fordisc and the determination of ancestry from craniometric data

101 Pages·2009·2.26 MB·English

FORDISC AND THE DETERMINATION OF
ANCESTRY FROM CRANIOMETRIC DATA
By
Marina Elliott
B.A., The University of British Columbia, 2005
A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF
THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ARTS
In
THE DEPARTMENT OF ARCHAEOLOGY
© Marina Elliott, 2008
SIMON FRASER UNIVERSITY
Summer 2008
All rights reserved. This work may not be
reproduced in whole or in part, by photocopy
or other means, without permission ofthe author.
APPROVAL
Name: Marina Elliott
Degree: Master ofArts
Title ofThesis: FORDISC and the determination of ancestry from
craniometric data
Examining Committee:
Chair:
Catherine D'Andrea
Graduate Program Chair
Mark Collard
Senior Supervisor
Associate Professor, Archaeology
Mark Skinner
Supervisor
Professor, Archaeology
Brian Chisholm
Internal Examiner
Senior Instructor, University of British Columbia
Date Defended/Approved:
ii
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Revised:Fall2007
Abstract
FORDISC is a computer program designed to determine ancestry from human
skeletal remains. It is widely used, yet its accuracy has been challenged. In this
study, 200 specimens from one of FORDISC's reference samples are used to
investigate four issues that are central to debate: (1) the inclusion ofthe source
population in the reference sample, (2) the influence of sex, (3) the impact of
variable number, and (4) the effect ofdifferent anatomical regions.
The results indicate that the source population must be present and the sex of
the specimen known before FORDISC can provide an accurate determination of
ancestry. Additionally, a determination will be successful only if more than 10
measurements pertaining to multiple anatomical regions are used. Even when
these conditions are met, few determinations may be considered unambiguously
correct. Overall, FORDISC performed below expectations and the results
suggest that the program should be used cautiously.
Keywords: FORDISC; ancestry determination; cranial morphology; forensic
identification; discriminant function analysis
Subject Terms: biological anthropology; forensics; craniometry; skull;
human variation
iii
Acknowledgements
This research could not have happened without the encouragement and
assistance of many people. In particular, Iwould like to thank my supervisor, Dr.
Mark Collard for his generous advice, support and patience throughout this
process. In addition to all of his other duties and responsibilities, he always
seemed to have time for my questions and concerns. Iwould also like to thank
my committee members, Dr. Mark Skinner and Dr. Brian Chisholm, both of whom
took precious time out oftheir summer schedules to read and provide feedback
on this research.
In addition, I am extremely fortunate to have an excellent group of colleagues,
friends and family members. I am especially grateful to Alan Cross, Mana
Dembo, Kevan Edinborough, Luseadra McKerracher and the other members of
the Laboratory of Biological Anthropology whose intelligence, curiosity and
enthusiasm for their research inspired my own efforts. Many thanks also go to my
friends and family for providing valuable comments, welcome distractions and
incalculable kindnesses along the way. Although no words can truly express how
lucky Iam to have them, thanks also go to my parents - their example gives me
something to strive for.
Finally, Iwould like to thank my husband, Robin Elliott. His writing and editing
contributions were invaluable, as were his computer skills when things went
awry. More importantly, his love, support, encouragement and apparently
endless tolerance of my interests (academic and otherwise) are a constant
source ofwonder and admiration to me. I hope I have made him proud.
