Therefore, the lack of correlation between Ne
and whole female crania is likely driven by the inclusion of the facial dataset. In particular, the
52
female Pongo pygmaeus facial PPD is lower than the male sample. This data point shifts
significantly between the combined sex sample and the female only sample and its relationship
relative to other species. This shift influences the overall strength of the correlation. This lack of
facial diversity in Pongo pygmaeus females, relative to males, may represent stabilizing selection
acting to constrain diversity.
Ne against cranial size variance, 34-landmark set
Cranial variation within a species is only partially understood through looking
exclusively at shape. Size diversity is also an important element, on its own, and as a potential
driver of shape variation. Results here show that a statistically significant relationship exists
between combined male and female cranial size variance and Ne , and between male size
variance and Ne. These results echo the pattern seen with shape variance and suggest that either
size variance is also influenced by the underlying genetic variance of the species, or that shape
variance is a direct reflection of size differences between individuals.
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
DISCUSSION
Taken together, the high R2 values from the regressions of Ne and PPD suggest that the
population history can serve as an explanation for the genetic variation and cranial variation
within each extant hominoid taxon. Neutral genetic loci and cranial morphology are separate
systems that are both potentially influenced by population history. This project is the first
attempt to contextualize cranial shape diversity in extant hominoids within a population genetics
framework, and quantify this trend. These results have implications for hominin fossil species
delimitation and potentially even more broadly, for patterns of vertebrate evolution.
Hypothesis I: The population history can explain the magnitude of intraspecific cranial
diversity across a range of hominoid taxa.
The high R2 values from Ne and PPD suggest that the population history is a strong
determinant in the magnitude of skeletal variation within hominoid species. This is not altogether
surprising given the patterns we see in humans, however it points to an important evolutionary
trend that bears on many issues including; species delimitation, phenotypic neutrality and
identifying adaptive skeletal evolution. For example, given this pattern, it is important to correct
for neutral population history when estimating variation within species, even in studies that are
primarily focused on the evolution of skeletal morphology alone.
Hypothesis II: Within each taxon, both the neurocranium and the face will show patterns
consistent with drift, not natural selection. Previous work has shown that these two cranial
regions are developmentally separate and therefore potentially subject to the action of
evolutionary forces in different ways (Leamy et al. 1999).
75
In all taxa, the face shows higher average PPD than the cranial vault. Wood and
Lieberman (2001) provide evidence that craniofacial traits subjected to low strains tend to show
less variation than those subjected to higher strains. Therefore the face would show more
variation than either the basicranium or the cranial vault. This result also fits with expectations
gleaned from work on modern humans, where the face shows a stronger response to climatic
selection and bone remodeling because of masticatory pressures (Roseman 2004). The data from
this dissertation support the well-studied pattern that intraspecific variation in facial shape is
inflated relative to other parts of the skull, such as the cranial vault. Shape differences within
taxa are uniformly higher in the facial dataset than in the cranial vault dataset. In certain taxa,
such as Pan paniscus, the face is more than twice as variable as the vault (Table 4.1). Increased
skeletal plasticity in this region may be due to mechanical loading from mastication. The face is
the last region of the cranium to reach adult form, therefore it is potentially subject to
environmental influence for a longer period of time that either the cranial vault or the cranial
base.
Hypothesis III: Cranial shape variation within each taxon is driven by sexual dimorphism.
Therefore when male and female cranial data are analyzed separately the average pairwise
Procrustes distance will shift significantly from the value for the combined sex dataset.
When males and females are analyzed separately they still show a positive correlation in
relation to Ne. This refutes the idea that it is only sexual dimorphism inflating the variation
within species, especially in Pongo and Gorilla. For example, male Sumatran orangutans, like
orangutans as a whole, are the most cranially variable male hominoids compared to all others in
this sample. In all landmark sets, both Pongo and Gorilla have higher PPD values than all other
76
taxa. Relative to all other taxa, PPD values are highest for Pongo and Gorilla in both the maleonly and female-only datasets. This is a key point because it demonstrates that sexual
dimorphism, while clearly present in these species, does not alone account for the high degree of
cranial shape variation within these species. The process of Procrustes superimposition adjusts
for size related differences between samples by scaling all data to a centroid. However, even
given this size adjustment, shape differences that are the result of allometry may remain. This
may contribute to the result that males show more shape diversity than females overall especially
in highly dimorphic species. Gorilla females show less diversity than males. This pattern is also
evident in Pongo. Across primates, it has been demonstrated that males are more variable than
females in crania and post-cranial variation (Leutenegger and Cheverud 1982; 1985).
Specifically, adult male orangutan cranial variation may be driven by differential male
dominance hierarchies. Only resident dominant males achieve full body size and development of
strong cranial robusticity, while other males retain a subadult body form even though they are
dentally adult (Utami Atmoko and van Hoof 2004). The result is that male cranial features are
more variable than females (Leutenegger and Masterson 1989). In sexually monomorphic
species such as humans and hylobatids, there is no consistent pattern of males being more or less
variable than females.
If we take the aforementioned results to suggest that the signature of population history is
evident from intraspecific cranial shape variation in extant hominoids, then the central question
becomes: What demographic and ecological factors shaped the population histories of each
taxa and drove a parallel change in both molecular and skeletal diversity?
The explanation for the apportionment of human cranial, and some post-cranial, diversity
is that our emergence from Africa and subsequent migration across the world caused Homo
77
sapiens to experience a bottleneck, in addition to successive founder effects as populations
traversed new lands. The regions of the crania that are less environmentally influenced, such as
the cranial vault and the temporal bone, retain a signature of this population history with more
fidelity than the face. Populations that are in closer geographic proximity to one another, also
have more similar cranial forms. Cranial diversity is highest in sub-Saharan Africa, as is genetic
diversity.
West African chimpanzees and bonobos have Ne values lower than humans, while
Central, and Eastern chimpanzees have higher Ne values. Bonobos and common chimpanzees
were separated by the formation of the Congo River approximately 1.5-2 mya. This barrier
inhibited gene flow and restricted bonobos to a small geographic area south of the river.
Additionally, periodic contractions of forest cover in this region may have forced bonobos into a
population bottleneck (Hamilton 1981). Within Pan troglodytes, central chimpanzees (P.t.
troglodytes) contain the most genetic diversity, followed by eastern chimpanzees (P.t.
schweinfurthii). Western chimpanzees have the lowest levels of genetic diversity (P.t.verus).
This pattern is supported by several analyses using many independent loci (Noda et al. 2001;
Fischer et al. 2004; 2006; Prado-Martinez et al. 2013). Therefore, central chimpanzees are
hypothesized to be ancestral to both western and eastern subspecies. Data from Bayesian
population modeling in chimpanzees suggests that the eastern and western populations went
through a bottleneck just after their divergence and before expanding to their current range. In
contrast, there is evidence that central chimpanzees have had a recent range expansion, without
any evidence of a bottleneck (Wegmann and Excoffier 2010). Sumatran orangutans have been
shown to have three deeply structured distinct genetic clusters (Nater et al. 2013). This indicates
78
long-term separation of these groups and offers an explanation for the high diversity of the
species as a whole.
Clarifying the historical population history of the living apes and estimating genetic
diversity has implications for conservation efforts. Therefore, many population genetic analyses
of the Hominidae have looked at neutral non-coding loci as indicators of how demographic
history has shaped intraspecific genetic diversity. Currently, there is a wealth of population
genetic data on all members of the Hominoidea, including whole genome data. Orangutans,
gorillas, gibbons and central and eastern chimpanzees are more genetically diverse than humans,
whereas western chimpanzees and bonobos have amounts of genetic diversity closer to humans
(Noda et al. 2001; Kaessmann and Pääbo 2002; Fischer et al. 2006; Prado-Martinez et al. 2013).
All of the living apes have drastically lower population census sizes than humans, but diversity
has been maintained in many of these species as a vestige of large ancestral population sizes or
population sub-structuring. The reverse is true in modern humans, where a recent population
expansion ~50,000 years ago has not resulted in an increase in genetic diversity because of the
rapid rate of population expansion over a short period of time (Amos and Hoffman 2010). From
this it is clear that population census size is not a direct indicator of diversity (Frankham 1995).
The effective population size (Ne) is a mathematical concept that estimates the theoretical
size a population would be were it an idealized statistical population. By doing so, Ne is
proportional to the amount of genetic drift that a population has experienced (Wright 1931). Ne
can be estimated using current levels of total genetic diversity and neutral mutation rates. Current
population census size is often discordant with Ne. This is partly because Ne is sensitive to
fluctuations in population size throughout history. For example, if a population experiences a
bottleneck, genetic diversity will be lost and Ne will decrease. Populations with low Ne estimates
79
have experienced stronger drift. Evaluating Ne together with historical biogeography provides
evidence toward reconstructing an evolutionary account for each living species. These data are
especially important for revealing the evolutionary histories of species with particularly
depauparate fossil records, such as chimpanzees, bonobos and gibbons.
Of the great apes, orangutans are the most genetically diverse, with the Sumatran
subspecies (Pongo pygmaeus abelii) more variable than Bornean (Pongo pygmaeus pygmaeus) (Zhi et al. 1996; Muir et al. 2000; Warren et al. 2001; Zhang and Ryder 2001; Steiper 2006; Jalil
et al. 2008; Hobolth et al. 2011). The current population census size of P. p. pygmaeus is
~50,000 individuals, and only ~7,000 for P. p. abelii. Yet, estimates of the effective population
sizes (Ne) are inconsistent with these absolute census sizes. A recent whole genome analysis
estimated the Ne for P. p. pygmaeus at ~8,800, and ~37,700 for P. p. abelii (Locke et al. 2011)
Genetic diversity within western and eastern gorilla species is lower than orangutans, and closer
to estimates for Pan. The high Ne in Gorilla gorilla populations (~24,100) versus Gorilla
beringei (~13,600) may be a relict of ancestral population sub-structuring (Clifford et al. 2004).
If a population becomes discontinuous, groups may maintain separate reservoirs of diversity,
which is retained should the groups resume gene flow. During the last 2 million years in Africa,
glacial periods resulted in rain-forest fragmentation, which was then mitigated post glacially,
when forest patches expanded and rejoined (Anthony et al. 2007). Within the genus Pan, genetic
diversity is lowest in western chimpanzees (Pan troglodytes verus) and bonobos (Pan paniscus),
higher in eastern chimpanzees (P.t. schweinfurthii) and highest in central chimpanzees (P.t.
troglodytes) (Kaessmann et al. 1999; Chen and Li 2001; Stone et al. 2002; Fischer et al. 2004;
Becquet et al. 2007). Accordingly, Ne estimates from nuclear loci are highest in central
chimpanzees (~25,000), intermediate in eastern chimpanzees (~12,400) and lowest in western
80
(~8,750) chimpanzees and bonobos (~9,450) (Hey 2010).
Genetic diversity within hylobatids, although traditionally not as well studied as the great
apes, has been recently estimated using nuclear autosomal markers (Kim et al. 2011; Chan et al.
2013). Hylobatidae are the most species-rich family of the apes, with estimates between 13 and
16 species (Groves 2001). They are comprised of four genera; Nomascus, Hylobates, Hoolock
and Symphalangus. The phylogenetic relationships between genera and species are still in
question (Thinh et al. 2010; Israfil et al. 2011; Kim et al. 2011; Meyer et al. 2012; Chan et al.
2013; Wall et al. 2013). This is likely due to the rapid diversification of these taxa during the
beginning of the Pleistocene, making phylogenetic resolution elusive even in analyses using
many independent loci. Using the same loci as tests in other apes, the range of nucleotide
diversity (π) within hylobatid species spans a wide range from Hylobates pileatus at 0.06 to H.
mulleri at 0.44. The genus Hylobates is more genetically diverse than the genus Pongo, and
estimates for Symphalangus are close to eastern chimpanzees and gorillas. Effective population
sizes (Ne) for gibbons range from 37,500-117,500. Therefore, including Hylobatids in this study
was a central part of testing the relationship between cranial and genetic diversity because they
represent a uniquely broad range of genetic diversity among hominoids.
