Phylogenetic Analysis of Plant Community Assemblages in the Kruger National Park, South Africa
PHYLOGENETIC ANALYSIS OF PLANT COMMUNITY ASSEMBLAGES IN THE KRUGER NATIONAL PARK, SOUTH AFRICA
By
KOWIYOU YESSOUFOU
Thesis submitted in fulfilment of the requirements for the degree
PHILOSOPHIAE DOCTOR
In
BOTANY
In the
Faculty of Sciences
At the
University of Johannesburg
Supervisor: Prof. Michelle van der Bank
Co-supervisor: Prof. Vincent Savolainen
January 2012 Declaration
Declaration
I declare that this thesis has been composed by me and the work contained within, unless otherwise stated, is my own
------K. Yessoufou (January 2012) Table of contents
Table of contents……………………………………………………………………i
Abstract…………….………………………………………………………………..v
Acknowledgements..……………………………………………………...... vii
List of tables………………………………………………………………………..ix
List of figures……………………………………………………………………….x
Dedication…………………………………………………………………………xiii
List of abbreviations……………………………………………………………..xiv
Table of contents
Table of content
Chapter 1- General introduction…………………………………………………..1
1. Savanna ecology…………………………………………………………………1
1.1. Features of tropical savanna………………………………………………….1
1.2. Role of fire in savanna ecology……………………………………………….3
1.3. Importance of herbivory in savanna ecology………………………………..3
2. Community ecology in a savanna context……………………………………..4
2.1. Theories of tree-grass coexistence…………………………………………..4
2.2. Tree-tree coexistence………………………………………………………….5
3. Phylogenetic investigation of community assembly…………………………..6
3.1. The use of DNA barcoding techniques to reconstruct phylogenetic tree…………………………………………………………………………………….6
3.2. Rationale of phylogenetic approach in ecology……………………………..7
3.3. Framework in community phylogenetics…………………………………….8
3.4. Emerging patterns in community phylogenetics…………………………..10
4. Study site and objectives of the study………………………………………...12
4.1. Study site………………………………………………………………………12
4.2. Objectives……………………………………………………………………...18
4.3. Outline of the thesis…………………………………………………………..19
Chapter 2- The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes…………………….………………………………………...23
1. Introduction……………………………………………………………………..23
2. Materials and methods………………………………………………………..25
i Table of contents
2.1. Sample collection…………………………………………………………….25
2.2. DNA extraction, PCR and sequencing……………………………………..25
2.3. Sequence alignment………………………………………………………….27
2.4. Tree reconstruction…………………………………………………………...28
3. Results…………………………………………………………………………..29
3.1. Tree statistics……………………………………………………………….29
3.2. Phylogeny of trees and shrubs of the KNP……………………………...30
4. Discussion………………………………………………………………………45
4.1. Clade 1: Fabidae…………………………………………………………...48
4.2. Clade 2: Malvidae………………………………………………………….50
4.3. Clade 3: Vitales…………………………………………………………….52
4.4. Clade 4: Santalales………………………………………………………..52
4.5. Clade 5: Lamiidae………………………………………………………….53
4.6. Clade 6: Campanulidae……………………………………………………55
4.7. Clade 7: Ericales…………………………………………………………...55
4.8. Clade 8: Caryophyllales…………………………………………………...56
4.9. Clade 9: Basal Eudicots…………………………………………………..56
4.10. Clade 10: Magnoliidae…………………………………………………….57
4.11. Clade 11: Monocotyledoneae…………………………………………….57
5. Conclusion………………………………………………………………………58
Chapter 3- Testing suitability of evolutionary models using traits of woody plants in the KNP…………………………………………………………………..59
1. Introduction..……………………………………………………………………59
2. Materials and methods….…………………………………………………….61
ii Table of contents
2.1. Plant ecological traits………………………………………………………61
2.2. Data collection and measurements………………………………………63
2.3. Data analysis……………………………………………………………….64
3. Results…………………………………………………………………...... 65
4. Discussion………………………………………………………………………71
5. Conclusion………………………………………………………………………74
Chapter 4- Characterising diversity and phylogenetic structure of woody plant communities in the Kruger National Park, South Africa………………………..75
1. Introduction……………………………………………………………………..75
2. Materials and methods………………………………………………………...77
2.1. Dataset………………………………………………………………………77
2.2. Data analysis……………………………………………………………….78
3. Results…………………………………………………………………………..82
3.1. Overall diversity and community structure in the KNP.………………..82
3.2. Community phylogenetic structure within and among sites…………...84
3.3. Community trait-based structure within and among sites……………..86
4. Discussion………………………………………………………………………86
5. Conclusion………………………………………………………………………89
Chapter 5- The role of megaherbivores in shaping the structure of subtropical plant communities...………………………………………………………………..90
1. Introduction……………………………………………………………………..90
2. Materials and methods………………………………………………………..94
2.1. Study site: exclosures……………………………………………………..94
iii Table of contents
2.2. Traits of anti-herbivores defences………………………………………..96
2.3. Community sampling in the exclosures………………………………….96
2.4. Statistical analyses………………………………………………………...97
3. Results…………………………………………………………………………..98
4. Discussion……………………………………………………………………..103
4.1. Exclusion of megaherbivores and plant diversity………………………..104
4.2. Exclusion of megaherbivores and phylogenetic diversity………………105
5. Conclusion…………………………………………………………………….106
Chapter 6- General conclusion…..…………………………………………….108
1. Major findings, discussion and contribution to literature..………………..108
1.1. Phylogenetic information database for the KNP………………………108
1.2. Ornstein-Uhlenbeck is more suitable for phylogenetic comparative
analysis of plant traits in the KNP………………………………………109
1.3. Plant community assemblages in the KNP are not neutral………….109
1.4. Megaherbivores leave distinct signature on plant community
structure…………………………………………………………………...109
2. Future challenges…………………………………………………………….111
Chapter 7- References…………………………………………………………..114
Supplementary Information…………………………………………………...139
Appendix- Submitted paper…………………………………………………….176
iv Abstract
Abstract
What underlies species distribution and species coexistence has long been of key
interest in community ecology. Several methods and theories have been used to
address this question. However, it still remains a controversial debate. The recent development of plant DNA barcodes with possibility of merging phylogeny with ecology brings high expectation in uncovering the processes underlying community assemblages. Previous works that used molecular approach in community ecology focused mainly on rainforests. Using a phylogenetic approach, this study brings novel understandings about savanna ecology, especially regarding how megaherbivores impact plant community composition.
The Kruger National Park (KNP) is one of the world’s largest reserves, but less studied from a phylogenetic perspective. A DNA database of 445 DNA sequences
(plant DNA barcodes, rbcLa + matK) was generated for the woody plants of the KNP.
This database proves reliable in reconstructing the phylogeny of Angiosperms of the
park. Based on this phylogeny, the present study characterised plant community
composition, and investigated how megaherbivores influence this composition.
Results indicate that plant communities in the KNP are not neutral, i.e. they are more
clustered than expected under various null models. This suggests that ecological
forces, most likely habitat filtering may be playing key role in dictating community
structure in the KNP. The KNP is well-known for its richness in megaherbivores. The
contribution of these animals to the current shape of plant community structures was
therefore further investigated. Where megaherbivores have been excluded, plant
diversity decreases, but shifts in plant community structure are contingent upon the
initial community composition, suggesting that herbivory might be important filter that
drives the clustering pattern observed.
v Abstract
These results also have important implications for management of African
woodlands, particularly given the continental decline in megaherbivores. As large
herbivores are lost from these ecosystems, one can predict a subsequent reduction
in plant diversity, whilst the impact on plant community structure will depend upon
the initial composition. Critically, I also show that the loss of phylogenetic diversity (a
surrogate for functional diversity) will depend on the relative shifts in phylogenetic
community structure, information that has never been considered before in
management strategy.
Key words: Community phylogenetics, functional diversity, species coexistence,
under/overdispersion, evolutionary models, megaherbivores, conservation,
extinction, Kruger National Park, South Africa.
vi Acknowledgements
Acknowledgements
This project was financially supported by the University of Johannesburg (UJ,
South Africa), the National Research Funds (NRF, South Africa), the
Government of Canada through Genome Canada and the Ontario Genomics Institute
(2008-OGI-ICI-03), and the Royal Society (UK). The massive contribution of the
Canadian Centre for DNA Barcoding (CCDB) through assistance in DNA
sequencing is also acknowledged.
I am grateful to my supervisors Prof. Michelle van der Bank (UJ, South
Africa) and Prof. Vincent Savolainen (Imperial College London, UK) for the
wonderful roles they played not only during the run of the project, but also for
their guidance that leads to the production of this dissertation. Dr T. Jonathan
Davies (McGill University, Canada) also provided invaluable assistance.
The University of Johannesburg through the Department of Botany
provided excellent work and social environment and must receive here my
gratitude. I would like to thank researchers from UJ, especially Dr Motsi
Cynthia Moleboheng (former PhD student), who took me through the process of molecular works, and Dr Olivier Maurin for his wonderful knowledge of the flora of the Kruger National Park, and all the current students of the African
Centre for DNA Barcoding and the Department of Botany at UJ.
I would also like to thank researchers from Imperial College London,
UK for their various helps during my multiple visits to the Ecology and
Evolution Section. I particularly thank Dr Martyn Powell, Dr Hanno Schaefer
(now at Harvard University, USA), Dr Guillaume Besnard (now at the
vii Acknowledgements
Universite’ de Toulouse, France) and Dr Alex Papadopulos (now postdoc at
Vincent’s lab, Imperial College London, UK).
Special thank to the Kruger National Park in general and specifically to
the Scientific Services for allowing the project and providing field assistance.
I would also like to acknowledge the wonderful opportunities offered to
me by various Institutions from around the world who invited me to attend,
learn and share findings of this project during different workshops and
conferences. I would like to mention the University of Johannesburg (SA, for
the annual symposium), Imperial College London, UK, for the molecular and
computing statistics courses, National Institute for Mathematical and
Biological Synthesis (NIMBioS, University of Tennessee, USA) for the
workshop on high performance computing in phylogenetics, Mathematical
Biosciences Institute (MBI, Ohio State University, USA), Centre for Discrete
Mathematics and Theoretical Computer Science (DIMACS, Rutgers
University, USA) and the School of Mathematics (University of Nairobi,
Kenya) for the workshop on Conservation Biology at the Kenya Wildlife
Services Training Institute, Rhodes University (South Africa) for the 9th
conference of the Southern African Society for Systematic Biology, and the
University of Pretoria (South Africa) for the 38th conference of South African
Association of Botanists. All these conferences and workshops provided me
with useful information to understand various concepts surrounding
conservation and molecular biology.
