The development of an accurate DNA based identification tool for traded and protected tree

147 Pages·2014·5.12 MB·English

by  Ronny Mukala Kabongo

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How to cite this thesis
Surname, Initial(s). (2012) Title of the thesis or dissertation. PhD. (Chemistry)/ M.Sc. (Physics)/
M.A. (Philosophy)/M.Com. (Finance) etc. [Unpublished]: University of Johannesburg. Retrieved
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The assessment of DNA barcoding as an
identification tool for traded and

protected trees in southern Africa:

Mozambican commercial timber species as a case study

By

Ronny Mukala Kabongo

Dissertation presented in the fulfilment of the requirements for the degree

MAGISTER SCIENTIAE

in

BOTANY

in the

FACULTY OF SCIENCE
DEPARTMENT OF BOTANY AND PLANT BIOTECHNOLOGY

at the

UNIVERSITY OF JOHANNESBURG
SUPERVISOR: PROF MICHELLE VAN DER BANK
CO-SUPERVISOR: DR. OLIVIER MAURIN
January 2014
I hereby declare that this dissertation has been composed by me and work contained
within unless stated otherwise, is my own.

Signed: Ronny Mukala Kabongo
Date: 30 January 2013
Table of Contents

Table of Contents .......................................................................................................................... iii
Abstract ......................................................................................................................................... vi
Acknowledgements ...................................................................................................................... vii
List of abbreviations .................................................................................................................... viii
Chapter 1 ....................................................................................................................................... 1
1. General introduction and objectives ...................................................................................... 1
1.1 Illegal logging and the global timber market .................................................................. 1
1.2 Effects of Illegal logging ................................................................................................. 2
1.3 The fight against illegal logging ..................................................................................... 5
1.4 Convention on International Trade in Endangered Species ............................................ 6
1.5 Other technical means of timber identification ............................................................... 8
1.6 Scale and effectiveness of global response ................................................................... 10
1.7 Molecular genetics tool for wood identification ........................................................... 12
1.8 DNA extraction from wood materials ........................................................................... 13
1.9 DNA Barcoding ............................................................................................................ 16
1.10 Diagnostic markers for land plants.............................................................................. 18
1.11 Tree-BOL Africa initiative .......................................................................................... 21
1.12 Study site: Mozambican forest .................................................................................... 23
1.13 State of Mozambican Forests ...................................................................................... 26
1.14 Research objectives ..................................................................................................... 28
Chapter 2 ..................................................................................................................................... 30
2. Materials and Methods ........................................................................................................ 30

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2.1 Specimen collection and reference samples. ................................................................. 30
2.2 DNA extraction and sequencing. .................................................................................. 31
2.3 Sequence editing, alignment and broad analysis. .......................................................... 33
2.4 Assessment of core DNA barcodes identification efficiency. ....................................... 34
2.5 Phylogenetic analysis .................................................................................................... 36
2.6 Correspondence of query sequences to the database..................................................... 37
Chapter 3 ..................................................................................................................................... 58
3. Results ................................................................................................................................. 58
3.1 Summary statistics ........................................................................................................ 58
3.2 Genetic divergence and barcode gap analyses .............................................................. 59
3.3 DNA barcode identification success rates using distance-based analysis ..................... 60
3.4 Cumulative error and threshold optimization ................................................................ 61
3.5 DNA barcode identification success rates using tree-based analysis ............................ 62
3.6 DNA barcode query assignment ................................................................................... 63
Chapter 4 ................................................................................................................................... 100
4. Discussion ......................................................................................................................... 100
4.1 Development of DNA barcode reference library ........................................................ 100
4.2 Genetic divergence and implication for identification ................................................ 102
4.3 Identification success rates .......................................................................................... 105
4.4 DNA barcoding: practical considerations ................................................................... 107
Chapter 5 ................................................................................................................................... 110
5. Conclusion ............................................................................................................................ 110
6. References ............................................................................................................................. 115
7. Supplementary information ................................................................................................... 131
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Figure 7-1 Specimen illustrations as submitted on BOLD Systems database, an additional
scan of the herbarium voucher specimen is available for every specimen sampled. ........ 138
........................................................................................................................................... 138
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Abstract