LINKING HOMINOID GENETICS with CRANIAL MORPHOLOGY
As genetic data on the population histories of the apes has accumulated, the results have
been referenced in studies of intraspecific cranial variation (Uchida 1998a; Uchida 1998b;
Schmittbuhl et al. 2007; Jabbour 2008). These analyses use genetically defined populations as a
guide to identify taxonomically informative craniodental characters for fossil analyses (Shea and
Coolidge 1988; Pilbrow 2006; Pilbrow 2010). However, cranial variation within extant hominoid
81
species has not been formally tested against genetic patterns for evidence of neutrality.
Intriguingly, it has recently been demonstrated that genetic and cranial distances between extant
hominoid species are relatively congruent (von Cramon-Taubadel and Smith 2011). According to
von Cramon-Taubadel and Smith (2011), the topology of a neighbor-joining tree built with
molecular distances matches that built with morphological distances. This analysis adds another
piece of evidence demonstrating that these two data types jointly reflect evolutionary history.
However, there are a few important points to glean from this work. One is that the similarity of
these phenetic trees does not rule out natural selection as a force shaping the differences in skull
shape between species. Natural selection, together with stochastic forces, may have acted to drive
the initial divergence in cranial form between species. Secondly, as the authors do point out, the
trees are not entirely congruent. Humans cluster with hylobatids, probably because of sexual
monomorphism in the crania and a relative lack of sub-nasal prognathism. Also, relative
congruence of both trees does not suggest that these two systems are evolving at the same rate.
Tree topology does not reflect time depth. The work of von Cramon-Taubadel and Smith (2011),
and the majority of work quantifying skeletal variation in extant hominoids, does so from the
perspective of elucidating variation in the hominin fossil record. Extant hominoids serve as one
of our only windows into the dynamics of our own evolutionary past. With the accumulation of
new fossil hominin material, the question of how much variation constitutes one fossil species is
becoming increasingly relevant (Lordkipanidze et al. 2013). Genetics offers additional evidence
as to how phenotype evolves in extant species, and this information is key in formulating
inferences for fossil species.
82
Given the intriguing result from the data here and the overwhelming strength of the
relationship between genetic and morphological diversity in living hominoids, it is relevant to
extrapolate these results to the hominin fossil record.
Application of this Research to Questions in Paleoanthropology
Evidence from the analyses here, together with quantitative genetics analyses done in
humans, supports the concept that population history is a key element in determining levels of
skeletal diversity. This idea has existed in the morphological literature as an implied yet
underexplored aspect of how skeletal diversity evolves (Kimbel and Martin 1993). For example,
craniodental variation in living primate species has been studied extensively to explore patterns
of geographic variation (Drenhaus 1975; Hull 1979; Thorington 1985; Cheverud and Moore
1990; Albrect and Miller 1993). It has long been understood that skeletal traits vary over
geographic space, in both living and extinct species. Accepting this pattern rests on three central
explanations, which are often not made explicit in the morphological literature. Morphology
within a species varies across space due to: 1) adaptation to local environments, 2) historical
patterns of dispersal and differentiation and 3) patterns of gene flow. Although it is a challenge
to tease apart these factors, patterns of gene flow and historical patterns of dispersal are
accessible through population genetic data. Therefore, in addition to providing a descriptive
analysis of geographic variation in morphology, one can more directly quantify the contribution
of factors that underlie geographic variation through the historical sensitivity of genetic data. The
empirical results presented here provide clear evidence of the evolutionary trend that aspects of
population history—be it population size or intrinsic level of genetic variation— can influence
83
skeletal morphology. How is this information directly applicable to clarifying species
boundaries in the fossil record?
Species are defined in the fossil record, in part, by comparing variation within fossil
assemblages to morphological variation found in extant species, where taxonomy is better
understood (Richmond and Jungers 1995; Wood and Lieberman 2001). One issue with this
method is that morphological diversity varies greatly among the extant model species, even those
closely related to one another (Ackermann 2002). Clearly, the choice of extant species model can
dramatically influence results for the fossil sample. This problem has been recognized and it has
been suggested that each living species offers a unique collection of biological variables, so the
putative fossil species can be compared to more than one analog depending upon the research
questions (Baab 2008; Harvati 2003; McNulty 2003). Aspects of the analog that may relate to the
fossil sample are: phylogenetic proximity, ecological similarity, shared skeletal function and
spatial or temporal equivalence.
If, as this work suggests, population history shapes the magnitude of variation within a
species, then choosing living analogs with comparable population histories to extinct species
may help clarify fossil taxonomy. For example, chimpanzee subspecies may provide a more
accurate model of fossil hominin diversity than modern Homo sapiens. Also, orangutan diversity
may bear little resemblance to what we would expect to find in any fossil hominin because of the
temporal depth and geographic parameters of their evolutionary history. Clearly, the hominin
fossil record is incomplete, however it does retain geographic information to some degree. It is
this geographic information that should compared to extant species when selecting appropriate
analogs of diversity.
84
As more hominin fossils are continually discovered, there is a growing awareness that
understanding intraspecific diversity is central to clarifying how our own species emerged. Even
though many fossil hominin species are represented by only a few individuals, adopting a
population perspective on diversity is an important aspect of interpreting the fossil material we
do have. The fossil record introduces a temporal depth that is seemingly intractable using extant
species as an ideal analog for diversity. However, in order to delineate fossil species using a
biologically grounded framework, an operational procedure needs to be uniformly applied. It has
been suggested that fossil species should be roughly comparable to living species in their
magnitude of variation (Gingerich 1985; Delson 1997; Plavcan and Cope 2001). Two alternate
scenarios could be clarified in the fossil record using extant diversity as a guide: fossil hominins
from different time-scales but putatively the same species and fossil hominin assemblages from
the same geological time period and locality. The first scenario is more complex, and therefore
necessitates an intrinsically imperfect comparative solution. This is exemplified by a species
such as Homo erectus, where a long evolutionary time period and extensive geographic range
may make all living hominoids sub-optimal models for contextualizing this great diversity.
Recent work on the earliest Homo erectus fossils outside of Africa, in Dmanisi the Republic of
Georgia, underscores this issue. Lordkipanidze et al. (2013) found that diversity within five
crania at the site exceeds that of modern humans and falls within the range of the genus Pan.
This site clearly represents individuals of the same species that were living at the same time
within a restricted geographic area. This work has received some criticism because the
individuals sampled represent different developmental stages and different sexes. Therefore, age
and sex variation may be inflating diversity beyond what is typically measured in other species.
Given the results from this dissertation, a larger degree of variation in early Homo erectus would
85
be expected in a species that presumably did not undergo the degree of population contraction
and subsequent expansion as modern humans. It should be noted that Homo sapiens, despite their
phylogenetic proximity to extinct hominins, are largely inadequate models of intraspecific
skeletal diversity because of our extremely distinct population history.
Here I use effective population size (Ne) as a means of summarizing historical
population history within each species. How can this metric inform fossil analyses?
Importantly, effective population size (Ne) is the number of individuals in a theoretically ideal
population with the same amount of drift as the actual population. Ne is sensitive to changes in
the census population size over time. Therefore, fossil species demarcated via modern analog do
not inherently mirror biological species, but groups with equivalent effective population sizes to
modern species. This is not intrinsically problematic, but the above correlation can also be used
to optimize efforts to model past diversity against living taxa. Furthermore, the correlation
between Ne and pairwise Procrustes distance can serve as a predictive system for estimating the
effective population size of fossil species with sufficient morphological specimens, but no
genetic data. Inferring the effective population size of paleospecies could further refine our
understanding of how population size and structure may have influenced diversity through
evolutionary time. In Baab (2008), for example, cranial shape variation, measured by the sum of
square Procrustes distance (SSD), of 13 H. erectus specimens was 0.46, similar to estimates for
the genus Pan. Assuming a linear relationship between SSD and Ne, this predicts an effective
population size of approximately 51,000 for H. erectus. A prior estimate for Ne of human
ancestors before 0.9 to 1.5 million years ago is 18,500 (Huff et al. 2010). The authors point out
that this value was surprisingly low especially for such a widespread species. The model shown
here could be further applied to estimate Ne for other fossil hominins or other species, especially
86
where multiple contemporaneous samples are available, such as the archaic Homo specimens at
the Sima de los Huesos in Atapuerca, Spain or the Australopithecus individuals at site AL333 in
Hadar, Ethiopia. The relationship between effective population size and cranial shape variation
creates opportunities for population genetic models to be explicitly applied to taxonomic
hypotheses. This is a novel approach to address the relationship of fossil diversity and taxonomy,
using microevolutionary methodology.
Future Directions
Several aspects of this study could be developed for future analyses. Firstly, genetic and
morphological data, while roughly matched geographically, are not taken from the same
individuals or populations. To address this in future studies, this relationship could be
investigated at the population-level in a wild vertebrate population to determine if the strength of
the correlation persists. For example, it would be optimal to study a species that exists both on a
mainland and an island population, one that is well documented genetically and ecologically. In
this case, biogeography and population history could serve as a more fine-scaled map onto
morphological data. Again, these combined morphology and genetics approaches are routine in
non-primate taxa and could be more readily implemented to probe this broad evolutionary
question.
Cranial specimens from museum collections, while often used as a proxy for wild
diversity, are a geographically and temporally biased collection and do not necessarily represent
a matched analog for wild cranial diversity. This caveat is implicit in all studies that use museum
collections to model diversity, including those aimed at clarifying fossil taxonomy. A better
87
understanding of the temporal, geographical and population biases in museum collections,
enabled by genetic analyses of the skeletal specimens, could help clarify this issue.
Another approach to further pursue the basic question outlined here would be teasing out
lack of morphological diversity that is due to drift, versus lack of diversity due to stabilizing
selection. For example, it could be argued that in humans and bonobos, the low diversity in
cranial data could be the result of stabilizing selection on cranial form, rather than drift limiting
diversity. Given that the remainder of the taxa here fit the model of increasing genetic and
cranial diversity, this is somewhat unlikely. Although, the issue of distinguishing drift versus
selection is central to many evolutionary questions, both molecular and skeletal, and warrants
deeper quantitative exploration. Additionally, PPD measures overall distance averaged across all
landmarks, but not all species vary in the same way. For example, even if we accept that drift
and mutation may be influencing both data types we still do not expect it to influence cranial
form in a uniform way across all species. The nature of drift and mutation is stochastic and
therefore the skeletal signature of it may not influence the same set of loci or cranial regions.
It is possible that the particular group of 12 taxa used in this study show a trend but with
the addition of more taxa, the strength of the relationship would shift. The addition of more
primate, mammal or vertebrate taxa would address this. Heritability in cranial regions, and
therefore their response to selection, between closely related species is comparable, but we may
expect this to vary in other, more phylogenetically distant species.
Crucial to making any inferences about natural selection is first testing whether neutral
evolutionary pressures (i.e. drift, migration or mutation) have played a strong role in shaping
variation. This neutralist-selectionist debate is a long-standing evolutionary conundrum that has
only recently been empirically addressed in studies of cranial variation. Data overwhelmingly
88
show that stochastic processes, such as drift, are important in shaping human cranial diversity.
This is a surprising finding, given the myriad developmental and adaptive pressures that
influence skull morphology. This project demonstrates that other primates also follow this broad
pattern. This project is unique because it integrates hominoid population genetics with the study
of skeletal variation. The goal here was move beyond morphological and molecular data as
seemingly opposed data-types and instead, unravel how both are the result of a unified
evolutionary process.