Special and very warm thank to Semiyou Abdou Rafiou and his wife
Souradjatou Rafiou for their wonderful and exceptional familial assistance during my stay in South Africa. I am also highly indebted to Mariam O.
viii Acknowledgements
Yessoufou and Roees O. Yessoufou for their special support. Thanks to
Karim Affolabi, my entire family in Benin and all my South African friends.
ix List of tables
List of tables
Chapter 1
Table 1 Summary of systems included in current phylogenetic structure (as reviewed
in Vamosi et al. 2009)……………………………………………………………………...11
Table 2 Megaherbivores in the KNP, abundance and dietary behaviour…………….17
Chapter 2
Table 1 Characteristics of each partition obtained from PAUP*……………...... 29
Table 2 Comparison of bootstrap values of major sub-(clades) in this study with those of Soltis et al. (2011)………………………………………………………………..47
Chapter 3
Table 1 Tests of phylogenetic signal in the studied traits based on Pagel and
Blomberg’s statistics……………………………………………………………………….66
Table 2 Test of phylogenetic signal in the studied traits based on Abouheif’s statistics……………………………………………………………………………………..68
Table 3 Models comparison of traits evolution of woody plants using AIC test…………………………………………………………………………………...... 70
IX List of figures
List of figures
Chapter 1
Figure 1 Interpretation of niche theory based on habitat filtering process. This is an illustration of Webb et al.’s framework (Webb et al. 2002)………………………..…….9
Figure 2 Frequency of different possible structures of community assemblages in literature (Vamosi et al. 2009)…………………………………………………………….11
Figure 3 Comparison of frequency of study sites (temperate vs. tropical) of community phylogenetics (as reviewed in Vamosi et al. 2009)……………………….12
Figure 4 Various habitats in the Kruger National Park………………………...... 13
Figure 5 Major vegetation types in the Kruger National Park………………...... 15
Figure 6 Giraffe during feeding activity in the KNP…………………………………….16
Figure 7 Diagram indicating the structure of the thesis………………………………..19
Chapter 2
Figure 1 Representation of steps of DNA sequencing at the Canadian Centre for
DNA Barcoding……………………………………………………………………………..27
Figure 2 The maximum likelihood phylogeny of trees and shrubs occurring in the
KNP…………………………………………………………………………………………35
Figure 3 The maximum parsimony majority rule consensus phylogeny of trees and shrubs occurring in the KNP……………………………………………………………..44
x List of figures
Figure 4 Summary tree of the maximum parsimony majority-rule consensus analysis of trees and shrubs occurring in the KNP………………………………………………..44
Figure 5 Comparison of the phylogenetic tree of Angiosperm of the KNP…………..46
Chapter 3
Figure 1 Illustration of Brownian motion (BM) model (A) and Ornstein-Uhlenbeck
(OU) model (B) of trait evolution………………………………………………………….61
Figure 2 Values of Blomberg’s K (dashed red line) for each trait…………………….67
Figure 3 Abouheif test of phylogenetic signal in the studied traits…………...... 69
Chapter 4
Figure 1 General pattern of plant diversity and phylogenetic structure along a south- north gradient in the KNP………………………………………………………………….84
Figure 2 Values of observed phylogenetic species variability (PSV) within communities (dashed red line)……………………………………………………………85
Chapter 5
Figure 1 Relationships between plant phylogeny and megaherbivory……………….92
Figure 2 Map of the KNP showing major soil types, location of plots and exclosures…………………………………………………………………………………...95
Figure 3 Location of KNP-plots (small circles) and exclosures (squares) along a south-north transect in the Park…………………………………………………………..98
xi List of figures
Figure 4 Comparison of plant diversity and phylogenetic structure between all
exclosures KNP-plots……………………………………………………………...... 99
Figure 5 Pairwise comparison of plant diversity between each exclosure and its
adjacent area………………………………………………………………………………100
Figure 6 Pairwise comparison of plant community structure between each exclosure
and its adjacent area……………………………………………………………………..101
Figure 7 Patterns of plant diversity according to age of exclosures contrasted with pattern in the 110 KNP-plots…………………………………………………………….102
Figure 8 Patterns of plant diversity according to treatment contrasted with pattern in the 110 KNP-plots………………………………………………………………………..103
xii Dedication
Dedication
To:
Roees O. Yessoufou; Mariam O. Yessoufou Nouratou A. Yessoufou
xiii List of abbreviations
List of abbreviations
ACDB African Centre for DNA Barcoding
AIC Akaike information criterion
APG Angiosperm Phylogeny Group
BM Brownian motion model
BOLD Barcoding of Life Database
BS Bootstrap support percentage
CBOL Consortium of Barcoding of Life
CCDB Canadian Centre for DNA Barcoding
CI Consistency index
Cij Schoener's index of co-occurrence
c.i. Confidence interval
CIPRES Cyberinfrastructure for Phylogenetic Research
DNA Deoxyribonucleic acid
xiv List of abbreviations
EB Early-burst
EBI European Bioinformatics Institute
e.g. exempli gratia (for example)
F Forward primer
Full Full exclosure
GenBank National Centre for Biotechnology Information
GTR + Γ General Time Reversible + Gamma + Proportion
Invariant
H Shannon diversity index
ha hectare
ID Identity
IST among-site differences in species frequencies
JRAU Herbarium of the University of Johannesburg (UJ),
Johannesburg, South Africa
K Blomberg’s metric of phylogenetic signal
xv List of abbreviations
km kilometre
KNP Kruger National Park
LA Leaf area
LDM Leaf dry mass
LDMC Leaf dry matter content
LFM Leaf fresh mass
LT Leaf thickness
m meter
m2 square meter
matK Maturase K
ML Maximum likelihood
MP Maximum parsimony
MUSCLE Multiple Sequence Comparison by Log-
Expectation program
xvi List of abbreviations
NRI Net relatedness index
OU Ornstein-Uhlenbeck
Partial Partial exclosure
PAUP Phylogenetic analysis using parsimony
PCA Phylogenetic comparative analysis
PCR Polymerase chain reaction
PD Phylogenetic diversity
PST Gain of phylogenetic divergence among species
PSV Phylogenetic species variability index
R Reverse primer
RAxML-VI-HPC Randomized Axelerated Maximum Likelihood for
High Performance Computing
rbcLa ribulose-1,5-bisphosphate carboxylase/oxygenase
large subunit ‘a’
RI Retention index
xvii List of abbreviations
RC Rescaled index
SI Supplementary information
SLA Specific leaf area
SR Species richness
Std Standard deviation
t Student t test
UJ University of Johannesburg, South Africa
vs. Version
WD Wood density
xviii Chapter 1 General introduction
Chapter 1
General introduction
1. Savanna ecology
1.1. Features of tropical savannas
Tropical savannas occur often under climatic conditions where there are strongly seasonal rainfall patterns (Frost 1996). They are structured by a continuous grass layer in which trees and shrubs are sometimes scattered (Nangendo et al. 2006).
Meanwhile savanna landscape may be interspersed with riparian or gallery forests, or patches of woodland, swamps or marshes (e.g. Kruger National Park; Scholes &
Walker 1993).
Globally, savannas cover approximately 20% of the land surface, produce around 30% of global net primary productivity and provide opportunities for cultural and economic activities (Scholes & Walker 1993). Tropical savannas represent about an eighth of the world land area (Scholes & Hall 1996) and account for over half the area of Africa and Australia, 45 % of South America, and 10% of India and
Southeast Asia (Werner 1991). In addition, savanna areas harbor a large proportion of the world’s human population and a majority of its rangelands and livestock
(Scholes & Archer 1997). This situation leads to a high pressure on savanna biome, mostly due to agricultural activities (Scholes & Archer 1997).
Savanna biome is widely distributed in northern and eastern South Africa where it is referred to as “bushveld” (Schmidt et al. 2007). Bushveld is savanna woodland dominated by woody vegetation (trees and shrubs) varying from an open to dense structure with a grassy understorey (Schmidt et al. 2007). Woody species
1 Chapter 1 General introduction
play key roles in the overall ecological function of tropical savannas. They influence
physiological function such as transpiration and production rates and nutrient cycling
and environmental conditions such as soil erosion, and hydrology (Hochberg et al.
1994). The presence or absence of trees and shrubs and their abundance are the
major criteria that assist not only to distinguish between savanna types, but also to differentiate savanna structure and functions from that of forest and desert biomes
(Scholes & Walker 1993; Burgess 1995; Solbrig et al. 1996). Based on these criteria, savanna can be referred to as tree savannas, shrub savannas or savanna woodlands (Menaut et al. 1990), as opposed to grassland where there are no
trees/shrubs or there are a very few that are widely scattered. However, another type
of savanna could be defined as savanna parkland which is a two-phase mosaic
landscape in which circular clumps, groves of woody plants are dispersed throughout
a grassy matrix (Menaut et al. 1990; San Jose’ et al. 1991).
Tropical savannas are maintained through a complex interaction between
climate (e.g. water availability, rainfall), fire, herbivory, tree growth, and plant
competition, which operate at different scales (Scholes & Archer 1997; Bond 2005;
Bond & Keeley 2005; Biaou 2009). A substantial body of literature has addressed the
individual and interactive effects of these different drivers on savanna structure (e.g.
Biaou 2009; but see Scholes & Archer 1997 for a comprehensive review). The
composition, structure and dynamic of savanna are shaped under fire (Bond & Van
Wilgen 1996) and herbivore pressures (McPherson 1993). The importance of these
two factors (fire and herbivory) in savanna ecology rests on the fact that they are
responsible for the coexistence of two apparently stable states (grassland and
woodland states) that make savanna a particular biome.
2 Chapter 1 General introduction
1.2. Role of fire in savanna ecology
Fire proneness is a well-known feature of African savannas (Bond & Van Wilgen
1996; Anderson et al. 2003). Fire controls the structure of savanna in a specific way way (Scholes & Archer 1997). During the dry seasons, grassland experiences periodic fire that burns grasses, kills vulnerable tree seedlings and eventually prevents trees from dominating. In doing so, fire ensures the maintenance of the grassland in grassland state.
In contrast, in savanna woodlands, the relatively high density of trees reduces the amount of grasses that can grow. The reduction of grass biomass limits considerably the fire that can burn. As a result, adult trees are safe and many small trees can grow, thus maintaining the woodland in a woodland state. Meanwhile, a frequent occurrence of fire within short intervals at high temperature is damaging for savanna. Frequent fires could kill all trees and force savanna woodland to shift into a grassland state while the absence of fire causes grassland to shift to closed woodlands (Scholes & Archer 1997; Higgins et al. 2000; Bond & Archibald 2003;
Govender et al. 2006) and even to forest (Hopkins 1992). Controlling fire intensities and its frequency is therefore key strategy for the management of savanna resources. The overall importance of fire resides in that it maintains a dynamic balance between savanna and forest.
1.3. Importance of herbivory in savanna ecology
Megaherbivores (e.g. elephants, giraffes, rhinos, etc.) play also a crucial role in shaping savanna landscape (Du Toit et al. 2003; Bond 2005). They eat new tree seedlings that grow in grassland, thus contributing to maintain grasslands in a grassland state.