iv
Table of Contents
Approval ii
Abstract iii
Acknowledgements iv
Table of Contents v
List ofTables vii
List of Figures viii
1. Introduction 1
1.1. Aims and objectives 1
1.2. FORDISC and its applications 3
1.3. The FORDISC debate 6
1.4. Issues investigated 12
1.5. Outline ofanalyses 16
2. Materials and Methods 18
2.1. Data 18
2.2. Analyses 20
3. Results 28
3.1. Impactof including source population and specifying sex 28
3.1.1. Numberofcorrect assignments accepting allposteriorandtypicality
probabilities 28
3.1.2. Numberofcorrect assignments using >0.5posteriorprobabilityand >0.01
typicalityprobability 31
3.1.3. Numberofcorrectassignments using >0.8posteriorprobabilityand >0.01
typicalityprobability 32
3.1.4. Summary 33
3.2. Impactofvariable number 34
3.2.1Numberofcorrect assignments accepting allposteriorandtypicality
probabilities 34
3.2.2 Numberofcorrect assignments using >0.5posteriorprobabilityand>0.01
typicalityprobability 36
3.2.3. Numberofcorrectassignments using >0.8posteriorprobabilityand >0.01
typicalityprobability 38
3.2.4. Variable numberandpopulation differences 39
3.2.4.1. Numberofcorrectassignments accepting allposteriorandtypicality
probabilities 39
3.2.4.2 Numberofcorrectclassifications using >0.5 posteriorprobabilityand >0.01
typicalityprobability 44
3.2.4.3 Numberofcorrectclassifications using >0.8 posteriorprobabilityand >0.01
typicalityprobability 46
3.2.5. Summary 48
3.3 Impactof cranial region 49
v
3.3.1. Numberofcorrect assignments accepting allposteriorandtypicality
probabilities 49
3.3.2. Numberofcorrect assignments using >0.5posteriorprobabilityand >0.01
typicalityprobability 51
3.3.3. Numberofcorrect assignments using >0.8posteriorprobabilityand >0.01
typicalityprobability 53
3.3.4. Cranialregion andpopulation differences 54
3.3.4.1. Numberofcorrect assignments accepting allposteriorandtypicality
probabilities 54
3.3.4.2. Numberofcorrect assignments using >0.5posteriorprobabilityand >0.01
typicalityprobability 58
3.3.5. Summary 61
4. Discussion 62
4.1. Main findings 62
4.2. Implicationsfor use of FORDISC 66
4.3. Future considerations 70
5. Conclusions 74
References 78
Appendix I 86
Appendix II 89
Appendix III 91
vi
List of Tables
Table 1. Total number of test specimens correctly classified (n=200) 30
Table 2. Number oftest specimens correctly classified using >0.5 posterior
probability and >0.01 typicality probability (n=200) 32
Table 3. Number oftest specimens correctly classified using >0.8 posterior
probability and >0.01 typicality probability (n=200) 33
Table 4. Total number of test specimens correctly classified by variable number
(n=200) 36
Table 5. Number oftest specimens correctly classified using >0.5 posterior
probability and >0.01 typicality probability (n=200) 38
Table 6. Numberof test specimens correctly c1assi'fied using >0.8 posterior
probability and >0.01 typicality probability (n=200) 39
Table 7. Results by population accepting all posterior and typicality probabilities
(n=40) 42
Table 8. Results by population using >0.5 posterior probability and >0.01
typicality probability (n=40) 45
Table 9. Results by population using >0.8 posterior probability and >0.01
typicality probability (n=40) 47
Table 10.Total number oftest specimens correctly classified (n=200) 50
Table 11. Number of test specimens correctly classified using >0.5 posterior
probability and >0.01 typicality criteria (n=200) 52
Table 12. Number of test specimens correctly classified using >0.8 posterior
probability and >0.01 typicality probability (n=200) 54
Table 13. Total results for each cranial region by population (n=40) 57
Table 14. Results for each cranial region using >0.5 posterior probability and
>0.01 typicality probability (n=40) 60
Table 15: Range of posterior and typicality probabilities for correct and incorrect
assignments by population 68
vii
List of Figures
Figure 1: Genetic tree for 26 European populations 26
Figure 2: Genetic tree for 33 African populations 26
Figure 3: Genetic tree for 21 Asian populations 27
Figure 4: Genetic tree for 23 American populations 27
viii
1. Introduction
1.1. Aims and objectives
Determining ancestry from skeletonized human remains is an important task for
bioarchaeologists and forensic anthropologists. As part ofa biological profile, this
information is used in a wide range of contexts, including the study ofthe
movements and interactions of past populations, ancestral land claims,
repatriation requests and the investigation of unlawful deaths and human rights
violations (Cox et al. 2006).
Despite attempts to use other skeletal elements (e.g. Marino, 1997; Ballard,
1999; Holliday and Falsetti, 1999: Patriquin et al. 2002), the skull continues to be
regarded as the most reliable area for determining ancestry (Bass 1995). As a
result, both non-metric and metric techniques have been developed to effect
ancestry determinations from the skull. The use of discrete traits, such as the
presence or absence of shoveled incisors, is common. However non-metric
characteristics are not exhaustive or always consistently defined and few
standards exist for their collection (Buikstra and Ubelaker 1994). Additionally,
non-metric methods have been challenged for being more susceptible to inter
observer error (Corruccini 1974).
1