89
APPENDIX: Cranial Specimens
CAT#
Inst
TAXON
SEX
LOCALITY
143602
NMNH
Pongo pygmaeus abelii
Female
Aru Bay, East Sumatra
270807
NMNH
Pongo pygmaeus abelii
Female
Atjeh Districts, Sumatra
267325
NMNH
Pongo pygmaeus abelii
Male
Adji, Kuala Simpang, Sumatra
143590
NMNH
Pongo pygmaeus abelii
Male
Aru Bay, East Sumatra;
143593
NMNH
Pongo pygmaeus abelii
Male
Aru Bay, East Sumatra;
83504
HUM
Pongo pygmaeus abelii
Female
Sumatra;
83507
HUM
Pongo pygmaeus abelii
Female
Sumatra (north?): Soekaranda, (Lankat)
67173
HUM
Pongo pygmaeus abelii
Female
Sumatra
83502
HUM
Pongo pygmaeus abelii
Female
Sumatra
83506
HUM
Pongo pygmaeus abelii
Male
Sumatra
83503
HUM
Pongo pygmaeus abelii
Male
Sumatra
12209
HUM
Pongo pygmaeus abelii
Male
Sumatra: Langkat;
ad 6420
HUM
Pongo pygmaeus abelii
Male
Sumatra (north?): Soekaranda;
50960
MCZ
Pongo pygmaeus abelii
Male
Sumatra: Sempang (R?), 2 days upriver from Ianala;
37517
MCZ
Pongo pygmaeus abelii
Male
Sumatra (N): Kabandsahe;
37516
MCZ
Pongo pygmaeus abelii
Male
Sumatra (N): Kabandsahe;
37519
MCZ
Pongo pygmaeus abelii
Female
Sumatra (N): Kabandsahe;
142170
NMNH
Pongo pygmaeus pygmaeus
Female
Sungai Sama, West Borneo;
142169
NMNH
Pongo pygmaeus pygmaeus
Female
Sungai Sama, West Borneo;
142186
NMNH
Pongo pygmaeus pygmaeus
Female
Sakaiam River, Sanggau district, West Borneo
142185
NMNH
Pongo pygmaeus pygmaeus
Female
Sakaiam River, West Borneo
142187
NMNH
Pongo pygmaeus pygmaeus
Female
Sakaiam River, West Borneo
142181
NMNH
Pongo pygmaeus pygmaeus
Female
Sakaiam River, West Borneo
142193
NMNH
Pongo pygmaeus pygmaeus
Female
Sakaiam River, West Borneo
142190
NMNH
Pongo pygmaeus pygmaeus
Female
Sakaiam River, West Borneo
145309
NMNH
Pongo pygmaeus pygmaeus
Female
Stempang River, West Borneo
145308
NMNH
Pongo pygmaeus pygmaeus
Female
Stempang River; West Borneo
145306
NMNH
Pongo pygmaeus pygmaeus
Female
Stempang River, Sungei Maton, West Borneo;
153282
NMNH
Pongo pygmaeus pygmaeus
Female
S.W. Borneo: Mambuluh River
197664
NMNH
Pongo pygmaeus pygmaeus
Female
Borneo: Sungai Menganne;
153830
NMNH
Pongo pygmaeus pygmaeus
Female
S.W. Borneo: Mambuluh River
145302
NMNH
Pongo pygmaeus pygmaeus
Female
W. Borneo: Sakaiam River:
145300
NMNH
Pongo pygmaeus pygmaeus
Female
W. Borneo: Sampang River
153805
NMNH
Pongo pygmaeus pygmaeus
Female
S.W. Borneo: Batu Jurond;
153822
NMNH
Pongo pygmaeus pygmaeus
Female
S.W. Borneo: Kendawangan River
50958
MCZ
Pongo pygmaeus pygmaeus
Female
Borneo: Kinabatangan River, Abai
37363
MCZ
Pongo pygmaeus pygmaeus
Female
Borneo: Kinabatangan River, Abai;
143188
NMNH
Pongo pygmaeus pygmaeus
Male
Sakaiam River, West Borneo
142189
NMNH
Pongo pygmaeus pygmaeus
Male
Sakaiam River, West Borneo;
145304
NMNH
Pongo pygmaeus pygmaeus
Male
Stempang River, West Borneo
145305
NMNH
Pongo pygmaeus pygmaeus
Male
Sungei Maton, Borneo; premax sutures obscured
153827
NMNH
Pongo pygmaeus pygmaeus
Male
S.W. Borneo: Mambuluh River
142197
NMNH
Pongo pygmaeus pygmaeus
Male
W. Borneo: Sakaiam River
142196
NMNH
Pongo pygmaeus pygmaeus
Male
W. Borneo: Sakaiam River;
142194
NMNH
Pongo pygmaeus pygmaeus
Male
W. Borneo: Sakaiam River;
90
142198
NMNH
Pongo pygmaeus pygmaeus
Male
W. Borneo: Sakaiam River
153806
NMNH
Pongo pygmaeus pygmaeus
Male
S.W. Borneo: Kendawangan River;
145319
NMNH
Pongo pygmaeus pygmaeus
Male
W. Borneo: Sempang River;
145318
NMNH
Pongo pygmaeus pygmaeus
Male
W. Borneo: Sempang River;
153823
NMNH
Pongo pygmaeus pygmaeus
Male
S.W. Borneo: Kendawangan River;
37362
MCZ
Pongo pygmaeus pygmaeus
Male
Borneo: Kinabatangan River, Abai;
5061
MCZ
Pongo pygmaeus pygmaeus
Male
Borneo
89354
AMNH
Pan troglodytes verus
Female
no tag
89351
AMNH
Pan troglodytes verus
Female
no tag
N/7002
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7003
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7004
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7017
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7012
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7032
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7026
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7036
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7037
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
N/7047
Peabody
Pan troglodytes verus
Female
Liberia: Ganta mission, Monrovia;
89407
AMNH
Pan troglodytes verus
Male
no tag
89355
AMNH
Pan troglodytes verus
Male
no tag
89353
AMNH
Pan troglodytes verus
Male
Ivory Coast: Durkoue;
89406
AMNH
Pan troglodytes verus
Male
no tag
N/7006
Peabody
Pan troglodytes verus
Male
Liberia: Ganta mission, Monrovia;
N/7024
Peabody
Pan troglodytes verus
Male
Liberia: Ganta mission, Monrovia;
N/7023
Peabody
Pan troglodytes verus
Male
Liberia: Ganta mission, Monrovia;
N/7022
Peabody
Pan troglodytes verus
Male
Liberia: Ganta mission, Monrovia;
N/7030
Peabody
Pan troglodytes verus
Male
Liberia: Ganta mission, Monrovia;
N/7040
Peabody
Pan troglodytes verus
Male
Liberia: Ganta mission, Monrovia;
167343
AMNH
Pan troglodytes troglodytes
Female
French Cameroons
90292
AMNH
Pan troglodytes troglodytes
Female
Cameroon: Metet
174860
AMNH
Pan troglodytes troglodytes
Female
No Tag
201469
AMNH
Pan troglodytes troglodytes
Female
French Cameroon: Lomie;
90293
AMNH
Pan troglodytes troglodytes
Female
No Tag
176226
NMNH
Pan troglodytes troglodytes
Female
West Africa: Southern Kamerun;
176229
NMNH
Pan troglodytes troglodytes
Female
West Africa: SOuthern Kamerun;
M.171
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.13
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M. 169
PCM
Pan troglodytes troglodytes
Female
Cameroons: Lelo Village, Batouri District
M.105
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.155
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.184
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.78
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.186
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.234
PCM
Pan troglodytes troglodytes
Female
Cameroons: Ndokofass, NE of Yabassi;
M.148
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.86
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.181
PCM
Pan troglodytes troglodytes
Female
Cameroons: Lelo Village, Batouri District;
M.172
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.158
PCM
Pan troglodytes troglodytes
Female
Cameroons: between Batouri & Lomie;
M.01
PCM
Pan troglodytes troglodytes
Female
Cameroons: Batouri District;
91
M.02
PCM
Pan troglodytes troglodytes
Female
Cameroons: Batouri District;
M.134
PCM
Pan troglodytes troglodytes
Female
Cameroons: Bimba, Batouri District;
M.249
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.299
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.352
PCM
Pan troglodytes troglodytes
Female
Cameroons: Meyoss Village, Batouri District;
M.348
PCM
Pan troglodytes troglodytes
Female
Cameroons: Mamalo Village, Batouri District;
M.254
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.279
PCM
Pan troglodytes troglodytes
Female
Cameroons: Yabassi District;
M.450
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.425
PCM
Pan troglodytes troglodytes
Female
Cameroons: Meyoss Village, Batouri District;
M.449
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.277
PCM
Pan troglodytes troglodytes
Female
Cameroons: Yabassi District;
M.424
PCM
Pan troglodytes troglodytes
Female
Cameroons: Meyoss Village, Batouri District;
M.664
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.576
PCM
Pan troglodytes troglodytes
Female
Cameroons: Lelo Village, Batouri Districtl
M.506
PCM
Pan troglodytes troglodytes
Female
Cameroons: Lomie District;
M.650
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.506
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District
M.702
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.501
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.504
PCM
Pan troglodytes troglodytes
Female
Cameroons: Obala Village, Batouri District;
M.491
PCM
Pan troglodytes troglodytes
Female
Cameroons: Lelo Village, Batouri Districtl
174699
NMNH
Pan troglodytes troglodytes
Female
French Congo: Lake Fernan Vaz.
174707
NMNH
Pan troglodytes troglodytes
Female
French Congo (Gabon?);
220063
NMNH
Pan troglodytes troglodytes
Female
French Congo (Gabon?): Animba;
84655
NMNH
Pan troglodytes troglodytes
Female
no info
174701
NMNH
Pan trogodytes troglodytes
Female
French Congo (Gabon)
167346
AMNH
Pan troglodytes troglodytes
Male
French Cameroons
167341
AMNH
Pan troglodytes troglodytes
Male
French Cameroons
167342
AMNH
Pan troglodytes troglodytes
Male
French Cameroons
90189
AMNH
Pan troglodytes troglodytes
Male
West Africa
174861
AMNH
Pan troglodytes troglodytes
Male
Sp. Guinea: N'sork
167344
AMNH
Pan troglodytes troglodytes
Male
No Tag
119770
AMNH
Pan troglodytes troglodytes
Male
French Equatorial Africa: Kango;
183130
AMNH
Pan troglodytes troglodytes
Male
S.E. Cameroons: Youkadouma;
176238
NMNH
Pan troglodytes troglodytes
Male
West Africa: Southern Kamerun;
176228
NMNH
Pan troglodytes trolodytes
Male
West Africa: Southern Kamerun;
M.144
PCM
Pan troglodytes troglodytes
Male
Cameroons: between Batouri and Lomie
M.440
PCM
Pan troglodytes troglodytes
Male
Cameroons: Obala Village, Batouri District;
M.254
PCM
Pan troglodytes troglodytes
Male
Cameroons: Lomie District;
M.272
PCM
Pan troglodytes troglodytes
Male
Cameroons: Ndinga Village, Batouri District;
M.984
PCM
Pan troglodytes troglodytes
Male
Cameroons: Meyoss Village, Batouri District;
M.724
PCM
Pan troglodytes troglodytes
Male
Cameroons: Obala Village, Batouri District;
M.988
PCM
Pan troglodytes troglodytes
Male
Cameroons: Meyoss Village, Batouri District;
M.712
PCM
Pan troglodytes troglodytes
Male
Cameroons: Obala Village, Batouri District;
CAM.236
PCM
Pan troglodytes troglodytes
Male
Cameroons: near Yaounde;
CAM.II.62
PCM
Pan troglodytes troglodytes
Male
Cameroons: Dehane;
CAM.219
PCM
Pan troglodytes troglodytes
Male
Cameroons: S. of Yaounde;
CAM.200
PCM
Pan troglodytes troglodytes
Male
Cameroons: S. of Yaounde;
CAM.206
PCM
Pan troglodytes troglodytes
Male
Cameroons: S. of Yaounde;
CAM.199
PCM
Pan troglodytes troglodytes
Male
Cameroons: S. of Yaounde;
92
174704
NMNH
Pan troglodytes troglodytes
Male
Gabon (French Congo): Lake Nkami;
220065
NMNH
Pan troglodytes troglodytes
Male
Gabon: Mperi, Fernan Vaz.;
51205
AMNH
Pan troglodytes schweinfurthii
Female
no tag
51204
AMNH
Pan troglodytes schweinfurthii
Male
Zaire: Faradje
51381
AMNH
Pan troglodytes schweinfurthii
Male
Congo: Akenge
51278
AMNH
Pan troglodytes schweinfurthii
Male
Congo: Akenge
51377
AMNH
Pan troglodytes schweinfurthii
Male
???