3 Chapter 1 General introduction
Indeed, seedlings and small trees are particularly vulnerable to herbivory from
browsers or grazing herbivores, which trample them or consume them along with
grasses (Borchert et al. 1989; Sankaran et al. 2005). By consuming grasses, grazers reduce the fuel load (i.e. grasses) and hence reduce fire intensity, frequency and its capacity to spread (Baisan & Swetnam 1990; Savage & Swetnam 1990). The reduction of fire effects due to grazing activities favours the release of small trees.
The increase in abundance of small trees could lead to an increase of population size of megaherbivores. At the same time, due to browsing pressures, megaherbivores limit tree growth. When grazing and browsing animals are of little consequence, fire may operate more directly to influence tree-grass mixtures and may slow, but not prevent, complete tree domination (Hochberg et al. 1994). Thus,
grazer-browser-fire interactions influence strongly savanna ecosystems.
These interactions impose the directionality of savanna dynamic. Forests
change through succession process (after being logged or disturbed), with grasses
growing in, followed by bushes, and then a chain of different trees until some
dominant tree species finish the sequence. In contrast, savannas generally do not
undergo succession, but will switch back and forth between grassland and woodland
without any intermediate stage.
2. Community ecology in a savanna context
2.1. Theories of tree-grass coexistence
What allows species to coexist is one of the major questions that always generate a great deal of interest in ecology. The question turns out to be even more exciting when it comes to investigating how two contrasting life-forms (trees and grasses)
could assemble. Absence of niche overlap in resource-use (especially water) could
4 Chapter 1 General introduction
allow such co-existence. Grass roots uptake water from surface soil horizons
whereas tree roots preferentially explore and utilise resources from deeper soil
horizons. This rooting niche separation between grasses and trees results in
absence of competition for resources, thus allowing species coexistence (Webb et
al. 2002; but see Scholes & Archer 1997 for detailed review). Another possible
explanation could be linked to the frequent disturbances occurring in African
savanna (e.g. fire, herbivory). These disturbance events prevent the competitive
ability of one life-form from excluding the other (Scholes & Archer 1997).
Meanwhile, despite these insights, many challenges still remain. For example,
the evolutionary relationships among coexisting species are shown to be critical in
the assembly process (Webb et al. 2002; Hardy & Senterre 2007), but how the
evolutionary history of taxa contributes to the current shape of community
assemblages, especially in tropical savanna ecosystems, is still a debated question.
Assembly process might be driven by neutral forces such as demographic drift, dispersal and speciation (neutral theory; Bell 2001; Hubbell 2001; Chave 2004), but niche differentiation is also likely to play key role in shaping community composition
(deterministic theory; Webb et al. 2002). The increasing availability of molecular DNA data opens ways of weighing the relative importance of neutral vs. niche parameters in assembly processes.
2.2. Tree-tree coexistence
In savannas, coexistence of trees has traditionally been attributed to two major forces: facilitation and competition (Scholes & Archer 1997; Biaou et al. 2011).
Facilitation is the process by which a tree species (nurse species) creates locally conditions that favour the establishment of other species which could not persist in
5 Chapter 1 General introduction
the ecosystem in the absence of the nurse tree (Horton & Hart 1998). It is a process
that dominates in harsh ecological conditions (Horton & Hart 1998) such as extreme
temperature, water-deficit, etc.
In contrast, competitive interactions tend to allow a species to exclude others
from the community. Although these two forces play important roles in species
coexistence, one question remains: how to tease apart the effect of each of them in
natural communities (Cahill et al. 2008; Mayfield & Levine 2010).
3. Phylogenetic investigation of community assembly
3.1. The use of DNA barcoding techniques to reconstruct phylogenetic tree
Species identification using morphological characters is well established and widely
used in systematic biology. However, this approach can become challenging due to
phenotypic plasticity, genetic variation in the trait used, and morphological variability
over life cycle (Hebert et al. 2003). It can also mislead especially when it comes to
the identification of small fragments (root, leaf, bark etc.).
DNA barcoding approach is currently increasingly acknowledged as a potential solution to overcome these challenges (Lahaye et al. 2008; CBOL Plant
Working Group 2009). DNA barcoding has been reputed not only as a tool for species identification, but also for species discovery (Lahaye et al. 2008). It is expected to provide accurate, rapid and automatable species identification without
morphological knowledge. It works by means of comparing the DNA sequences from
a small fragment of the genome that is standardised between groups of organisms,
with the aim of contributing to a wide range of ecological and conservation studies in
which traditional morphological identification is not practical (Hebert et al. 2003;
6 Chapter 1 General introduction
Armstrong & Ball 2005; Markmann & Tautz 2005; Savolainen et al. 2005; Smith et al.
2005; Ardura et al. 2010).
Given the performance of DNA barcoding in species identification, it is a suitable technique for phylogeny reconstruction. Two regions, matK and rbcLa are
identified as DNA barcodes for land plants (Lahaye et al. 2008; CBOL Plant Working
Group 2009). These two regions are used in this study to reconstruct evolutionary
history that connects woody plants occurring in the KNP.
3.2. Rationale of phylogenetic approach in ecology
Several observations have contributed to the current increasing use of phylogeny in
ecology. Firstly, close relatives occur less frequently in local communities (Gotelli &
Graves 1996; Cavender-Bares et al. 2004; Ackerly et al. 2006). Secondly, the number of species per genus is generally lower in small areas than in larger areas
(Elton 1946). Thirdly, close relatives tend to share similar traits (Darwin 1859;
Felsenstein 1985a; Webb et al. 2002). These observations lead to the conclusion
that there is a phylogenetic driver of community structure. Therefore, the basic
rationale of comparative analysis is that the genetic relatedness of coexisting
species along with the evolutionary pattern of species traits can lead to the dominant
process shaping community structure (Webb et al. 2002; Cavender-Bares et al.
2004; Ackerly et al. 2006; Kembel & Hubbell 2006; Silvertown et al. 2006; Webb et
al. 2006; Hardy & Senterre 2007; Johnson & Stinchcombe 2007; Emerson &
Gillespie 2008; Hardy 2008; Cavender-Bares et al. 2009).
Currently, the use of molecular phylogenetics to investigate patterns in
community structure has largely focused on rainforests (e.g. Webb 2000; Chazdon et
al. 2003; Kembel & Hubbell 2006; Swenson et al. 2006; Webb et al. 2006) with little
7 Chapter 1 General introduction emphasis on tropical savannas, despite the fact that savanna and forest biomes represent two distinct systems (Hoffmann et al. 2005).
3.3. Framework in community phylogenetics
The use of phylogeny in ecology is undergoing an exciting development (Chazdon et al. 2003; Kembel & Hubbell 2006; Swenson et al. 2006; Webb et al. 2006; Cavender-
Bares et al. 2009; Schaefer et al. 2011). Webb et al. (2002) provided a basic framework, which is guided by Darwin’s (1859) assumption that close relatives could not coexist due to high competitive exclusion. This theoretical basis of the framework results in two forces i.e. habitat filtering and competition shaping community structure (Webb et al. 2002). Habitat filtering is expected to drive either phylogenetic clustered community when species traits are conserved or overdispersed when traits are convergent (Figure 1). Meanwhile, when taxa are very similar ecologically and physiologically (due to close phylogenetic relatedness), there is overlap in resource- use (niche overlap), resulting in high competitive interactions. Competitive exclusion should lead to community overdispersion when traits are conserved, but to a random pattern when traits are convergent.
However, additional forces have been showed to play important roles in assembly processes, especially facilitation and mutualism (Bruno et al. 2003;
Valiente-Banuet & Verdu 2007; Elias et al. 2009). The most recent limitation to
Webb’s framework was pioneered by Cahill et al. (2008) and Mayfield & Levine
(2010) who demonstrated that competition is not always strong among close relatives, and that it can also drive clustering pattern.
8 Chapter 1 General introduction
Regional pool Habitat filters Local communities
A
B
C
Figure 1 Interpretation of niche theory based on habitat filtering process. This is an
illustration of Webb et al.’s framework (Webb et al. 2002). Each filled circle indicates
a distinct species; and each colour corresponds to a specific trait. Community composition is determined by both phylogenetic relatedness and species traits.
During the process, habitat filters (vertical bar in the centre) filter out species lacking
9 Chapter 1 General introduction
a particular trait preventing them to occur in local communities (e.g. red and black
species in A and red and green species in B), while filtering in others exhibiting compatible traits with the available niches in local communities (green species in A, and black in B). Local communities in both cases (A and B) are, as a result, composed of species closely related on the phylogeny (regional pool) and sharing similar traits (trait conservatism or phylogenetic signal). Such communities are clustered or underdispersed. In scenario C, taxa less related on the phylogeny and exhibiting different traits (convergent traits) are filtered in, leading to communities composed of species with various evolutionary histories. Such communities are overdispersed or even.
3.4. Emerging patterns in community phylogenetics
Vamosi et al. (2009) recently reviewed studies that investigated assembly processes from a phylogenetic perspective. This review shows an increase of interest in phylogenetic study of community ecology, and highlighted three important aspects.
Firstly, the review reveals that three possible expectations of community
structure (clustering, overdispersion and random distribution) are documented in
literature, with clustering emerging as the most frequently observed pattern (Figure
2).
10 Chapter 1 General introduction
15% Clustering (e.g. Hardy 2008)
Eveness (e.g. Ackerly et al. 2006) 26% 59% Random (e.g. Silvertown et al. 2006)
Figure 2 Frequency of different possible structures of community assemblages in literature (Vamosi et al. 2009).
Secondly, the review shows that various systems have been studied, but plants attracted the most attention (Table 1). Within plant systems, rainforests are the most dominant (20%) while savannas have received little attention (4%)
(Table1).
Table 1 Summary of systems included in current phylogenetic structure (as reviewed in Vamosi et al. 2009)
Systems Microbes Plants Animals Frequency in 20.51 64.1 15.39 literature (%) Sub-systems Rainforests Savanna Various Arthropods Vertebrates (Grassland) trees Frequency in literature (%) 20 4 76 33.34 66.66
Thirdly, the majority of these studies were conducted in temperate regions
11 Chapter 1 General introduction
(72%) and only 28% were piloted in tropical biomes (Figure 3). Given that savannas
receive less interest, a better understanding of the phylogenetic structure of savanna
plant communities is urgently needed. For this reason, the current study can be seen
as a contribution to fill this gap, since it is based in the KNP which is a subtropical
African woodland savanna in South Africa.
28.2
Temperate Tropical
71.8
Figure 3 Comparison of frequency of study sites (temperate vs. tropical) of
community phylogenetics (as reviewed in Vamosi et al. 2009)
4. Study site and objectives of the study
4.1. Study site
The KNP is situated in the north-eastern part of South Africa between 22°25’ and
25°32’ S and 30°50’ and 32° E. It is part of the ‘Greater Maputaland-Pondoland-
Albany’ biodiversity hotspot (Perera et al. 2011). Rainfall varies from 440 mm in the north to 740 mm in south (Venter 1990). Mean annual temperature is around 21-
23°C, but in summer temperatures often exceed 38°C, whereas frost can occur sporadically during Winter.