Global efforts to protect the world’s forests from unsustainable and inequitable
exploitation have been undermined in recent years by rampant illegal logging in many
timber-producing countries. A prerequisite for efficient control and seizure of illegally
harvested forest product is a rapid, accurate and tamper proof method of species
identification. DNA barcoding is one such a tool, relatively simple to apply. It is
acknowledged to bring about accuracy and efficiency in species identification. In this
study a DNA barcode reference library for traded and protected tree species of southern
Africa was developed comprising of 81 species and 48 genera. Four primary analyses
were conducted to assess the suitability of the core barcodes as a species identification
tool using the R package Spider 1.2-0. Lastly, to evaluate this identification tool, query
specimens independently sampled at a Mozambican logging concession were identified
using DNA barcoding techniques. The nearest neighbour (k-NN) and best close match
(BCM) distance based parameter yielded 90% and 85% identification success rate using
the core plant barcodes respectively. DNA barcoding identification of query specimens
maintained a constant 83% accuracy over the single marker dataset and the combined
dataset. This database can serve as a backbone to a control mechanism based on DNA
techniques for species identification and also advance the ability of relevant authorities
to rapidly identify species of timber at entry and exit points between countries with
simple, fast, and accurate DNA techniques.

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Acknowledgements

The work presented in this document would not have been possible without the
contribution of a small group of people; therefore, I would like to take this opportunity
to acknowledge them. First and foremost I would like to express my appreciation to my
research supervisor prof. Michelle Van der Bank for her constant guidance and
enthusiastic encouragement. Your kind, patient and enthusiastic nature is admirable and
they are life skills I wish to take with me. Secondly, I wish to sincerely thank my co-
supervisor Dr. Olivier Maurin for his contribution and assistance during the planning
and development of this research project. Your constant willingness to give your time
so generously has been very much appreciated. I am further indebted to my colleagues
both past and present at the African Centre for DNA Barcoding. Your support and
motivation kept me going. I would also like to thank my family who have been by my
side throughout my studies.

Lastly I thank the Government of Canada through Genome Canada and the
Ontario Genomics Institute (2008-OGI-ICI-03), the International Development
Research Centre (IDRC), Canada and the University of Johannesburg for financial
support.

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List of abbreviations

°C = Degree Celsius
ABI = Applied Biosystems, Inc.
ACDB = African Centre for DNA Barcoding
BL = Bayesian Likelihood
BLAST = Basic Sequence Alignment Search Tool
BOLD = Barcode of Life Datasystems
BP = Bootstrap Percentages
CBOL = Consortium for the Barcode of Life
CCDB = Canadian Centre for DNA Barcoding
CITES = Convention on International Trade in Endangered Species
CTAB = Hexadecyltrimethylammonium Bromide
DNA = Deoxyribonucleic Acid
F = Forward Primer
FSC Forest Stewardship Council
g = Gram
GenBank (NCBI) = National Centre for Biotechnology Information
Inc = Incorporated
JRAU = Herbarium of the University of Johannesburg (UJ)
matK = Maturase K
min = Minute(s)
MUSCLE = Multiple Accurate and Fast Sequence Comparison by Log-Expectation
NJ = Neighbour Joining
No = Number
NRF = National Research Foundation
PAUP = Phylogenetic Analysis Using Parsimony Software Program
PCR = Polymerase Chain Reaction
PP = Posterior Probabilities
PVP = Polyvinyl Pyrolidone
R = Reverse Primer
rbcLa = Subunit ‘a’ of Ribulose-Bisphosphate Carboxylase
sec = Second
TBR = Tree-Bisection-Reconnection
psbA-trnH = Spacer between trnH and psbA Genes
UJ = University of Johannesburg
USFDA = U.S. Food and Drug Administration

Page | viii
Chapter 1
1. General introduction and objectives
1.1 Illegal logging and the global timber market
Global efforts to protect the world’s forests from unsustainable and inequitable
exploitation have been undermined in recent years by rampant illegal logging in many
timber-producing countries. According to Seneca Creek Associates, Wood Resource
International (2004), illegal logging occurs in the instance of theft, harvesting without
the required approvals, purchase, sale, acquisition of harvesting or trading
authorisations through corruption, the trade or processing of timber in breach of national
and international laws. The immediate impacts of illegal logging include loss of
biodiversity, soil erosion, water pollution, forest fires, flash flooding and landslides
(Lawson and MacFaul 2010). Furthermore, illegal logging also threatens the livelihoods
of millions of forest dependent people and starves cash-strapped governments of
billions of dollars in revenue. It undermines the rule of law, promotes forest corruption,
plus creates and fuels armed conflict in developing nations (Global Witness 2002).

To date the extent and size of illegal logging remains debatable, however, it is
universally accepted that some form of illegal logging takes place in all producing
countries. According to most forest associations, illegal logging represents 10% of the
total global harvest (American Forest & Paper Association 2009). This estimation is
based on documented incidents, despite the fact that most illegal logging activities go

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