220062
NMNH
Pan troglodytes schweinfurthii
Female
Alboona
236971
NMNH
Pan troglodytes schweinfurthii
Female
Uganda: Budongo Forest;
C.259
PCM
Pan troglodytes schweinfurthii
Male
Congo: Makala-Avakubi Road, Ituri Forest;
51379
AMNH
Pan troglodytes schweinfurthii
Male
Zaire: Faradje
51202
AMNH
Pan troglodytes schweinfurthii
Male
Zaire: Medje
51209
AMNH
Pan troglodytes schweinfurthii
Male
Zaire: Medje
R.G.9338
TER
Pan paniscus
Female
R.G.15296
TER
Pan paniscus
Female
Belgian Congo:30 Km environs Sud Befale
Belgian Congo: Stanleyville, 25km S. terr (rive
gauche)
R.G. 13201
TER
Pan paniscus
Female
R.G. 11351
TER
Pan paniscus
Female
Belgian Congo: Lingomo (Ikela), alt: env. 350m
Belgian Congo: Basankusu, chefferie Poma. terr. des
Gombe du Lopori
R.G. 26963
TER
Pan paniscus
Female
Belgian Congo: Bengamisa (terr. Banalia)
R.G. 26945
TER
Pan paniscus
Female
Belgian Congo: Banalia
20882
TER
Pan paniscus
Female
Belgian Congo: Botanankasa
R.G. 21697
TER
Pan paniscus
Female
Belgian Congo; Zoo specimen?
R.G. 26989
TER
Pan paniscus
Female
Belgian Congo: Ponthierville;
R.G. 26991
TER
Pan paniscus
Female
Belgian Congo: Ponthierville;
R.G. 27012
TER
Pan paniscus
Female
Belgian Congo: Ponthierville;
R.G. 27002
TER
Pan paniscus
Female
Belgian Congo: Ponthierville;
R.G. 29034
TER
Pan paniscus
Female
Belgian Congo: no info;
R.G. 27698
TER
Pan paniscus
Female
R.G. 29042
TER
Pan paniscus
Female
terr. Ponthierville (rive gauche du Congo);
Belgian Congo: Wasamba (35km E. de Balangala,
terr. Basankusu)
R.G. 29045
TER
Pan paniscus
Female
R.G. 29040
TER
Pan paniscus
Female
Belgian Congo: Dongo, 15km SE de Yahuma
Belgian Congo: Wamba (35km E. de Balangala, terr.
Basankusu);
R.G. 29060
TER
Pan paniscus
Female
Belgian Congo: Dongo, Oshwe 50km S de Dekese;
R.G. 29065
TER
Pan paniscus
Female
Belgian Congo: Djeka, terr. Katako Kombe
88041 M3
TER
Pan paniscus
Female
Zaire: Babusoko
84036.1
TER
Pan paniscus
Female
Belgian Congo
R.G.888
TER
Pan paniscus
Male
R.G.15294
TER
Pan paniscus
Male
R.G.15295
TER
Pan paniscus
Male
R.G. 11353
TER
Pan paniscus
Male
Belgian Congo: Kasai (district du);
Belgian Congo: Stanleyville, 25km S. terr (rive
gauche)
Belgian Congo: Stanleyville, 25km S. terr (rive
gauche)
Belgian Congo: Chefferie Baolongo, terr.,
Bangandanga, rive gauche Lopori
R.G. 11149
TER
Pan paniscus
Male
Belgian Congo: Djolu (Lulonga)
R.G. 26960
TER
Pan paniscus
Male
Belgian Congo: Bengamisa (terr. Banalia)
R.G. 26939
TER
Pan paniscus
Male
Belgian Congo: Banalia;
R.G. 27005
TER
Pan paniscus
Male
Belgian Congo: Ponthierville;
R.G. 29037
TER
Pan paniscus
Male
Belgian Congo: no info;
R.G. 29036
TER
Pan paniscus
male
R.G. 27699
TER
Pan paniscus
Male
Belgian Congo: no info;
Belgian Congo: terr. Ponthierville (rive gauche du
Congo)
R.G. 28712
TER
Pan paniscus
Male
R.G. 29052
TER
Pan paniscus
Male
93
Belgian Congo
Belgian Congo: Batiamoyowa, 35km SSW de
Ponthierville
R.G. 29050
TER
Pan paniscus
Male
Belgian Congo: Route Stanleyville, Yateloma 50km
84036.09
TER
Pan paniscus
Male
Belgian Congo
38020
MCZ
Pan paniscus
Male
D.R. Congo: Stanleyville
38018
MCZ
Pan paniscus
male
D.R. Congo: 25 km S ofStanleyville
88041 M13
TER
Pan paniscus
Unknown
Zaire: Babusoko
88041 M10
TER
Pan paniscus
Unknown
Zaire: Babusoko
88041 M12
TER
Pan paniscus
Unknown
Zaire: Babusoko
201472
AMNH
Gorilla gorilla gorilla
Female
No tag
81652
AMNH
Gorilla gorilla gorilla
Female
Fr. Congo: Nola
54356
AMNH
Gorilla gorilla gorilla
Female
Cameroon
167340
AMNH
Gorilla gorilla gorilla
Female
Fr. Cameroons
167337
AMNH
Gorilla gorilla gorilla
Female
no tag
54327
AMNH
Gorilla gorilla gorilla
Female
Cameroon: Ebole Bangon
252582
NMNH
Gorilla gorilla gorilla
Female
French Congo
252579
NMNH
Gorilla gorilla gorilla
Female
French Congo: Soho; Sangha
252577
NMNH
Gorilla gorilla gorilla
Female
no info
M.11
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.03
PCM
Gorilla gorilla gorilla
Female
Cameroons: Bimba, Batouri District;
M.II.2
PCM
Gorilla gorilla gorilla
Female
Cameroons: S. of Yaounde;
M.150
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.96
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.138
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.58
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.136
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.139
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.89
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.256
PCM
Gorilla gorilla gorilla
Female
Cameroons: Kanyol, Batouri District;
M.177
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
M.174
PCM
Gorilla gorilla gorilla
Female
Cameroons: between Batouri & Lomie;
46325
MCZ
Gorilla gorilla gorilla
Female
no data
29047
MCZ
Gorilla gorilla goilla
Female
Cameroon: 1mi from Eboleura;
26850
MCZ
Gorilla gorilla gorilla
Female
Cameroon: Sakbayeme;
17684
MCZ
Gorilla gorilla gorilla
Female
Cameroon: Metet;
14750
MCZ
Gorilla gorilla gorilla
Female
Cameroon: Nellafup.
37264
MCZ
Gorilla gorilla gorilla
Female
Cameroon: Metet
38326
MCZ
Gorilla gorilla gorilla
Female
Cameroon: Metet;
214115
AMNH
Gorilla gorilla gorilla
Male
Fr. Congo: village of Oka, west of Okio;
214107
AMNH
Gorilla gorilla goilla
Male
Fr. Congo: village of Oka, west of Okio;
214114
AMNH
Gorilla gorilla gorilla
Male
Fr. Congo: village of Oka, west of Okio;
214109
AMNH
Gorilla gorilla gorilla
Male
Fr. Congo: village of Oka, west of Okio;
214113
AMNH
Gorilla gorilla gorilla
Male
Fr. Congo: village of Oka, west of Okio;
69398
AMNH
Gorilla gorilla gorilla
Male
?
214116
AMNH
Gorilla gorilla gorilla
Male
Fr. Congo: village of Oka, west of Okio;
201471
AMNH
Gorilla gorilla gorilla
Male
Fr. Cameroons: Sangmelima;
200506
AMNH
Gorilla gorilla gorilla
Male
Congo: Quesso region
200504
AMNm
Gorilla gorilla gorilla
Male
Congo: Quesso Region
200508
AMNH
Gorilla gorilla gorilla
Male
Congo: Quesso region
200502
AMNH
Gorilla gorilla gorilla
Male
Congo: Quesso region
200503
AMNH
Gorilla gorilla gorilla
Male
Congo: Quesso region
200505
AMNH
Gorilla gorilla gorilla
Male
Congo: Quesso region
167327
AMNH
Gorilla gorilla gorilla
Male
Fr. Cameroons
94
167326
AMNH
Gorilla gorilla gorilla
Male
no tag
54355
AMNH
Gorilla gorilla gorilla
Male
Cameroon
167329
AMNH
Gorilla gorilla gorilla
Male
French Cameroons
167332
AMNH
Gorilla gorilla gorilla
Male
French Cameroons
167334
AMNH
Gorilla gorilla gorilla
Male
French Cameroons
167338
AMNH
Goilla gorilla gorilla
Male
Fr. Cameroons
90194
AMNH
Gorilla gorilla gorilla
Male
Cameroon: Div. Moloundou, N'Guilili;
90290
AMNH
Gorilla gorilla gorilla
Male
Cameroon: Metet
145600
AMNH
Gorilla gorilla gorilla
Male
no data
176210
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun;
176217
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun
176216
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun;
176211
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun;
176209
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun;
176222
NMNH
Gorilla gorilla gorilla
Male
West AFrica: Southern Kamerun;
176224
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun;
176220
NMNH
Gorilla gorilla gorilla
Male
West Africa: Southern Kamerun;
220324
NMNH
Gorilla gorilla gorilla
Male
French Congo: Moamba Sanga, Ngovi;
174718
NMNH
Gorilla gorilla gorilla
Male
French Congo
174717
NMNH
Gorilla gorilla gorilla
Male
Frenh Congo
174714
NMNH
Gorilla gorilla gorilla
Male
French Congo: Lake Ferran Vaz.
174712
NMNH
Gorilla gorilla gorilla
Male
French Congo: Lake Ferran Vaz.