12 Chapter 1 General introduction
The KNP is one of the largest natural reserves (20,000 km2) in Africa. It is a
‘woodland biome’ of southern Africa (Schmidt et al. 2007) with various habitats found
within its boundary (Figure 4).
A
C B
E D
G F
13 Chapter 1 General introduction
Figure 4 Various habitats in the KNP (Photos from O. Maurin): A = View from
Shabeni Hill in the Pretoriuskop section (southern KNP), with from left to right Ship,
Newu and Sitfungwane Mountains. The Pretoriuskop section is an area rich in tree and shrub species, and several species are restricted to these hills in their distribution in KNP; B = View on the Biyamiti river (southern KNP, Malelane section).
Many river systems crossed the KNP. Riparian vegetation host many specific trees and shrubs; C = View of the Lebombo mountains in their northern range. The
Lebombo mountains represent a natural border between South Africa and
Mozambique and are host to a wide number of trees and shrubs; D = View in foreground on the southern low rolling hills and on background on the Malelane mountain (southern KNP, Malelane section). The Malelane mountain presents the highest point in the KNP at 840 m. E = View on the Luvuvhu river and gorge, and the
Matshitshindzudzi mountain range (northern KNP, Pafuri section); F = View on a rocky outcrop in the Malelane section (Southern KNP). The KNP is scattered with rocky outcrops that often host species that cannot be found in the surrounding savanna vegetation; G= View on the Sandveld around Punda Maria (northern KNP,
Punda Maria section). This region has the highest rainfall in the park, estimated around 530.6 mm/year.
The density of vegetation varies from dense thicket, savanna woodland, tree savanna and montane savanna to forest with tall trees and closed canopy (Schmidt et al. 2007; Figure 5). The vegetation of the KNP grows on two major soil types
(basaltic vs. granitic) and consists of approximately 1974 different plant species including trees and shrubs (458 species), grasses (236 species), ferns (27 species), lianas (16 species), and Aloes (20 species) (Venter 1990). Dominant tree species
14 Chapter 1 General introduction
include Colophospermum mopane (Kirk ex Benth.) Kirk ex J. Leon., Combretum
apiculatum Sond., Acacia nigrescens Oliv., Sclerocarya birrea (A. Rich.) Hochst. and
Combretum imberbe Wawra in the north and Spirostachys africana Sond.,
Terminalia sericea Burch. ex DC., and Dichrostachys cinerea (L) Wight & Arn. in the south.
Figure 5 Major vegetation types in the KNP (modified from Venter 1990).
15 Chapter 1 General introduction
The fauna of the KNP includes c. 500 bird species (Venter 1990), and it is
home to the largest terrestrial mammals (>5kg; 148 mammal species), of which 30 are megaherbivores (>1000kg; elephants, rhinos, giraffes, and many species of antelopes; Owen-Smith & Ogutu 2003; Table 2). These megaherbivores (defined here as herbivorous mammals weighing over 5 kg) are one of major components of
African savanna because of their ecological importance (ecosystem engineers,
Waldram et al. 2008; or keystone ecosystem species, Owen-Smith 1988). They exert continuous pressures on plant communities, increasing landscape heterogeneity (Du
Toit et al. 2003; Figure 6), but little is known as to the extent of their impacts on phylogenetic structure on plant communities.
Figure 6 Giraffe during feeding activity in the KNP (Photo from K. Yessoufou)
16 Chapter 1 General introduction
Table 2 Megaherbivores in the KNP, abundance and dietary behaviour Species Common name Diet Feeding behaviour Abundance in KNP (Prins & Douglas-Hamilton 1990; Gagnon & (Prins & Douglas-Hamilton 1990; Gagnon & (census 2009; Chew 2000) Chew 2000) www.sanparks.org Aepyceros melampus Impala Browser-grazer Generalist 127,788 Cephalophus natalensis Red duiker Frugivores Specialist Connochaetes taurinus Blue wildebeest Variable grazers Specialist 8,963 Damaliscus lunatus Tsessebe Obligate grazer Specialist 160 Hippotragus equinus Roan Variable grazers Specialist 50 Hippotragus niger Sable Variable grazers Specialist 325 Kobus ellipsiprymnus Waterbuck Variable grazers Specialist 5,000 Neotragus moschatus Suni Browser Specialist Oreotragus oreotragus Klipspringer Browser-grazer Generalist Ourebia ourebi Oribi Variable grazers Specialist Pelea capreolus Grey rhebok Browser Specialist Raphicerus campestris Steenbok Browser-grazer Generalist Raphicerus sharpei Sharpe’s grysbok Browser-grazer Generalist Redunca arundinum Reedbuck Obligate grazer Specialist 300 Redunca fulvorufula Mountain reedbuck Obligate grazer Specialist 150 Sigmoceros lichtensteinii Lichtenstein’s Obligate grazer Specialist hartebeest Sylvicapra grimmia Common duiker Browser Specialist Syncerus caffer Buffalo Variable grazers Specialist 37,462 Taurotragus oryx Eland Browser-grazer Generalist 300 Tragelaphus angasii Nyala Browser-grazer Generalist 300 Tragelaphus scriptus Bushbuk Browser Specialist 500 Tragelaphus strepsiceros Kudu Browser-grazer Generalist 8,045 Giraffa camelopardalis Giraffe Browser Specialist 7,091 Hippopotamus Hippo Obligate grazer Specialist 3,000 amphibious Phacochoerus aethiopicus Warthog Variable grazer Specialist 2,316 Potamochoerus porcus Bushpig Browser Specialist Equus burchelli Burchell’s zebra Obligate grazer Specialist 20,868 Ceratotherium simum White rhino Variable grazer Specialist 12,158 Diceros bicornis Black rhino Browser Specialist 627 Loxodonta africana Elephant Browser-grazer Generalist 13,573
17 Chapter 1 General introduction
4.2. Objectives
The current project aims to:
1- Assemble the phylogeny of all trees and shrubs occurring in the KNP;
2- Investigate the suitability of commonly used models of evolution for
plant ecological traits;
3- Investigate the processes of community assembly (deterministic vs.
neutral) in the KNP;
4- Address the impacts of megaherbivores on community structure in the
KNP.
18 Chapter 1 General introduction
4.3. Outline of the thesis
The thesis comprises six chapters briefly described as follow:
Chapter 1 General Introduction
Chapter 2 Phylogeny of trees and shrubs in the KNP using DNA barcodes
Chapter 3 Brownian motion model is not suitable for comparative analysis of plant traits in the KNP
Chapter 4 Characterising diversity and phylogenetic structure of woody plant communities in the KNP
Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
Chapter 6 General Conclusion
19 Chapter 1 General introduction
Figure 7 Diagram indicating the structure of the thesis (see below for details). Each
Chapter is linked to the next, with link indicated by arrows. For example, the general
introduction (Chapter 1) covers all themes discussed in the entire thesis; the
phylogeny presented in Chapter 2 is used in all chapters that follow; etc. Chapter 6 is
linked to all precedent chapters in that it gives a synthesis of all findings.
- Chapter 1: General introduction
It presents a general overview on savanna ecology with emphasis on theories of
coexistence and the rationale of the phylogenetic approach in assembly processes.
- Chapter 2: Phylogeny of trees and shrubs in the Kruger National Park
using DNA barcodes
I use plant DNA barcodes (rbcLa + matK; CBOL Plant Working Group 2009) to
reconstruct the evolutionary pathways that connect all trees and shrubs occurring in
the study area. This phylogeny is used as the regional pool in all phylogenetic analyses conducted in this thesis.
- Chapter 3: Brownian motion model is not suitable for comparative
analysis of plant traits in the KNP
Comparative analysis is a widely used technique in modern evolutionary biology
(Felsenstein 1985a; Freckleton & Harvey 2006; Ackerly 2009; Jombart et al. 2010;
Wiens et al. 2010; Davies et al. 2011; Schaefer et al. 2011). Most studies that apply
this technique assume that ecological traits follow a Brownian motion (BM) of
character evolution without prior test (Freckleton & Harvey 2006). However recent
studies suggest that BM model might not be a generalisable model for all traits
20 Chapter 1 General introduction
(Freckleton & Harvey 2006; Ackerly 2009). Here I fit various models of evolution, and
discuss the best candidate for comparative analysis of plant traits in the KNP.
- Chapter 4: Characterising diversity and phylogenetic structure of woody
plant communities in the KNP
How communities assemble and how they respond to change are still controversial
questions in ecology (Webb et al. 2002; Cavender-Bares et al. 2009). Communities might assemble through a random process (neutral theory; Hubbell 2001), but several studies challenge this theory (e.g. Cavender-Bares et al. 2004; Hardy &
Senterre 2007), advocating that community composition is determined by niche characteristics. Characterising community structure is critical as to what are the major dominant forces that dictate community composition (Hardy & Senterre 2007;
Hardy 2008; Hardy & Jost 2008). In this chapter I discuss these questions in the
KNP using a phylogenetic approach.
- Chapter 5: The role of megaherbivores in shaping the structure of
subtropical plant communities
To date, results of studies that address megaherbivores impacts on plant communities are mixed. Megaherbivores might favour species diversity (Kalwij et al.
2010), but could also have negative impacts (Asner et al. 2009). The presence of exclosures in the park, gives opportunity to address the question in the KNP. This is discussed, and for the first time, the theoretical predictions of Cavender-Bares et al.
(2009) are tested experimentally.
21 Chapter 1 General introduction
- Chapter 6: General conclusion
The last chapter highlights the main findings and their relevance but also opens complementary areas for future studies.
22 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Chapter 2
The phylogeny of trees and shrubs in the Kruger National Park using DNA
barcodes
1. Introduction
Phylogeny is used to address taxonomical questions and classification of a set of
taxa (e.g. Angiosperms, APG III 2009). Meanwhile, over the past 20 years, it has
been acknowledged as an important tool for ecological investigations (Vamosi et al.
2009; Cavender-Bares et al. 2009). Key ecological questions related to species
coexistence (Vamosi et al. 2009; Cavender-Bares et al. 2009), conservation (Purvis
& Gittleman 2005; Forest et al. 2007), species and ecosystems responses to global
change (Willis et al. 2008), and biological invasions (Proches et al. 2008; Cadotte et
al. 2009; Schaefer et al. 2011) are undergoing phylogenetic investigation.
Furthermore, conservation planning, which traditionally focuses only on species
diversity increasingly acknowledges the necessity of taking phylogenetic information
into account (Forest et al. 2007).