174716
NMNH
Gorilla gorilla gorilla
Male
FrenchCongo
174715
NMNH
Gorilla gorilla gorilla
Male
French Congo
297857
NMNH
Gorilla gorilla gorilla
Male
no info
174722
NMNH
Gorilla gorilla gorilla
Male
French Congo
38557
HUM
Hylobates agilis unko
Female
Sumatra: Pelawi;
38556
HUM
Hylobates agilis unko
Female
Sumatra: Pelawi;
38561
HUM
Hylobates agilis unko
Female
Sumatra: Pelawi;
85368
HUM
Hylobates agilis unko
Male
Sumatra: Pasir Pengerayan
85367
HUM
Hylobates agilis unko
Male
Sumatra: Danau Handjang;
38566
HUM
Hylobates agilis unko
Male
Sumatra: Pelawi;
38565
HUM
Hylobates agilis unko
Male
Sumatra: Rawas;
85365
HUM
Hylobates agilis unko
Male
Sumatra: Telok Betong;
38564
HUM
Hylobates agilis unko
Male
Sumatra (west);
38563
HUM
Hylobates agilis unko
Male
Sumatra: Palembang;
85378
HUM
Hylobates agilis unko
Unknown
Sumatra: Paoh;
38568
HUM
Hylobates agilis unko
Unknown
Sumatra: Laut Kawas Battak;
81046
HUM
Hylobates agilis unko
Unknown
Sumatra: Sockaranda;
VL/5056Dup
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5051
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5053
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5053Dup
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5058Dup
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5057Dup
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5061Dup
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5061
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/5063Dup
AMNH-a
Homo sapiens sapiens
Female
Hungary: Keszo-Hidegkut
VL/203
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/206
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/207
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
95
VL/350
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/442
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/443
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/444
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/445
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/455
AMNH-a
Homo sapiens sapiens
Female
West Africa: Calabar
VL/5054Dup
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5055Dup
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5056
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5052
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5052Dup
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5058
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5063
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5068
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5070
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/5070Dup
AMNH-a
Homo sapiens sapiens
Male
Hungary: Keszo-Hidegkut
VL/205
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/204
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/202
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/81
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/349
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/351
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/405
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/401
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/402
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
VL/999
AMNH-a
Homo sapiens sapiens
Male
West Africa: Calabar
103348
AMNH
Hylobates klossii
Female
Mentawi Islands North Pagi
103347
AMNH
Hylobates klossii
Female
Mentawi Islands North Pagi
103252
AMNH
Hylobates klossii
Female
Mentawi Islands North Pagi
103248
AMNH
Hylobates klossii
Female
Mentawi Islands North Pagi
103246
AMNH
Hylobates klossii
Female
Mentawi Islands North Pagi
103244
AMNH
Hylobates klossii
Female
Mentawi Islands North Pagi
121685
NMNH
Hylobates klossii
Female
S. Pagi Island
252307
NMNH
Hylobates klossii
Female
Indonesia: Sumatra: Sipora Island
252310
NMNH
Hylobates klossii
Female
A49657
NMNH
Hylobates klossii
Female
Indonesia: Sumatra, Siberut Island
Indonesia: Sumatra: Sumatra Barat Province,
Mentawi Islands, S Pagi
103352
AMNH
Hylobates klossii
Male
Mentawi Islands North Pagi
103350
AMNH
Hylobates klossii
Male
Mentawi Islands North Pagi
103349
AMNH
Hylobates klossii
Male
Mentawi Islands North Pagi
103344
AMNH
Hylobates klossii
Male
Mentawi Islands North Pagi
103249
AMNH
Hylobates klossii
Male
Mentawi Islands North Pagi
103247
AMNH
Hylobates klossii
Male
Mentawi Islands North Pagi
12.2.2.1
NHM
Hylobates klossii
Male
N. Pagi Island, W.Coast. Sumatra
2292
NCBN
Hylobates klossii
Male
121674
NMNH
Hylobates klossii
Male
121679
NMNH
Hylobates klossii
Male
A49656
NMNH
Hylobates klossii
Male
N. Pagai Mentawi-I.Sumatra
Indonesia: Sumatra: Sumatra Barat Province,
Mentawi Islands, S Pagi
Indonesia: Sumatra: Sumatra Barat Province,
Mentawi Islands, S Pagi
Indonesia: Sumatra: Sumatra Barat Province,
Mentawi Islands, S Pagi
1064
NCBN
Hylobates moloch
Female
Java, Indonesia
1938.11.30.2
NHM
Hylobates moloch
Female
Salak, West Java
96
2104
NCBN
Hylobates moloch
Female
Java, Indonesia
4622
NCBN
Hylobates moloch
Female
Java, Indonesia
34322
NCBN
Hylobates moloch
Female
Mt. Slamet, Kalikidang, Java, Indonesia
156471
NMNH
Hylobates moloch
Female
Indonesia:Java, Tamandjaija
1037
NCBN
Hylobates moloch
Male
Java, Indonesia
1063
NCBN
Hylobates moloch
Male
Java, Indonesia
1909.1.5.1
NHM
Hylobates moloch
Male
Tji Wangie, Java
1938.11.30.1
NHM
Hylobates moloch
Male
Salak, West Java
4621
NCBN
Hylobates moloch
Male
Java, Indonesia
42095
NCBN
Hylobates moloch
Male
Java, Indonesia
54.5
NHM
Hylobates moloch
Male
Tjiboclas, Gedeh, West Java
5784
NCBN
Hylobates moloch
Male
Zoological Museum Amsterdam, 1939
1845.4.2.1
NHM
Hylobates moloch
Male
Malacca
102190
AMNH
Symphalangus syndactylus
Female
S. Sumatra: Boekit Doeloe
102188
AMNH
Symphalangus syndactylus
Female
S. Sumatra: Boekit Doeloe
106584
AMNH
Symphalangus syndactylus
Female
Sumatra, Boekit Sanggoel, Benkoelen
1920.1.26.2
NHM
Symphalangus syndactylus
Female
c.Lubukraman, 3deg29'S104deg08'E
106581
AMNH
Symphalangus syndactylus
Male
Sumatra, Goenoeng Dempo
102463
AMNH
Symphalangus syndactylus
Male
Sumatra, Loeboeck-Linggan Plains
1920.1.26.1
NHM
Symphalangus syndactylus
Male
e. Lubukraman, 3deg29'S104deg08'E
106583
AMNH
Symphalangus syndactylus
Female
Sumatra, Boekit Sanggoel, Benkoelen
102721
AMNH
Symphalangus syndactylus
Female
Sumatra, Palembang, Macarah Doewa
102195
AMNH
Symphalangus syndactylus
Female
Sumatra: Loebock Linggan
19.11.12.3
NHM
Symphalangus syndactylus
Female
Surjei Kulentag,Sumatra
1938.11.30.5
NHM
Symphalangus syndactylus
Female
Siantar, N.E. Sumatra,3N,99E(app)
2332
NCBN
Symphalangus syndactylus
Female
NA
4619
NCBN
Symphalangus syndactylus
Female
Palembang, Sumatera Selatan, Indonesia
5790
NCBN
Symphalangus syndactylus
Female
Deli, profestation
14279
NCBN
Symphalangus syndactylus
Female
Siantar, Deli, Sumatra
42171
NCBN
Symphalangus syndactylus
Female
Sumatra, Indonesia
42172
NCBN
Symphalangus syndactylus
Female
42179
NCBN
Symphalangus syndactylus
Female
Sumatra, Indonesia, 24 Feb 1880
NW slope Mt. Talaman, Ophir District, Sumatra
Indonesia. May 19, 1917
42182
NCBN
Symphalangus syndactylus
Female
Sumatra, Indonesia
114497
NMNH
Symphalangus syndactylus
Female
Indonesia: Sumatra, Tapanuli Bay
271048
NMNH
Symphalangus syndactylus
Female
Indonesia: Sumatra, Atjeh Gunong Shaitan
519573
NMNH
Symphalangus syndactylus
Female
Locality unknown
102728
AMNH
Symphalangus syndactylus
Male
Sumatra: Moeara Doewa
102726
AMNH
Symphalangus syndactylus
Male
Sumatra: Moeara Doewa
102725
AMNH
Symphalangus syndactylus
Male
Sumatra: Moeara Doewa
102720
AMNH
Symphalangus syndactylus
Male
Sumatra, Palembang, Macarah Doewa
102187
AMNH
Symphalangus syndactylus
Male
Sumatra: Boekit Doeloe
19.11.12.1
NHM
Symphalangus syndactylus
Male
Seolat, Darus, horinchi, Sumatra
81.3.15.1
NHM
Symphalangus syndactylus
Male
Laupong, S. Sumatra
1220
NCBN
Symphalangus syndactylus
Male
Pangkalanbrandan, Sumatra, Indonesia
4615
NCBN
Symphalangus syndactylus
Male
Padangse Bovenlanden, Pangkalan Sumatra
4617
NCBN
Symphalangus syndactylus
Male
Palembang, Sumatera Selatan, Indonesia
4618
NCBN
Symphalangus syndactylus
Male
Sumatra, Indonesia
4620
NCBN
Symphalangus syndactylus
Male
Sumatra, Boekit Nantiga, Goenoeng, Sago
5788
NCBN
Symphalangus syndactylus
Male
Tandjong, Morawa, Deli Sumatra
5789
NCBN
Symphalangus syndactylus
Male
Asahan, N. Sumatra
42162
NCBN
Symphalangus syndactylus
Male
Batang-Singalang, Sumatra
97
42168
NCBN
Symphalangus syndactylus
Male
Sumatra, Indonesia
283563
NMNH
Symphalangus syndactylus
Male
Indonesia: Sumatra
1867.4.12.5
NHM
Symphalangus syndactylus
Unknown
NA
42164
NCBN
Symphalangus syndactylus
Unknown
Sumatra, Indonesia
42174
NCBN
Symphalangus syndactylus
Unknown
Sumatra, Indonesia, 1878
42181
NCBN
Symphalangus syndactylus
Unknown
NA
201554
NMNH
Hylobates pileatus
Male
Thailand: Klong Menao
201555
NMNH
Hylobates pileatus
Male
Thailand: Lem Ngop
201556
NMNH
Hylobates pileatus
Female
Thailand: Klong Menao
241018
NMNH
Hylobates pileatus
Male
Thailand Nong Khor, Near
241019
NMNH
Hylobates pileatus
Female
Thailand Nong Khor, Near
321549
NMNH
Hylobates pileatus
Male
Cambodia: Plateau Kiri Rom
98
BIBLIOGRAPHY
Ackermann RR. 2002. Patterns of covariation in the hominoid craniofacial skeleton: implications
for paleoanthropological models. J Hum Evol 43(2):167-187.
Ackermann RR, and Cheverud JM. 2002. Discerning evolutionary processes in patterns of
tamarin (genus Saguinus) craniofacial variation. Am J Phys Anthropol 117(3):260-271.
Ackermann RR, and Cheverud JM. 2004. Detecting genetic drift versus selection in human
evolution. Proc Natl Acad Sci U S A 101(52):17946-17951.
Amos W, and Hoffman JI. 2010. Evidence that two main bottleneck events shaped modern
human genetic diversity. Proc Biol Sci 277(1678):131-137.
Andayani N, Brockelman W, Geissmann T, Nijman V, and Supriatna J. 2008. Hylobates moloch.
In: IUCN 2010 IUCN Red List of Threatened Species Version 20104.
Andayani N, and Melnick D. 2000. Genetic Variability in mtDNA of the Silvery Gibbon:
Implications for the Conservation of a Critically Endangered Species. Conservation
Biology 15(3):770-775.
Anthony NM, Johnson-Bawe M, Jeffery K, Clifford SL, Abernethy KA, Tutin CE, Lahm SA,
White LJ, Utley JF, Wickings EJ, and Bruford MW. 2007. The role of Pleistocene refugia
and rivers in shaping gorilla genetic diversity in central Africa. Proc Natl Acad Sci U S A
104(51):20432-20436.
Baab KL. 2008. The taxonomic implications of cranial shape variation in Homo erectus. J Hum
Evol 54(6):827-847.
Bastir M, and Rosas A. 2005. Hierarchical nature of morphological integration and modularity in
the human posterior face. Am J Phys Anthropol 128: 26–34.
Bastir M, Rosas A, Lieberman DE, O’Higgins P. 2008. Middle cranial fossa anatomy and the
origin of modern humans. Anat Rec 291:130–140.
Beals KL.1972. Head form and climatic stress. Am J Phys Anthropol 37:85–92.
Beals KL, Smith CL, and Dodd SM. 1983. Climate and the evolution of brachycephalization.
Am J Phys Anthropol 62:425-437.
Becquet C, Patterson N, Stone AC, Przeworski M, and Reich D. 2007. Genetic structure of
chimpanzee populations. PLoS Genet 3(4):e66.