Merging phylogeny and ecology brings additional values to ecological studies for three main reasons. First, because species delimitation is still a debated question, conservation planning based only on species diversity could be biased
(Mace et al. 2003). Therefore, the use of diversity metric, which is not sensitive to
species delimitation (e.g. phylogenetic diversity; Faith 1992) can contribute additional key information to biodiversity quantification (Isaac et al. 2004; Faith & Williams
2005). Second, phylogenetic diversity (PD) is not a surrogate of species diversity
and therefore conveys different features of diversity (Forest et al. 2007; Knapp et al.
23 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
2008). Third, the higher the PD the more productive the ecosystem (Maherali &
Klironomos 2007; Cadotte et al. 2008) and stronger its ability to survive environmental changes (Forest et al. 2007; Knapp et al. 2008). In addition, a high PD is expected to favour ecosystem resistance to pathogen attacks (Gilbert & Webb
2007) and invasion (Cavender-Bares et al. 2009). Given the importance of utilising phylogeny in ecology, providing ecologists with a working DNA-based phylogeny for a biodiversity hotspot, is crucial to fuel ecological investigations that can help develop practical tools to sustain its biological diversity. Meanwhile, a comprehensive DNA database to reconstruct the more likely phylogenetic relationships among taxa occurring in a specific site is not always available
(Anderson et al. 2004). This lack constrains ecologists to use a ‘Phylomatic’ approach (Webb & Donoghue 2005) for phylogeny reconstruction.
Phylomatic is a program that uses a megatree [e.g. Davies et al.’s (2004)
Angiosperm consensus tree] to generate a rapid and instant phylogeny of any set of higher plant taxa (Webb & Donoghue 2005). It utilises as input file a list of taxa for which family and genus information are provided. The program performs a series of match of the input taxa to the most resolved position possible in the indicated megatree in the database. For each input taxon, a match in the megatree is first sought for the genus name. Failing this, a match is sought for the family name. As a result, congeners are attached to a polytomous genus node. However, if no internal phylogeny is available for a family, the genera nodes are connected directly to a polytomous family node. To generate a tree, family clades are connected using the super-familial resolution in the megatree (Webb & Donoghue 2005). Although this approach has proven useful (e.g. Kembel & Hubbell 2006), recent studies have
24 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
showed that it could be problematic for ecological analyses because of the
polytomies often generated between congeners (Kress et al. 2009).
In this study I contribute to generate the first and largest plant DNA barcode database (sensu CBOL Plant Working Group 2009) for a tropical African woodland reserve, the Kruger National Park (KNP) in South Africa. Although the primary objective of DNA barcoding is not to assemble phylogenetic trees, it has been proved extremely valuable for phylogenetic investigations of ecological questions
(Kress et al. 2009). The main objective of the current study is to provide ecologists with a phylogenetic framework with which to address such questions in the KNP.
2. Material and methods
2.1. Samples collection
From 2006 to 2010, intensive fieldwork was conducted throughout the KNP, during which leaf materials were collected for 445 tree and shrub species occurring in the park. Leaf tissue was collected for DNA analyses during field collection, and placed into plastic bags filled with silica gel. Species identification was facilitated using field guides (Schmidt et al. 2007). All herbarium vouchers and DNA extracts are housed at the herbarium JRAU, and the DNA Bank at the University of Johannesburg (UJ), with all data available online (www.acdb.co.za).
2.2. DNA extraction, PCR and sequences
Leaf samples were sent to the Canadian Centre for DNA Barcoding (CCDB) where
two DNA regions were sequenced: a portion of matK and subunit 'a' of rbcL. These
regions have been identified as suitable 'DNA barcodes' for land plants (CBOL Plant
Working Group 2009), but also for phylogeny reconstruction (Kress et al. 2009).
25 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Molecular procedures conducted at CCDB (DNA extraction, amplification and
sequencing) follow several steps which are presented in Figure 1.
DNA extraction was performed using a semi-automated protocol as described by Hajibabaei et al. (2005) and Ivanova et al. (2008).
Polymerase Chain Reactions (PCR) used a PCR cocktail including 5 trehalose as a PCR enhancer. PCR was run in two rounds, the first round being essentially the “proper” PCR, and the second round was mainly failure tracking
(Figure 1). Different primers were used at each round. For rbcLa, the primers rbcLa-
F/rbcLa-R was used during the first round of PCR reactions, and rbcLa-
F/rbcLajf634R during the second round whereas 1R_KIM-f/3F-KIM-r (1st round) and
matK-390f/matK-1326r (2nd round) was used for matK. These two sequential PCR rounds with different primer sets allow improvement of sequencing success for both rbcLa and matK.
Sequencing of the cleaned PCR products were conducted using the standard
CCDB sequencing protocols described by Hajibabaei et al. (2005). The sequencing process was followed by sequence editing (Figure 1), whereafter sequences were uploaded on the Barcoding of Life Data base (BOLD; www.boldsystems.org).
26 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Sample’s Entering CCDB
MATRIX BOX
TISSUE PLATE
LYSIS
DNA Extraction
PCR
PCR Gel Band Verification (E-Gel)
FAILURE TRACKING
Cycle Sequencing Positive Hit-Picking SECONDARY PCR (Different Primer Pair)
Full 96 Well Plate Cycle Sequencing Cleanup PCR Gel Band Verification (E-Gel)
DNA Sequencing Positive Hit-Picking
Sequence Editing and upload Full 96 Well Plate to BOLD
Figure 1 Representation of steps for DNA sequencing at CCDB.
2.3. Sequences alignment
Sequences were aligned using Multiple Sequence Comparison by Log-Expectation program (MUSCLE vs. 3.8.31; Edgar 2004). Subsequent adjustments were made by eye when there were obvious alignment errors. All aligned sequences were submitted to GenBank/EBI. All GenBank/EBI accession numbers for gene sequences (accession numbers JF265241-JF265667 for rbcLa and JF270599-
JF271008 for matK) and voucher information (including photographic images) are available online (www.acdb.co.za) and listed in Supplementary Information Table S1.
I finally combined all sequences (matK and rbcLa) in a single matrix for phylogenetic analysis. Tree statistics (consistency index CI, rescaled index RC, and retention index RI) for each partition (rbcLa and matK) were also calculated using PAUP*
(Swofford 2003). I applied two methods of tree reconstruction.
27 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
2.4. Tree reconstruction
I reconstructed the phylogeny of 445 species plus Amborella used as outgroup to root the tree (APG III 2009); these species represent 246 genera, 71 families and 30 orders (sensu APG III 2009; Supplementary Information Table S1). Phylogeny
reconstruction based on combined matK + rbcLa data was performed with maximum
likelihood (ML; Felsenstein 1973). The combined two-gene dataset was
phylogenetically analysed using RAxML-VI-HPC 7.2.6 (Randomized Axelerated
Maximum Likelihood for High Performance Computing; Stamatakis et al. 2008) on
the CIPRES cluster (Miller et al. 2009). Based on the Akaike Information Criterion
(Akaike 1974) as implemented in jModeltest (Posada 2008), all analyses utilised the
GTR + Γ model of nucleotide substitution and the rapid hill-climbing algorithm. This model indicates six general time-reversible substitution rates, assuming gamma rate heterogeneity. In the combined analysis, model parameters were estimated and optimised separately for each gene. Each analysis comprised 1000 alternative runs from distinct randomised maximum parsimony starting trees. To assess branch support, non-parametric bootstrap analyses (Felsenstein 1985b) with 1000 replicates were conducted.
I also reconstructed the phylogeny using maximum parsimony (MP). Tree searches were conducted using heuristic searches with 1000 random sequence- additions but keeping only 10 trees per replicate to reduce time spent on branch swapping in each replicate. Tree bisection-reconnection was performed with all character transformations treated as equally likely i.e. Fitch parsimony (Fitch 1971).
MP searches and bootstrap resampling were performed using PAUP* (Swofford
2003). Resulting trees (available on TreeBase ID 11232) were summarised as
28 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
majority-rule consensus tree. Bootstrap support (BP) was classified as high (85-100), moderate (75–84) or low (50-74).
3. Results
3.1. Tree statistics
Tree statistics are summarised in Table 1. The alignment of each gene including gaps resulted in 552 and 942 characters for rbcLa and matK respectively, making the aligned combined-genes consisted of 1494 characters. The aligned matK contains the most variable sites (80) whereas only 47 of sites in rbcLa are variable.
The number of potentially informative sites is also higher for matK (71) than for rbcLa
(42). Variable positions followed the same trend with matK containing the highest average number of changes per variable site (9.63) followed by rbcLa (9.23).
Parsimony trees are shorter with rbcLa matrix (2373 steps) and higher with matK
(7220 steps). Comparing the indices values, again rbcLa scored lower for CI, RI and
RC than matK (Table 1).
Table 1 Characteristics of each partition obtained from PAUP*. * = indicate number of missing sequences for each region.
Characteristics rbcLa matK Combined matrix
Number of taxa included 432 (13*) 412 (33*) 445
Total number of characters 552 942 1494
Number of constant characters 295 192 487
Number of variable sites 257 (47% ) 750 (80%) 1007 (67%)
Number of parsimony informative sites 230 (42% ) 669 (71%) 899 (60%)
Number of steps (tree length) 2373 7220 9707
Consistency index (CI) 0.18 0.22 0.21
Retention index (RI) 0.83 0.84 0.84
29 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Rescaled index (RC) 0.15 0.19 0.18
Average number of changes per variable site 9.23 9.63 9.25
(number steps/number variable sites)
3.2. Phylogeny of trees and shrubs of the KNP
In Figure 2, I present the maximum likelihood tree, henceforth referred to as ML tree.
Figure 3 presents the majority-rule consensus phylogeny generated using the
maximum parsimony approach. This tree is referred to as MP tree. There is a strong
similarity between both the trees (high congruence) but with a few noteworthy
differences (see discussion section). Bootstrap supports for all clades and subclades
resulting from MP and ML analyses are presented in Table 2. In discussion section,
when BS values obtained from MP and ML analyses are different, I report only the
highest BS value and specify the type of analysis (MP or ML).
Major clades and subclades obtained from the detailed MP and ML trees are
summarised below (Figure 4). This summary tree reveals 11 major clades that can
be equated to the recognized Monocotyledonae (Arecales, Pandanales, and
Asparagales), Magnoliidae (Canellales, Laurales, and Magnoliales), Basal Eudicots
(Ranunculales and Proteales), Caryophyllales, Ericales, Campanulidae (Apiales and
Asterales), Lamidae (Gentianales, Boraginaceae, Lamiales, Solanales, and
Icacinaceae), Santalales, Vitales, Malvidae (Sapindales, Malvales, Brassicales,
Myrtales, and Geraniales) and Fabidae (Fabales, Fagales, Rosales, Malpighiales,
Celastrales, and Zygophyllales) (Figure 4).
30 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
31 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
32 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
33 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
34 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Figure 2 The Maximum likelihood phylogeny of trees and shrubs occurring in the
KNP. Values indicated on the branches are bootstrap values. Family names are indicated following APG III (2009). Amborella trichopoda (Amborellaceae) is used as
35 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
outgroup. Names of species in red indicate species recorded in plot surveys for analyses in chapters 4 and 5.
36 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
37 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
38 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
39 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
40 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
41 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
42 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
43 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Figure 3 The maximum parsimony majority rule consensus phylogeny of trees and
shrubs occurring in the KNP. Names of species and families follow APG III (2009).
Amborella trichopoda (Amborellaceae) is used as outgroup. Dashed red lines indicate tip labels for which font has been reduced to fit in the tip label column. Black dashed line indicates the name of the family Clusiaceae written obliquely also for fitness purpose. Red branch indicates the family Polygalaceae that has been embedded within Fabaceae.
Figure 4 Summary tree of the maximum parsimony majority-rule consensus analysis of trees and shrubs occurring in the KNP. Names of the orders and families follow
APG III (2009), but names of the clades [Monocotyledonae, Magnoliidae,
44 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Campanulidae, Lamidae, Malvidae, and Fabidae] follow Soltis et al. (2011). Numbers
above branches are bootstrap values for MP analysis and values below branches
are bootstrap values for ML analysis.
4. Discussion
I compared the topology of the DNA-barcode tree to the latest APG tree (APG III
2009). Results of comparative analysis are presented in Figure 5. The KNP-barcode
tree presented in this Figure is well supported resolved, and shows a high congruence in topology with the APG-tree (APG III 2009). However, there is an important difference between the two topologies namely the early divergent clades in
APG-tree are sequentially Magnoliids (Magnoliidae) and Monocots
(Monocotyledoneae) while in KNP-barcode tree they appear to be Monocots and
Magnoliids. I argue that this is likely due to a very limited sampling of members of early-divergent clades (only 19 species in this study), compared to a comprehensive sampling in APG III (APG III 2009).
45 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Figure 5 Comparison of the phylogenetic tree of Angiosperm of the KNP (referred to
as KNP-Barcode tree) with the overall phylogeny of Angiosperm (APG III 2009).
APG III phylogeny has been pruned to include only orders occurring in the KNP. The
KNP-Barcode tree results from the ML analysis, and values indicated on the branches are bootstrap values.
I also compared the bootstrap values of MP and ML trees (this study) with those of Soltis et al. (2011). Results are presented in Table 2. Soltis et al. (2011)
used ML to reconstruct the phylogeny of Angiosperm based on 17-genes (including
nuclear, plastid and mitochondrial genes) and using the broadest taxonomic
coverage: 640 species, 640 genera, 330 families and 58 of the 59 orders identified
for Angiosperms (sensu APG III 2009). Their study provides the most resolved
Angiosperm phylogeny ever produced. Clades (as identified in this study) are highly
46 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
supported (BP > 97) and all subclades (as identified in this study) have BP = 100 except Icacinaceae, which was not supported (BS < 50; Table 2). In this study, clades and subclades receive less support compared to Soltis et al. (2011). This is likely to be attributable to differences in sample size (445 taxa vs. 640 taxa) and number of genes used (2 genes vs. 17 genes).
Furthermore, there are differences in BP values between MP and ML analysis
(Table 2; Figure 4). In ML analysis, only two clades: Malvidae (BP = 63) and
Lamiidae (BP = 73) receive low support; all other clades are highly supported (BP >
88). In MP analysis, two clades are not supported (BP < 50): Fabidae and Malvidae; and three clades receive low support: Santalales (BP = 58), Lamiidae (BP = 65) and
Magnoliidae (BP = 59). Supports for subclades are generally higher in ML analysis than in MP (Table 2).
Table 2 Comparison of bootstrap values ( ) of major sub-(clades) in this study with those of Soltis et al. (2011). “-” indicates clades identified in this study but not found in Soltis et al. (2011)
Clades This study Soltis et al. Subclades This study Soltis et al.
(2011) (2011)
MP ML ML tree MP ML ML tree
tree tree tree tree
Fabales 51 72 100
Fagales < 50 < 50 100
Rosales 94 100 100
FABIDAE < 50 97 99 Malpighiales < 50 94 100
Celastrales 100 < 50 100
Zygophyllales 100 < 50 100
Sapindales 93 78 100
Malvales 94 100 100
47 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
MALVIDAE < 50 63 97 Brassicales 99 100 100
Myrtales 91 < 50 100
Geraniales < 50 < 50 68
VITALES 100 < 50 100 Vitales 100 < 50 100
SANTALALES 58 95 100 Santalales 58 95 100
LAMIIDAE 65 73 100 Gentianales < 50 100 100
Boraginaceae 100 100 100
Lamiales 99 100 100
Solanales 100 < 50 100
Icacinaceae < 50 < 50 < 50
Apiales 99 100 100
CAMPANULIDAE 65 73 100 Asterales 100 100 100
ERICALES 98 100 100 Ericales 98 100 100
CARYOPHYLLALES 100 100 100 Caryophyllales 100 100 100
BASAL EUDICOTS < 50 <50 - Proteales 100 < 50 100
Ranunculales < 50 < 50 100
Magnoliales 100 100 100
MAGNOLIIDAE 59 88 100 Laurales < 50 < 50 100
Canellales < 50 < 50 100
Arecales 96 100 100
MONOCOTYLEDONEAE < 50 100 100 Pandanales < 50 < 50 100
Asparagales 61 99 100
Finally, I investigated the relationships within and between major clades recovered. 11 clades and 30 subclades have been identified and are described below.
4.1. Clade 1: Fabidae
Fabidae is represented in the KNP by six subclades: Fabales, Fagales, Rosales,
Malpighiales, Celastrales, and Zygophyllales (Table 2, Figure 4). Fabidae is a well-
48 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
supported clade, receiving a support of BP = 97 in ML analysis, and 99 BP in Soltis et al. (2011). Zygophyllales (MP tree, BS = 100) are sister to [Celastrales +
Malpighiales] but with no support in MP analysis (BP < 50) and low support in ML analysis (BP = 63) (Figure 4). Members of Fabales, Fagales, and Rosales are all nitrogen-fixing plants, and group together on the tree as expected (Figures 2 & 3).
Within this nitrogen-fixing group, Fabales are sister to Fagales (no support), and both are sisters to Rosales (moderate support in ML tree, BP = 77).
In ML analysis, Bauhinia galpinii appears as sister (although no support,
Figure 3) to the rest of Fabaceae, further confirming that the genus Bauhinia may be the early-divergent Fabaceae (Doyle et al. 2000). However in MP analysis, Bauhinia galpinii is sister to the subclade Fabales which include Fabaceae and Polygalaceae
(with poor support, BP = 51).
Subclade Celastrales is recovered in MP and ML trees, but is only supported in MP analysis (MP tree, BP = 100). It is represented in the KNP by only one family i.e. Celastraceae, which includes nine genera: Catha, Gymnosporia, Putterlickia,
Elaeodendron, Loeseneriella, Pristimera, Maytenus, Mystroxylon, and Salacia.
Cronquist (1981) excluded the genus Salacia from Celastrales, but in the current study, Salacia is found embedded within Celastrales, as currently accepted in modern Angiosperm classification (e.g. Savolainen et al. 1997; APG III 2009; Soltis et al. 2011). Celastrales is moderately supported as sister to Malpighiales (ML analysis, BP = 79).
Malpighiales is recovered with high support in ML analysis (ML tree, BP= 94), and represented 13 families in KNP: Linaceae, Phyllanthaceae, Ochnaceae,
Picrodendraceae, Achariaceae, Putranjivaceae, Salicaceae, Passifloraceae,
49 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Malpighiaceae, Erythroxylaceae, Chrysobalanaceae, Clusiaceae, and
Euphorbiaceae.
The family Linaceae comprises two subfamilies, Linoideae and Hugonioideae.
Recent study showed that this family is monophyletic (McDill et al. 2009). It is
represented by only one species in the KNP (Hugonia orientalis), and therefore the monophyly of the family cannot be discussed here. Chase et al. (2002) pointed out a
weak relationship between Linaceae and Picrodendraceae, but this is not recovered
in this study. Linaceae is rather sister to [Malpighiaceae + Erythroxylaceae] in MP
analysis, but to [Malpighiaceae + Ochnaceae] in ML analysis, with no support in both
analyses (Figures 2 & 3).
Members of the family Putranjivaceae have been included either in
Euphorbiaceae (Webster 1994) or in Brassicales (Rodman et al. 1997, 1998). In this
study, Putranjivaceae is not sister to either Euphorbiaceae or Brassicales (see also
APG III 2009), but group as sister to Clusiaceae.
In MP analysis, Garcinia livingstonei (Clusiaceae) is unexpectedly embedded
within Euphorbiaceae. This may be due to the fact that only rbcLa (that includes high
number of uninformative site; Table 1) was sequenced for this species. However in
ML tree, Clusiaceae is clearly outside Euphorbiaceae, and appears sister to
Putranjivaceae (although with no support).
4.2. Clade 2: Malvidae
Malvidae, is poorly supported in this study (ML tree, BP = 63; Table 2; Figure 4), but
highly supported (BP = 97) in Soltis et al. (2011). It includes five subclades:
Sapindales, Malvales, Brassicales, Myrtales, and Geraniales. Malvidae is sister to a
well supported (ML tree, BP = 97) Fabidae with moderate support (ML tree, BP =
50 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
72). This sister relationships between Malvidae and Fabidae was also recovered in
previous studies, but with stronger support (BP =100, Soltis et al. 2011).
In a recent study, Myrtales were found sister to Geraniales with moderate
support (BP = 79), and [Myrtales + Geraniales] was recovered as sister to the
remaining Malvidae (Soltis et al. 2011). In this study, this topology is not recovered.
Instead, I found that in both MP and ML analyses, Geraniales (represented in this
study by only one species, Bersama lucens) is sister to the rest of the clade (ML
tree, BP = 50). In addition, I also found that Myrtales (MP tree, BP = 91) is sister to a
highly supported group of [Sapindales + (Brassicales + Malvales)] (ML tree, BP =
100). The divergent results between this study and that of Soltis et al. (2011)
regarding relationships within Malvidae are more likely due to both a limited taxon
sampling and genes used in this analysis (445 taxa, 2 genes) compared to Soltis et
al.’s (2011) analysis (640 taxa, 17 genes).
Sapindales (MP tree, BP = 93) is sister to [Brassicales + Malvales]. This relationship is similar to recent topology resulting from analyses focusing on Rosidae
(Wang et al. 2009). Brassicales (> 18 families) is represented in the KNP by only two families, Capparaceae (ML tree, BP = 100) and Salvadoraceae (BP = 100 for both
MP and ML trees), showing a strong sister-relationship (ML tree, BP =100).
Salvadoraceae is not included in Soltis et al. (2011). Within Sapindales,
Sapindaceae (ML tree, BP = 97) is sister to [Meliaceae (ML tree, BS =97) +
Rutaceae (ML tree, BP = 99)]. In MP analysis, Kirkiaceae (BS = 100) is sister to a
moderately supported (BS = 73) group of [Burseraceae (BS = 100) + Anacardiaceae
(BS < 50)], but this sister-relationship is poorly supported. However, in ML analysis,
Burseraceae (BS = 100) are embedded within Anacardiaceae. The relationships
51 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
within Sapindales showed in MP analysis largely agree with other analyses (e.g.