Betti L, Balloux F, Amos W, Hanihara T, and Manica A. 2009. Distance from Africa, not
climate, explains within-population phenotypic diversity in humans. Proc Biol Sci
276(1658):809-814.
99
Betti L, Balloux F, Hanihara T, and Manica A. 2010. The relative role of drift and selection in
shaping the human skull. Am J Phys Anthropol 141(1):76-82.
Betti L, von Cramon-Taubadel N, Lycett SJ. 2012. Human pelvis and long bones reveal
differential preservation of ancient population history and migration out of Africa. Hum
Biol 84: 139–152.
Betti L, von Cramon-Taubadel N, Manica A, Lycett SJ. 2013. Global geometric morphometric
analyses of the human pelvis reveal substantial neutral population history effects, even
across sexes. PLoS One 8:e55909.
Boas F. 1912. Changes in the bodily form of descendants of immigrants. New York: Columbia
University Press.
Briscoe DA, Malpica JM, Robertson A, Smith GJ, Frankham R, Banks RG, and Barker JSF.
1992. Rapid loss of genetic variation in large captive populations of Drosophila flies:
implications for the genetic management of captive populations. Conserv Biol 6:416–
425.
Bromham L, Woolfit M, Lee MSY, Rambaut A. 2002. Testing the relationship between
morphological and molecular rates of change along phylogenies. Evolution 56:1921–
1930.
Bromham L. 2003. Molecular clocks and explosive radiations. J Mol Evol 57:S13–S20.
Cann RL, Stoneking M, Wilson AC. 1987. Mitochondrial DNA and human evolution. Nature
325, 31−36.
Cardini A, Thorington RW, Jr., and Polly PD. 2007. Evolutionary acceleration in the most
endangered mammal of Canada: speciation and divergence in the Vancouver Island
marmot (Rodentia, Sciuridae). J Evol Biol 20(5):1833-1846.
Carey JW, and Steegmann AT. 1981. Human Nasal protrusion, latitude and climate. Am J Phys
Anthropol 56:313-319.
Cavalli-Sforza LL, Menozzi P, Piazza A. 1994. The History and Geography of Human Genes
Princeton Univ. Press.
Chan Y. 2013. Inferring the evolutionary histories of divergences in Hylobates and Nomascus
gibbons through multilocus sequence data. BMC Evolutionary Biology 13.1 82.
Chen FC, and Li WH. 2001. Genomic divergences between humans and other hominoids and the
effective population size of the common ancestor of humans and chimpanzees.
Am J Hum Genet 68(2):444-456.
100
Cheverud JM. 1982. Phenotypic, genetic, and environmental. morphological integration in the
cranium. Evolution 36: 499–516.
Cheverud J. 1988. A comparison of genetic and phenotypic correlations. Evolution, 42: 958-968.
Cheverud J. 1996. Developmental integration and the evolution of pleiotropy. American
Zoologist 36(1):44-50.
Clifford SL, Anthony NM, Bawe-Johnson M, Abernethy KA, Tutin CE, White LJ, Bermejo M,
Goldsmith ML, McFarland K, Jeffery KJ, Bruford MW, and Wickings EJ. 2004.
Mitochondrial DNA phylogeography of western lowland gorillas (Gorilla gorilla
gorilla). Mol Ecol 13(6):1551-1565, 1567.
Coon C. 1962 The Origin of Races. Alfred A. Knopf, New York.
Davies TJ, Savolainen V. 2006. Neutral theory, phylogenies, and the relationship between
phenotypic change and evolutionary rates Evolution 60(3):476-483.
Darwin C and Wallace AR. 1858. On the tendency of species to form varieties; and on the
perpetuation of varieties and species by natural means of selection. J Proc Linn Soc.
Zoology 3: 46–50.
de Beer GR. 1937. The Development of the Vertebrate Skull. London: Oxford University Press.
Delson E. 1997. One skull does not a species make. Nature 389: 445-446.
Dhuyvetter H, Maelfait JP, and Desender K. 2007. Inter- and intraspecific genetic and
morphological variation in a sibling pair of carabid species. Saline Systems 3:4.
Dobzhansky T. 1937. Genetics and the Origin of Species. New York: Columbia University
Press.
Endler JA, 1986. Natural Selection in the Wild. Princeton University Press, Princeton.
Enlow DH, 1968. Handbook of Facial Growth. W.B. Saunders Company, Philadelphia, London.
Finlayson C. 2004. Neanderthals and Modern Humans: An Ecological and Evolutionary
Perspective. Cambridge University Press, Cambridge.
Fischer A, Pollack J, Thalmann O, Nickel B, and Pääbo S. 2006. Demographic history and
genetic differentiation in apes. Curr Biol 16(11):1133-1138.
Fischer A, Wiebe V, Pääbo S, and Przeworski M. 2004. Evidence for a complex demographic
history of chimpanzees. Mol Biol Evol 21(5):799-808.
Fisher RA. 1930. The Genetical Theory of Natural Selection. Oxford: Clarendon Press.
101
Flicek, P, Amode MR, Barrell D, Beal K, Brent S, Carvalho-Silva D, Clapham P, Coates G,
Fairley S, Fitzgerald S, Gil L, Gordon L, Hendrix M, Hourlier T, Johnson N, Kähäri AK,
Keefe D, Keenan S, Kinsella R, Komorowska M, Koscielny G, Kulesha E, Larsson P,
Longden I, McLaren W, Muffato M, Overduin B, Pignatelli M, Pritchard B, Riat HS,
Ritchie GRS, Ruffier M, Schuster M, Sobral D, Tang YA, Taylor K, Trevanion S,
Vandrovcova J, White S, Wilson M, Wilder SP, Aken BL, Birney E, Cunningham F,
Dunham I, Durbin R, Fernández-Suarez XM, Harrow J, Herrero J, Hubbard TJP, Parker
A, Proctor G, Spudich G, Vogel J, Yates A, Zadissa A, and Searle SMJ. 2012. Ensembl
Nucleic Acids Research 40:D84-D90.
Franciscus RG, Long JC. 1991. Variation in human nasal height and breadth. Am J Phys
Anthropol 85: 419–427.
Franciscus RG. 2003. Internal nasal floor configuration in Homo with special reference to the
evolution of Neandertal facial form. J Hum Evol 44: 701–729.
Frankham R. 1995. Effective population size/adult population size ratios in wildlife: a review.
Genetical Research 66:95–107.
Fulgione D, Guglielmi S, Odierna G, Rippa D, Caliendo MF, and Rastogi RK. 2008.
Morphological differentiation and genetic structure in island lizard populations.
Zoolog Sci 25(5):465-474.
Goodman M. 1985. Rates of molecular evolution: the hominoid slowdown BioEssays 3: 9–14.
Goswami A, and Polly PD. 2010. The influence of modularity on cranial morphological disparity
in Carnivora and Primates (Mammalia). Plos One 5(3):e9517.
Gould SJ, and Lewontin RC. 1979. The spandrels of San Marco and the Panglossian paradigm: a
critique of the adaptationist programme. Proc R Soc Lond B Biol Sci 205(1161):581-598.
Guglielmino-Matessi CR, Gluckman P, and Cavalli-Sforza LL. 1979. Climate and evolution of
skull metrics in man. Am J Phys Anthropol 50:549-564.
Guy F, Brunet M, Schmittbuhl M, and Viriot L. 2003. New approaches in hominoid taxonomy:
morphometrics. Am J Phys Anthropol 121(3):198-218.
Hamilton AC, 1981. The quaternary history of African forests: its relevance to conservation.
Afr J Ecol 19:1–6.
Harpending HC, Sherry ST, Rogers AR, and Stoneking M. 1993. The genetic structure of ancient
human populations. Current Anthropology 34:483–496.
Harris EE. 2010. Nonadaptive processes in primate and human evolution. Am J Phys Anthropol
143 Suppl 51:13-45.
102
Harvati K. 2003. The Neanderthal taxonomic position: models of intra- and inter-specific
craniofacial variation. J Hum Evol 44(1):107-132.
Harvati K, Weaver TD. 2006. Human cranial anatomy and the differential preservation of
population history and climate signatures. Anat Rec 288:1225–1233.
Henn BM, Cavalli-Sforza LL and Feldman MW. 2012. The great human expansion. Proc. Natl.
Acad. Sci. USA 109, 17758–17764.
Hey J. 2010. The Divergence of Chimpanzee Species and Subspecies as Revealed in
Multipopulation Isolation-with-Migration Analyses. Molecular Biology and Evolution
27(4):921-933.
Hey J, and Wakeley J. 1997. A coalescent estimator of the population recombination rate.
Genetics 145(3):833-846.
Hobolth A, Dutheil JY, Hawks J, Schierup MH, and Mailund T. 2011. Incomplete lineage
sorting patterns among human, chimpanzee, and orangutan suggest recent orangutan
speciation and widespread selection. Genome Res 21(3):349-356.
Hofreiter M, Capelli C, Krings M, Waits L, Conard N, Munzel S, Rabeder G, Nagel D, Paunovic
M, Jambresic G, Meyer S, Weiss G, and Pääbo S. 2002. Ancient DNA analyses reveal
high mitochondrial DNA sequence diversity and parallel morphological evolution of late
pleistocene cave bears. Mol Biol Evol 19(8):1244-1250.
Holton NE, Franciscus RG. 2008. The paradox of a wide nasal aperture in cold-adapted
Neandertals: a causal assessment. J Hum Evol 55:942–951.
Hubbe M, Hanihara T, and Harvati K. 2009. Climate signatures in the morphological
differentiation of worldwide modern human populations. Anat Rec (Hoboken)
292(11):1720-1733.
Huff CD, Xing J, Rogers AR, Witherspoon D, and Jorde LB. 2010. Mobile elements reveal small
population size in the ancient ancestors of Homo sapiens. Proc Natl Acad Sci U S A
107(5):2147-2152.
Hlusko LJ. 2004. Integrating the genotype and phenotype in hominid paleontology.
Proc Natl Acad Sci U S A 101(9): 2653-2657.
Hylander WL. 1977. The adaptive significance of Eskimo craniofacial morphology. Orofacial
Growth and Development, Mouton Publishers, The Hague 129–169.
Israfil H, Zehr SM, Mootnick AR, Ruvolo M, and Steiper ME. 2011. Unresolved molecular
phylogenies of gibbons and siamangs (Family: Hylobatidae) based on mitochondrial, Ylinked, and X-linked loci indicate a rapid Miocene radiation or sudden vicariance event.
Mol Phylogenet Evol 58(3):447-455.
103
Jabbour, Rebecca S. 2008. Geographic variation in the forelimb and hindlimb skeletons of
African apes Ph.D., CITY UNIVERSITY OF NEW YORK.
Jalil MF, Cable J, Sinyor J, Lackman-Ancrenaz I, Ancrenaz M, Bruford MW, and Goossens B.
2008. Riverine effects on mitochondrial structure of Bornean orangutans (Pongo
pygmaeus) at two spatial scales. Mol Ecol 17(12):2898-2909.
Kaessmann H, and Pääbo S. 2002. The genetical history of humans and the great apes. J Intern
Med 251(1):1-18.
Kaessmann H, Wiebe V, and Pääbo S. 1999. Extensive nuclear DNA sequence diversity among
chimpanzees. Science 286(5442):1159-1162.
Kim SK, Carbone L, Becquet C, Mootnick AR, Li DJ, de Jong PJ, and Wall JD. 2011. Patterns
of genetic variation within and between gibbon species. Mol Biol Evol.
Kimbel, W. H. & White, T. D. 1988. Variation, sexual dimorphism, and taxonomy of Australopithecus. In (F. E. Grine, Ed.) Evolutionary History of the ‘‘Robust’’ Australopithecines,
175–192. New York: Aldine de Gruyter.
Kimbel WH, Martin LB. 1993. Species, species concepts, and primate evolution. New York:
Plenum Press.
Kimura M. 1968. Evolutionary rate at the molecular level. Nature 217(5129):624-626.
King JL, Jukes TH. Non-Darwinian evolution. Science. 1969. 164(3881):788–798.
Klingenberg CP. 2008. Morphological integration and developmental modularity. Annu Rev
Ecol Evol Syst 39: 115–132.
Koertvelyessy T. 1972. Relationships between the frontal sinus and climatic conditions: a
skeletal approach to cold adaptation. Am J Phys Anthropol 37:161–172
Lande R. 1976. Natural Selection and Random Genetic Drift in Phenotypic Evolution. Evolution
30(2):314-334.
Lande R. 1979. Quantitative genetic analysis of multivariate evolution, applied to brain: body
size allometry. Evolution 33:402–416.
Lande R. 1981. Models of speciation by sexual selection on polygenic traits. Proc Natl Acad Sci.
USA 78:3721–3725.
Leamy LL, and Cheverud JM. 1999. Quantitative trait loci for early- and late-developing skull
characters in mice: a test of the genetic independence model of morphological
integration. American Naturalist 153:201-214.
104
Leinonen T, Cano JM, Makinen H, and Merila J. 2006. Contrasting patterns of body shape and
neutral genetic divergence in marine and lake populations of threespine sticklebacks. J
Evol Biol 19(6):1803-1812.
Leutenegger W, and Cheverud JM. 1982. Correlates of sexual dimorphism in primates:
Ecological and size variables. Int J Primatol 3:387–402.
Leutenegger W, and Cheverud JM. 1985. Sexual dimorphism in primates: The effects of size. In
WLJungers (ed.): Size and Scaling in Primate Biology. New York: Plenum Press, 33–50.
Leutenegger W, Masterson TJ. 1989. The ontogeny of sexual dimorphism in the cranium of
Bornean orangutans (Pongo pygmaeus pygmaeus): II. Allometry and heterochrony Z
Morph Anthrop 78:15–24.
Locke DP, Hillier LW, Warren WC, Worley KC, Nazareth LV, Muzny DM, Yang SP, Wang Z,
Chinwalla AT, Minx P, Mitreva M, Cook L, Delehaunty KD, Fronick C, Schmidt H,
Fulton LA, Fulton RS, Nelson JO, Magrini V, Pohl C, Graves TA, Markovic C, Cree A,
Dinh HH, Hume J, Kovar CL, Fowler GR, Lunter G, Meader S, Heger A, Ponting CP,
Marques-Bonet T, Alkan C, Chen L, Cheng Z, Kidd JM, Eichler EE, White S, Searle S,
Vilella AJ, Chen Y, Flicek P, Ma J, Raney B, Suh B, Burhans R, Herrero J, Haussler D,
Faria R, Fernando O, Darre F, Farre D, Gazave E, Oliva M, Navarro A, Roberto R,
Capozzi O, Archidiacono N, Della Valle G, Purgato S, Rocchi M, Konkel MK, Walker
JA, Ullmer B, Batzer MA, Smit AF, Hubley R, Casola C, Schrider DR, Hahn MW,
Quesada V, Puente XS, Ordonez GR, Lopez-Otin C, Vinar T, Brejova B, Ratan A, Harris
RS, Miller W, Kosiol C, Lawson HA, Taliwal V, Martins AL, Siepel A, Roychoudhury
A, Ma X, Degenhardt J, Bustamante CD, Gutenkunst RN, Mailund T, Dutheil JY,
Hobolth A, Schierup MH, Ryder OA, Yoshinaga Y, de Jong PJ, Weinstock GM, Rogers
J, Mardis ER, Gibbs RA, and Wilson RK. 2011. Comparative and demographic analysis
of orang-utan genomes. Nature 469(7331):529-533.
Lockwood C, Kimbel W, Lynch J. 2004. Morphometrics and hominoid phylogeny: support for a
chimpanzee-human clade and differentiation among great ape subspecies. Proc Natl Acad
Sci USA 101:4356–4360.
Lordkipanidze D, Ponce de León MS, Margvelashvili A, Rak Y, Rightmire GP, Vekua A,
Zollikofer CPE. 2013. A complete skull from Dmanisi, Georgia, and the evolutionary
biology of early Homo. Science, 342: 326–331.
Lougheed SC, Austin JD, Bogart JP, Boag PT, and Chek AA. 2006. Multi-character perspectives
on the evolution of intraspecific differentiation in a neotropical hylid frog. BMC Evol
Biol 6:23.
Lynch M. 1990. The Rate of Morphological Evolution in Mammals from the Standpoint of the
Neutral Expectation. The American Naturalist 136(6):727-741.
105
Lynch M, and Hill WG. 1986. Phenotypic Evolution by Neutral Mutation. Evolution 40(5):915935.
MacKinnon J, and MacKinnon K. 1987. Conservation status of the primates of the Indo-Chinese
subregion. Primate Conservation 8:187-195.
Manica A, Prugnolle F, and Balloux F. 2005. Geography is a better determinant of human
genetic differentiation than ethnicity. Hum Genet 118: 366–371.
Manica A, Amos W, Balloux F, and Hanihara T. 2007. The effect of ancient population
bottlenecks on human phenotypic variation. Nature 448(7151):346-348.
Manica A, Betti L, Balloux F, and Hanihara T. 2010. The Relative Role of Drift and Selection in
Shaping the Human Skull. Am J Phys Anthropol 141(1):76-82.
Marroig G, and Cheverud JM. 2004. Did natural selection or genetic drift produce the cranial
diversification of neotropical monkeys? Am Nat 163(3):417-428.
Martin RB. 2000. Towards a unifying theory of bone remodeling. Bone 26: 1–6.
Mayr E. 1942. Systematics and the Origin of Species. New York: Columbia University Press.
Mayr E. 1983. How to carry out the adaptationist program? American Naturalist,121: 324–334.
Makedonska J, Wright BW, Strait DS. 2012. The effect of dietary adaption on cranial
morphological integration in capuchins (order Primates, genus Cebus). PLoS One
2012;7(10):e40398.
McEvoy BP, Powell JE, Goddard ME, Visscher PM. 2011. Human population dispersal “Out of
Africa” estimated from linkage disequilibrium and allele frequencies of SNPs Genome
Res 21: 821–829.
McNulty KP. 2003. Geometric Morphometric Analyses of Extant and Fossil Hominoid
Craniofacial Morphology. Ph.D. Dissertation: City University of New York Graduate
Center.
McNulty KM, Frost SR, Strait DS. 2006. Examining affinities of the Taung child by
developmental simulation. J Hum Evol 51: 274-296.
Meyer TJ, McLain AT, Oldenburg JM, Faulk C, Bourgeois MG, Conlin EM, Mootnick AR, de
Jong PJ, Roos C, Carbone L, Batzer MA. 2012. An Alu-based phylogeny of gibbons
(Hylobatidae). Mol Biol Evol 2012, 29:3441-3450.
Millstein R. 2007. Concepts of Drift and Selection in “The Great Snail Debate” of the 1950s and
Early 1960s.” J. Cain and M. Ruse (eds.), Descended from Darwin: Insights into the
History of Evolutionary Studies, 1900–1970. PA: American Philosophical Society.
106
Moss ML and Young RW. 1960. A functional approach to craniology. Am J Phys Anthropol.
18: 281–291.
Muir CC, Galdikas BM, and Beckenbach AT. 2000. mtDNA sequence diversity of orangutans
from the islands of Borneo and Sumatra. J Mol Evol 51(5):471-480.
Nater A, Arora N, Greminger MP, van Schaik CP, Singleton I. 2013. Marked population
structure and recent migration in the critically endangered Sumatran orangutan (Pongo
abelii). J Hered 104: 2–13.
Nei M. 1987. Molecular Evolutionary Genetics. New York: Columbia University Press.
Nei M. 2007. The new mutation theory of phenotypic evolution. Proc Natl Acad Sci U S A
104:12235–42
Nijman V, and Geissman T. 2008. Symphalangus syndactylus. In: IUCN 2010 IUCN Red List of
Threatened Species Version 20104.
Noda R, Kim CG, Takenaka O, Ferrell RE, Tanoue T, Hayasaka I, Ueda S, Ishida T, and Saitou
N. 2001. Mitochondrial 16S rRNA sequence diversity of hominoids. J Hered 92(6):490496.
O’Higgins P and Jones N., 1998. Facial growth in Cercocebus torquatus: An application of three
dimensional geometric morphometric techniques to the study of morphological variation.
J Anat. 193: 251-272.
Olson EC, Miller RL. 1958. Morphological integration. University of Chicago Press; Chicago.
Palombit RA. 1997. Inter- and intraspecific variation in the diets of sympatric siamang
(Hylobates syndactylus) and lar gibbons (Hylobates lar). Folia Primatol (Basel)
68(6):321-337.
Pigliucci M, and Preston K .2004. Phenotypic integration: Studying the ecology and evolution of
complex phenotypes. Oxford: Oxford University Press.
Pilbrow V. 2006. Population systematics of chimpanzees using molar morphometrics. J Hum
Evol 51(6):646-662.
Pilbrow V. 2010. Dental and phylogeographic patterns of variation in gorillas. J Hum Evol
59(1):16-34.
Plavcan JM, 1993. Catarrhine dental variability and species recognition in the fossil record.
In: Kimbel WH, Martin LB (Eds.) 1993. Species, Species Concepts, and Primate Evolution.
Plenum Press, NewYork, 239–293.
107
Pearson OM, Lieberman DE. 2004. The aging of Wolff’s ‘‘law: ontogeny and responses to
mechanical loading cortical bone. Am J Phys Anthropol 2004:63–99.
Plavcan JM, and Cope DA. 2001. Metric variation and species recognition in the fossil record.
Evol Anthropol. 10: 204-222.
Plavcan JM. 2002. Taxonomic variation in the patterns of craniofacial dimorphism in primates. J
Hum Evol 42(5):579-608.
Prado-Martinez J. 2013. Great ape genetic diversity and population history. Nature 499, 471–
475.
Prugnolle F, Manica A, and Balloux F. 2005. Geography predicts neutral genetic diversity of
human populations. Curr Biol 15(5):R159-160.
Ramachandran S, Deshpande O, Roseman CC, Rosenberg NA, Feldman MW, and CavalliSforza LL. 2005. Support from the relationship of genetic and geographic distance in
human populations for a serial founder effect originating in Africa. Proc Natl Acad Sci U
S A 102(44):15942-15947.
Rasner CA, Yeh P, Eggert LS, Hunt KE, Woodruff DS, and Price TD. 2004. Genetic and
morphological evolution following a founder event in the dark-eyed junco, Junco
hyemalis thurberi. Mol Ecol 13(3):671-681.
Ravosa MJ. 1991. The ontogeny of cranial sexual dimorphism in two Old World monkeys:
Macaca fascicularis and Nasalis larvatus. Int J Primatol 12:403-426.
Reed DH, and Frankham R. 2001. How closely correlated are molecular and quantitative
measures of genetic variation? A meta-analysis. Evolution 55: 1095–1103.
Relethford JH. 1994. Craniometric variation among modern human populations. Am J Phys
Anthropol 95(1):53-62.
Relethford JH. 2002. Apportionment of global human genetic diversity based on craniometrics
and skin color. Am J Phys Anthropol 118(4):393-398.
Relethford JH. 2009. Geographic structure of global craniometric variation. Am J Phys
Anthropol 220-220.
Rendel JM. 1967. Canalization and Gene Control. Academic Press, New York.
Ricaut FX, Auriol V, von Cramon-Taubadel N, Keyser C, Murail P, Ludes B, and Crubezy E.