Gadek et al. 1996; Muellner et al. 2007; Soltis et al. 2011).
In MP analysis, Myrtales (BP = 91) comprises two major groups: [Onagraceae
(BP < 50) + Lythraceae (BP < 50)] and [Myrtaceae (BP = 94) + Combretaceae (BP =
100)]. The topology recovered in ML analysis is slightly different from that of MP.
Although the sister-relationship between Onagraceae and Lythraceae is also found,
Myrtaceae (BP = 100) is not found sister to Combretaceae (BP = 100). The strong
support found in this study for the placement of Combretaceae is not found in
previous studies (BS < 50 in Soltis et al. 2011; but see Maurin et al. 2010).
4.3. Clade 3: Vitales
This clade is highly supported in both analyses: MP tree (BP = 100) and ML (BP =
100). It is represented in the KNP by only one family, Vitaceae with two genera
(Rhoicissus and Cissus). Two subclades can be defined, one including members of the genus Rhoicissus and the other including members of the genus Cissus. Vitales
+ [Malvidae + Fabidae] represent the super-clade Rosidae (Figure 4; Soltis et al.
2011) in the KNP. Vitales were included in Rosidae (Savolainen et al. 2000; Soltis et
al. 2011), and this placement is also recovered in this study.
4.4. Clade 4: Santalales
Santalales is represented in the KNP by one family, Olacaceae (ML tree, BP = 95)
with three species: Olax dissitiflora, sister to [Ximenia caffra + Ximenia americana].
Santalales was recovered as sister to Asteridae (Soltis et al. 2000; Hilu et al. 2003) with no support. Soltis et al. (2011) included Santalales in their ‘Super-asteridae’
(Soltis et al. 2011) which is in agreement with previous studies (e.g. Soltis et al.
52 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
2000; Hilu et al. 2003). In ML analysis, Santalales is sister to Caryophyllales (no
support); both orders are included in Super-asteridae in Soltis et al. (2011).
Meanwhile, the MP analysis places Santalales sister to Rosidae (no support, BP <
50).
4.5. Clade 5: Lamiidae
This clade is not well supported (MP tree, BP = 73; ML tree, BP = 65) in this study, unlike in Soltis et al. (2011) where Lamiidae was highly supported (BS = 100).
Lamiidae is represented in the KNP by three orders (Gentianales, Lamiales and
Solanales) and one unplaced family (Boraginaceae) in APG III system.
Within Gentianales (ML tree, BP = 100), Gentianaceae (represented in the
KNP by only Anthocleista grandiflora) is sister to two groups: Rubiaceae (ML tree,
BS =100) and [Loganiaceae (ML tree, BP =100) + Apocynaceae (ML tree, BS =
100)]. The sister-grouping of Loganiaceae and Apocynaceae is well supported (BP =
91). The same topology was recovered from MP analysis with similar support.
Similar topology within Gentianales is confirmed by Bremer (1996), Backlund et al.
(2000), Potgieter et al. (2000) and Frasier (2009). However, the sister-relationship found between Apocynaceae and Gentianaceae in Soltis et al. (2011) is not recovered in this study. The reason could be due to very limited sampling of members of Gentianaceae, which is represented in this study by only one species,
Anthocleista grandiflora.
In the KNP, members of Lamiales belong to six well supported families in ML analysis: Lamiaceae (BP = 100), Acanthaceae (BP = 100), Bignoniaceae (BP = 72),
Verbenaceae (BP = 91), Stilbaceae (BP = 88), Oleaceae (BP 100), and one unsupported Pedaliaceae (BP < 50) (represented by only one species). Within
53 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
Lamiales, Oleaceae is sister to the remaining (BP = 100) and Lamiaceae is sister to
Acanthaceae (with no support). The same topology with similar support was
recovered from the MP analysis.
Solanales, a well supported group (in MP tree only, BP = 100), is represented in this study by one family, Solanaceae with two genera, Solanum and Nicotiana.
Boraginaceae (BP = 100 in all analyses), an unplaced family in APG system, comprise two genera, Cordia and Ehretia.
The relationships within Lamiidae vary in literature (e.g. APG III 2009 and
Soltis et al. 2011). In APG III (2009), the relationships are unresolved, but Soltis et al. (2011) provide a resolved topology within the clade. In this latter study, there is a sister-relationship between [Lamiales (BP = 100) + Boraginaceae (BP =100)] and
[Solanales (BP = 100) + Gentianales (BP = 99)] (Soltis et al. 2011). In the current study, the topology observed also varies depending on the methods used. Based on
MP analysis, Icacinaceae is sister to the rest of the clade (BP < 50) and [Solanales
(BP = 100) + Lamiales (BP = 99)] are sister to [Gentianales (BP < 50) +
Boraginaceae (BP =100)]. None of the sister-groupings within Lamiidae in this study, as well as in previous studies, is well supported. In addition, in MP analysis,
Gentianales is sister to Boraginaceae and Lamiales is sister to Solanales. In ML analysis, the topology is different. Gentianales is sister to Boraginaceae + [Solanales
+ Lamiales] indicating that the sister-relationship between Gentianales (BS = 100 ) and Boraginaceae (BS = 100 ) is not recovered.
The placement of Icacinaceae is still a debated question. It was included in
Celastrales by Cronquist (1981, 1988), in Theales by Thorne (1983), and in Cornales by Dahlgren (1983) and Thorne (1992). In this study, Icacinaceae is embedded within Lamiidae. The placement of Icacinaceae within Lamiidae is not only confirmed
54 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
by molecular works (Savolainen et al. 1997; Kårehed 2003; Soltis et al. 2011) but
their unitegmic ovules and connate petals also support this placement (APG III
2009). In APG system, the placement of Apodytes and Cassinopsis, the two representatives of the family Icacinaceae in the KNP, is unresolved.
4.6. Clade 6: Campanulidae
Representatives of Campanulidae grouped with strong support (ML analysis, BP =
100), and comprise two lineages: Apiales (ML tree, BP = 100) and Asterales (ML and
MP tree, BP = 100). This sister-grouping is well supported (ML tree, BP = 100).
Asterales is represented by the family Asteraceae including two genera, Vernonia and Brachylaena. Apiales comprise three families with Pittosporaceae (represented in the KNP by one species, Pittosporum viridiflorum) being sister to [Apiaceae (ML tree, BP = 100) + Araliaceae (ML tree, BP = 100)]. The sistership between Apiaceae and Araliaceae is well supported in ML analysis (BP = 98) but less supported in MP
(BP = 78).
4.7. Clade 7: Ericales
A well supported Ericales (ML tree, BP = 100) is recovered in all analyses as sister to [Lamiidae + Campanulidae]. This sister-grouping is well supported only in ML analysis (BP = 100). A similar topology was also found in Soltis et al. (2011). Three families represent this clade in the KNP: Ebenaceae (MP and ML trees, BP = 100) sister to [Primulaceae + Sapotaceae]. Primulaceae (only one species, Maesa lanceolata) is sister to a well supported Sapotaceae (BP = 100), but this sister- grouping is poorly supported in all analyses (MP tree, BP = 52; ML tree, BP = 62).
Ericales + [Campanulidae + Lamiidae] represent the super-clade Asteridae (Figure
55 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
4) as defined in Soltis et al. (2011). Asteridae is sister to [Santalales + Rosidae] (with no support) with which they constitute the Core Eudicots in the KNP (Figure 4).
4.8. Clade 8: Caryophyllales
Caryophyllales is represented by Plumbaginaceae (BP = 100 in all analyses) sister to Portulacaceae (BP = 100). The placement of Caryophyllales has been debated in
Soltis et al. (1997) who showed this clade nested (BP < 50) within Asteridae and in
Soltis & Soltis (1997) who placed the order within Rosidae while recently Hilu et al.
(2003) placed it as sister to Asteridae. Based on MP analysis in this study,
Caryophyllales is recovered as sister (no support, BP < 50) to the rest of Core
Eudicots, while in ML analysis, it is sister to Santalales (with no support, BP < 50).
The ML topology found in this study is similar to that of Soltis et al. (2011). However the placement in APG III (2009) is similar to that of Hilu et al. (2003) i.e. sister to
Asteridae. The limited sampling in this study combined with the limited genes used could explain the difference in placement between this study and that of Soltis et al.
(2011).
4.9. Clade 9: Basal Eudicots
Basal Eudicots are represented in this study by Proteales (MP tree, BP = 100) and
Ranunculales (no support in both analyses). Proteales is represented by the family
Proteaceae (MP tree, BP = 100) and Ranunculales by Ranunculaceae (one species only, Clematis brachiata). Both lineages are sister in MP tree (Figure 4), but this topology is found neither in ML analysis (Figure 5) nor in APG III (2009) and Soltis et al. (2011). However the ML analysis in this study (KNP-Barcode tree, Figure 5) as
56 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
well as APG III (2009) and Soltis et al. (2011) recover Ranunculales as the early- divergent clade of the Eudicots.
4.10. Clade 10: Magnoliidae
The Magnoliidae or Magnoliids is not extensively represented in the KNP. In MP and
ML analyses, I found the same topology for this clade. Magnoliales (BP = 100 in all analyses) is sister to Laurales (poor support, ML tree, BP = 57). This sister- relationship between Magnoliales and Laurales was also observed in previous analyses (e.g. Zanis et al. 2002; Hilu et al. 2003; Moore et al. 2010; Soltis et al.
2011). I also found Canellales sister to [Magnoliales + Laurales] with high support
(ML tree, BP = 88) but no support in MP analysis (BP < 50).
In Magnoliales, one strongly supported family (i.e. Annonaceae, BS = 100 in all analyses) including seven species was observed. All other families within
Magnoliidae in the KNP comprise only one species: Laurales (Hernandiaceae,
Gyrocarpus americanus), and Canellales (Canellaceae, Warburgia salutaris).
Previous combined analyses of Graham & Olmstead (2000), Qui et al. (2000) and Zanis et al. (2002) have excluded Monocotyledoneae (Monocots) from
Magnoliidae whereas Savolainen et al. (2000), Soltis et al. (2000, 2003) have expanded Magnoliidae to include Monocots. In this study, the phylogeny of the
KNP’s woody plant recovers Magnoliidae excluding Monocotyledonae supporting the conclusion of Graham & Olmstead (2000), Qui et al. (2000), and Zanis et al. (2002).