2010. Comparison between morphological and genetic data to estimate biological
relationship: the case of the Egyin Gol necropolis (Mongolia). Am J Phys Anthropol
143(3):355-364.
108
Richmond BG, and Jungers WL. 1995. Size Variation and Sexual Dimorphism in
Australopithecus afarensis and Living Hominoids. J Hum Evol 29(3):229-245.
Roseman CC. 2004. Detecting interregionally diversifying natural selection on modern human
cranial form by using matched molecular and morphometric data. Proc Natl Acad Sci U S
A 101(35):12824-12829.
Roseman CC, and Weaver TD. 2004. Multivariate apportionment of global human craniometric
diversity. Am J Phys Anthropol 125(3):257-263.
Roseman CC, and Weaver TD. 2007. Molecules versus morphology? Not for the human
cranium. Bioessays 29(12):1185-1188.
Rosenberg NA, Pritchard JK, Weber JL .2002. Genetic structure of human populations. Science
298: 2981–2985.
Ruvolo M, Pan D, Zehr S, Goldberg T, Disotell TR, von Dornum M. 1994. Gene trees and
hominoid phylogeny. Proc Natl Acad Sci USA 91:8900–8904
Ruvolo M. 1997. Genetic Diversity in Hominoid Primates. Annu Rev of Anthropol. 26:525-540.
Schaffler MB, and Burr DB. 1984. Primate cortical bone microstructure: relationship to
locomotion. Am J Phys Anthropol 65:191-197.
Schlichting CDP. 1998. Phenotypic Evolution: A Reaction Norm Perspective. Sunderland, MA:
Sinauer Associates.
Schmittbuhl M, Rieger J, Le Minor JM, Schaaf A, and Guy F. 2007. Variations of the
mandibular shape in extant hominoids: Generic, specific, and subspecific quantification
using elliptical fourier analysis in lateral view. Am J Phys Anthropol 132(1):119-131.
Shea BT, Leigh SR, Groves, CP. 1993. Multivariate craniometric variation in chimpanzees:
implications for species identification in paleoanthropology. In: Kimbel, W.H., Martin,
L.B. (Eds.), Species, Species Concepts, and Primate Evolution. Plenum Press, New York,
265-296.
Shea BT, and Coolidge HJ. 1988. Craniometric differentiation and systematics in the genus Pan.
J Hum Evol 17(7):671-685.
Schoenebeck JJ, Hutchinson SA, Byers A, Beale HC, Carrington B. 2012. Variation of BMP3
contributes to dog breed skull diversity. PLoS Genet. 8: e1002849.
Simpson GG. 1944. Tempo and Mode in Evolution. New York: Columbia University Press.
Simpson GG. 1964. Organisms and molecules in evolution. Science 146: 1535–1538.
109
Smith HF. 2009. Which cranial regions reflect molecular distances reliably in humans? Evidence
from three-dimensional morphology. Am J Hum Biol 21(1):36-47.
Smith HF, Terhune CE, and Lockwood CA. 2007. Genetic, geographic, and environmental
correlates of human temporal bone variation. Am J Phys Anthropol 134(3):312-322.
Sistrom M, Donnellan S & Hutchinson M. 2013. Delimiting species in recent radiations with low
levels of morphological divergence: a case study in Australian Gehyra geckos. Molecular
Phylogenetics and Evolution 68: 135-143.
Soule ́ME and Yang SY. 1973. Genetic variation in the side- blotched lizards on islands in the
Gulf of California. Evolution 27:593–600.
Soulé ME, and Zegers GP. 1996. Phenetics of natural populations: Genetic correlates of
phenotypic variation in the pocket gopher (Thomomys bottae) in California J Hered 87:
341–350.
Spitze, K. 1993. Population structure in Daphnia obtusa: quantitative genetic and allozyme
variation. Genetics 135:367–374.
Steegmann A. 1970. Cold adaptation and the human face. Am. J. Phys. Anthropol 32:243–250.
Stebbins GL. 1950. Variation and Evolution in Plants. New York: Columbia University Press.
Steiper ME, Young NM, Sukarna TY. 2004. Genomic data support the hominoid
slowdown and an early Oligocene estimate for the hominoid–cercopithecoid divergence
Proc Natl Acad Sci USA 101:17021–17026.
Steiper ME. 2006. Population history, biogeography, and taxonomy of orangutans (Genus:
Pongo) based on a population genetic meta-analysis of multiple loci. J Hum Evol
50(5):509-522.
Stone AC, Griffiths RC, Zegura SL, and Hammer MF. 2002. High levels of Y-chromosome
nucleotide diversity in the genus Pan. Proc Natl Acad Sci U S A 99(1):43-48.
Tattersall, I. 1986. Species recognition in human paleontology. J Hum Evol 15, 165-175.
Tattersall, I. 1992. Species concepts and species identification in human evolution. J Hum Evol
22, 341-349.
Thalmann O, Fischer A, Lankester F, Paabo S, and Vigilant L. 2007. The complex evolutionary
history of gorillas: insights from genomic data. Mol Biol Evol 24(1):146-158.
Thinh VN, Mootnick AR, Geissmann T, Li M, Ziegler T, Agil M, Moisson P., Nadler T, Walter
L, Roos C. 2010. Mitochondrial evidence for multiple radiations in the evolutionary
history of small apes. BMC Evolutionary Biology, 10: 74.
110
Uchida A.1992. Intra-species variation among the great apes: implications for taxonomy of fossil
hominids. PhD thesis. Harvard Univ., Cambridge, MA.
Uchida A. 1996. What we don’t know about great ape variation. Trends Ecol Evol. 11:163–68.
Uchida A. 1998a. Variation in tooth morphology of Gorilla gorilla. J Hum Evol 34(1):55-70.
Uchida A. 1998b. Variation in tooth morphology of Pongo pygmaeus. J Hum Evol 34(1):71-79.
Smith HF. 2011. The role of genetic drift in shaping modern human cranial evolution: A test
using microevolutionary modeling. Int J Evol Biol 2011:145262.
Steiper ME, Seiffert ER. Evidence for a convergent slowdown in primate molecular rates and its
implications for the timing of early primate evolution. 2012. Proc Natl Acad Sci U S A.
109:6006-6011.
Vidarsdottir U, and Cobb SN. 2004. Inter- and intra-specific variation in the ontogeny of the
hominoid facial skeleton: testing assumptions of ontogenetic variability. Annals of
Anatomy, 186, 423-428.
von Cramon-Taubadel N, and Lycett SJ. 2008. Human cranial variation fits iterative founder
effect model with African origin. Am J Phys Anthropol 136: 108-113.
von Cramon-Taubadel N. 2009. Congruence of individual cranial bone morphology and neutral
molecular affinity patterns in modern humans. Am J Phys Anthropol 140(2):205-215.
von Cramon-Taubadel N, and Weaver T. 2009. Insights from a quantitative genetic approach to
human morphological evolution. Evol Anthropol 18(6):237-240.
von Cramon-Taubadel N, and Smith HF. 2012. The relative congruence of cranial and genetic
estimates of hominoid taxon relationships: implications for the reconstruction of hominin
phylogeny. J Hum Evol 62 (5): 640-653.
Waddington CH. Canalization of development and the inheritance of acquired characteristics.
Nature 1942;150:563–65.
Waldmann P, and Andersson S. 1998. Comparison of quantitative genetic variation and allozyme
diversity within and between populations of Scabiosa canescens and S. columbaria.
Heredity 81:79–86.
Wall JD, Kim SK, Luca F, Carbone L, Mootnick AR, de Jong PJ, Di Rienzo A. 2013 Incomplete
lineage sorting is common in extant gibbon genera. PLoS ONE, 8:e53682.
Wang IJ, and Summers K. 2010. Genetic structure is correlated with phenotypic divergence
rather than geographic isolation in the highly polymorphic strawberry poison-dart frog.
Mol Ecol 19(3):447-458.
111
Warren KS, Verschoor EJ, Langenhuijzen S, Heriyanto, Swan RA, Vigilant L, and Heeney JL.
2001. Speciation and intrasubspecific variation of Bornean orangutans, Pongo pygmaeus
pygmaeus. Mol Biol Evol 18(4):472-480.
Washburn SL. 1963. The study of race. American Anthropologist 65:521–531.
Watterson GA. 1975. Number of segregating sites in genetic models without recombination.
Theor Popul Biol 7:256-276.
Weaver TD, Roseman CC, and Stringer CB. 2007. Were neandertal and modern human cranial
differences produced by natural selection or genetic drift? J Hum Evol 53(2):135-145.
Weaver TD, Roseman CC, Stringer CB. Close correspondence between quantitative- and
molecular-genetic divergence times for Neandertals and modern humans. 2008. Proc Natl
Acad Sci USA. 105(12):4645–4649.
West-Eberhard MJ .2003. Developmental plasticity and evolution. Oxford University Press, New
York.
Whitlock MC and Fowler K. 1999. The changes in genetic and environmental variance with
inbreeding in Drosophila melanogaster. Genetics 152: 345-353.
Whittaker D, and Geissmann T. 2008. Hylobates klossii. In: IUCN 2010 IUCN Red List of
Threatened Species Version 20104.
Whittaker DJ. 2005. Evolutionary Genetics of the Kloss’s Gibbon (Hylobates klossii):
Systematics, Phylogeography, and Conservation. Ph.D. Thesis: City University of New
York Graduate Center.
Willmore KE, Zelditch ML, Young N, Ah-Seng A, Lozanoff S. 2006. Canalization and
developmental stability in the brachyrrhine mouse. J Anat 208: 361–372.
Williams SA. 2012. Variation in anthropoid vertebral formulae: implications for homology and
homoplasy in hominoid evolution. J Exp Zool B Mol Dev Evol. 318(2):134-47.
Wolff J. 1986. The law of bone remodelling. Berlin: Springer-Verlag.
Wood B, and Harrison T. 2011. The evolutionary context of the first hominins. Nature
470(7334):347-352.
Wood B, and Lieberman DE. 2001. Craniodental variation in Paranthropus boisei: a
developmental and functional perspective. Am J Phys Anthropol 116(1):13-25.
Wright S. 1931. Evolution in Mendelian populations. Genetics 16:97-159.
Wright S. 1943. Isolation by distance. Genetics 28:114–138.
112
Young NM. 2008. A comparison of the ontogeny of shape variation in the anthropoid scapula:
functional and phylogenetic signal. Am J Phys Anthropol. 136(3):247-64.
Yu N, Chen FC, Ota S, Jorde LB, Pamilo P, Patthy L, Ramsay M, Jenkins T, Shyue SK, and Li
WH. 2002. Larger genetic differences within Africans than between Africans and
Eurasians. Genetics 161(1):269-274.
Yu, N., M.I. Jensen-Seaman, L. Chemnick, J.R. Kidd, A.S. Deinard, O. Ryder, K.K. Kidd, and
W.H. Li. 2003. Low Nucleotide Diversity in Chimpanzees and Bonobos. Genetics
164:1511-1518.
Yu N, Jensen-Seaman MI, Chemnick L, Ryder O, and Li WH. 2004. Nucleotide diversity in
gorillas. Genetics 166(3):1375-1383.
Zhang Y, and Ryder OA. 2001. Genetic divergence of orangutan subspecies (Pongo pygmaeus).
J Mol Evol 52(6):516-526.
Zhi L, Karesh WB, Janczewski DN, Frazier-Taylor H, Sajuthi D, Gombek F, Andau M,
Martenson JS, and O’Brien SJ. 1996. Genomic differentiation among natural populations
of orang-utan (Pongo pygmaeus). Curr Biol 6(10):1326-1336.
Zuckerkandl E, Pauling L. Molecules as documents of evolutionary history. 1965. J Theor Biol.
8(2):357–366.
113