4.11. Clade 11: Monocotyledoneae
The Monocotyledoneae or monocot clade is a well supported group (ML tree, BP =
100). It comprises three subclades: Asparagales (ML tree, BP = 99), Arecales (ML
57 Chapter 2 The phylogeny of trees and shrubs in the Kruger National Park using DNA barcodes
tree, BP = 100), and Pandanales (represented by only one species, Xerophyta retinervis). The relationships within Monocotyledoneae vary with methods of data analysis. In MP analysis, Asparagales (BP = 61) is sister to [Arecales (BP = 96) +
Pandanales]. However, in ML analysis, Arecales (BP = 100) is sister to [Asparagales
(BP = 99) + Pandanales].
5. Conclusion
This study provides a DNA barcoding database and phylogenetic tree for the
Angiosperm trees and shrubs of the largest subtropical reserve in Africa. This
database is available online on BOLD and at the African Centre for DNA Barcoding,
University of the Johannesburg (South Africa). The topology of the tree assembled in
the current study is in agreement with current knowledge regarding relationships
within Angiosperm. This provides ecologists with a unique phylogenetic tool with
which numerous long-standing conservation questions can be addressed in the
KNP. I anticipate that this well resolved angiosperm tree (ML tree) will be of broad
utility in many areas of biology, including community ecology in savanna biomes,
physiology, and conservation ecology in the KNP.
58 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Chapter 3
Brownian motion model is not a suitable model for comparative analysis
of plant traits in the KNP
1. Introduction
Phylogenetic comparative analysis (PCA) is a widely used technique in
modern evolutionary biology (Felsenstein 1985a; Freckleton & Harvey 2006;
Ackerly 2009; Jombart et al. 2010; Wiens et al. 2010; Davies et al. 2011;
Schaefer et al. 2011). It has provided new insights into our understanding of current biological challenges. These challenges include pest management
(Gilbert & Webb 2007), impacts of climate change on biodiversity (Willis et al.
2008, 2010), invasion management (Cadotte et al. 2009; Schaefer et al.
2011), and extinction risk (Davies et al. 2011).
Furthermore, the PCA has also provided several ways of testing
phylogenetic autocorrelation or signal in ecological traits, which is critical, for
example in phylogenetic analysis of community structure (Webb et al. 2002;
Wiens et al. 2010; Krasnov et al. 2011). For instance, demonstrating the
presence of phylogenetic signal may be useful in detecting shifts in correlation
patterns on a phylogeny (Revell & Collar 2009), or specific selective regimes
(Hansen 1997; Butler & King 2004; Hansen et al. 2008). It is also acknowledged as essential in addressing changes in evolutionary rates
(O’Meara et al. 2006; Ackerly 2009).
Despite the importance of PCA, recent studies have questioned the key assumption that underlies its application (e.g. Freckleton & Harvey 2006;
59 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Ackerly 2009). In particular, these studies assumed that ecological traits evolve following a Brownian motion (BM) model without prior investigation
(Harvey & Pagel 1991; Hansen 1997; Freckleton et al. 2002), causing
Freckleton & Harvey (2006) to refer to BM as the “conventional model of trait evolution”. However, this conventional model may be misleading (Freckleton
& Harvey 2006; Ackerly 2009). The BM model corresponds to a model where trait diverges indefinitely following a random walk (Figure 1A), and where the likelihood of trait change is unaffected by the state of a trait or by other species (Felsenstein 1973). As a result, the variance in mean phenotypic traits of two traits evolving under BM model is expected to increase over time in unbounded way (Figure 1A), whilst under selection model [e.g. Ornstein-
Uhlenbeck (OU) model], the variance is expected to grow in a bounded fashion (Figure 1B; Butler & King 2004; Ackerly 2009).
60 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Figure 1 Illustration of Brownian motion (BM) model (A) and Ornstein-
Uhlenbeck (OU) model (B) of trait evolution. This illustration results from analysis of simulations with five replicates.
Figure 1 clearly indicates that if a trait evolving in a OU fashion (e.g. under
natural selection) is analysed assuming a BM model, inferences drawn from
such analyses would be incorrect (Butler & King 2004; Harvey & Rambaut
2000).
The main objective of this study is to test the suitability of BM model
using the ecological data of woody plants in the KNP. On African continent,
only the Cape region (Forest et al. 2007; Davies et al. 2011), has received
great attention from a phylogenetic perspective. Therefore providing
ecologists with information regarding the evolutionary model that best-fits
plant ecological data in the KNP is important.
2. Data collection
2.1. Plant ecological traits
Several traits have been identified as strategic for plants due to their
ecological importance (Rosenthal & Kotanen 1994; Wright et al. 2007). For
instance, in African savannas, well known for its rich fauna (see Chapter 1),
plants undergo constant pressures due to herbivory. These pressures include
breaking of branches and trunks, bark stripping, and uprooting, especially
from megaherbivores (e.g. elephants), which can lead to plant death. Dense-
wood plants are better able to resist such herbivores pressures (Turner 2001;
61 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Chave et al. 2009). I therefore measured wood density to quantify plant
strength and resistance to pressures.
Growth strategy might also act as a defence trait against
megaherbivory, for example, leaves of taller plants may escape herbivory
(Palo et al. 1993). Taller plants were also shown to better survive herbivory
pressures in tropical African savannas (Field 1971; Guy 1976). Data on plant
maximum height were therefore recorded to represent plant growth strategy.
Leaf-nutrient content or leaf economics might also represent additional
survival strategies. Herbivores preferentially forage for high-quality leaves
containing high nutrient concentration (Grant & Scholes 2006). Plants with low
leaf-nutrient content will therefore be subject to reduced herbivory. Leaf
economics also indicate plant ability to capture, secure and use nutrients
(Wright et al. 2007). As such, plants with high ability to capture nutrients will
be able to store enough resources that could be used later to survive nutrient-
deficit, and meet their physiological, growth and developmental requirements.
Therefore I measured specific leaf area (leaf area per dry mass) to assess
plant leaf economics (Wright et al. 2004, 2007).
Finally, leaf structure is also key especially for controlling plant
physiology (respiration and transpiration). Also, plants experiencing herbivory
or growing in water-deficit areas develop shorter and narrower leaves
compared to plants free of herbivory pressures or growing in areas where
water is not a limiting factor (Christoph & David 1997). Additionally thicker
leaves may be less palatable (Coley 1983; Cunningham et al. 1999; Wright et
al. 2004). Thus, leaf area and leaf thickness were measured to represent plant leaf structure and physiology.
62 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
2.2. Data collection and measurements
I collected trait data for 216 species of trees and shrubs in the KNP following a south-north gradient to include as much intra and interspecific variability as possible. Four traits were measured: specific leaf area (SLA), leaf area (LA), leaf thickness (LT) and wood density (WD). Two other traits including maximum height and spinescence (presence/absence of spines) were collected from literature.
In the field, leaf samples were sealed in plastic bags and conserved in a cool box containing ice. In the lab (ACDB lab, University of Johannesburg), samples were kept in -4°C until further processing. Prior to the measurement of leaf area (LA), I scanned 3-12 leaves for each species (representing 1324 leaves scanned in total). These leaves were later oven-dried at 60°C for 2 days, and their dry mass was determined. The scanned leaves were used to determine leaf area using Image-Pro Analyzer 7.0.1.
For other leaf measurements, rehydrated samples were used according to the standardised protocol described by Garnier et al. (2001). Rehydrated leaves were immediately weighed to determine the saturated leaf fresh mass
(LFM). I then calculated specific leaf area (SLA) as: SLA = LA/LDM and leaf dry matter content (LDMC) as LDMC = LDM/LFM where LDM = leaf dry mass and LFM = leaf fresh mass. Leaf thickness (LT) was calculated as LT = (SLA x LDMC)-1 following Vile et al. (2005).
To determine wood density (WD), I sampled, from living trees, 3-5 small pieces of wood per species (764 wood samples in total) and immediately placed them into plastic-sealed bags on ice, labelled according to the species voucher. These samples were maintained at constant humidity at -4°C until
63 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
they were placed into water for 48 hours to ensure adequate swelling. The
volume of fully hydrated samples was measured by the water displacement
method. The same samples were then dried for 2-3 days in a well-ventilated oven at 80°C until it achieved constant mass. The oven-dry mass was then measured to calculate the wood density (WD) as: WD = dry mass/hydrated wood volume.
Maximum plant height and spinescence data were collected from
Schmidt et al. (2007) who conducted intensive fieldwork in the KNP.
3. Data analysis
To test the suitability of BM model to the trait data collected, I applied two analyses. First, I assumed that traits evolve in a BM fashion. I then evaluated the phylogenetic autocorrelation or phylogenetic signal using two tests:
Pagel’s lambda (Pagel 1999) and Blomberg’s K test (Blomberg et al. 2003).
Pagel’s lambda, calculates a parameter lambda, which is a multiplier of the off-diagonal elements of the variance/covariance matrix describing tree topology and branch lengths. Pagel’s test implemented in the R package
Geiger 1.0 (Harmon et al. 2008) transforms the phylogeny in an attempt to improve the fit of the BM model (Pagel 1999). Values of lambda vary between
0 and 1. A best fit of BM model is indicated by lambda equals 1. Significance of lambda was assessed using the likelihood ratio test. Blomberg’s K test implemented in the R package Picante 1.2 (Kembel et al. 2010) evaluates the phylogenetic signal in a trait against a BM model. If K equals 1, the trait follows a BM model, but K < 1 is an indication of a non-BM model. Statistical significance of the K values was evaluated by randomly shuffling traits 1000 times and calculating 95% confidence intervals (ci). Both Lambda and K were
64 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
calculated from the ML tree (see Chapter 2) with branch lengths transformed
to relative time using penalized likelihood (Sanderson 2002).
To allow comparison of patterns observed under BM-related tests
(Lambda and K), I also performed a non-BM test of phylogenetic signal, the
Abouheif’s test (Abouheif 1999) implemented in the R package Adephylo 1.1
(Jombart et al. 2010). Abouheif’s test uses a nonparametric test to detect
signal based on the Moran's index test (Pavoine et al. 2008). This test first
calculates a C-statistic; C = 1-η/2, η as the ratio of the sum of squared differences between successive observations (i.e. the trait values of successive taxa on the phylogeny) and sum the squares of each observation.
The mean of the observed C-values is then compared to the mean of randomised C-values estimated by randomly shuffling the data so that taxa are randomly placed on the tips of the phylogeny. I used 999 random permutations to obtain p-values.
In the second analyses, I fitted six commonly used evolutionary models to the plant ecological traits. These models are lambda, delta, kappa, early- burst (EB), Ornstein-Uhlenbeck (OU), and Brownian motion (BM). Each model depicts a different selective regime. The best model was selected using
Akaike Information Criterion (AIC; Burnham & Anderson 2002). The model with the lowest AIC value is the best model for the data (Burnham & Anderson
2002).
3. Results
Pagel’s lambda and Blomberg’s K values were both lower than 1 for all traits:
(lambda ≤ 0.88; K ≤ 0.05; Table 1), indicating that traits might not evolve in a
65 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
BM fashion. However, significant signal was detected in all traits (Table 1;
Figure 2)
Table 1 Tests of phylogenetic signal in the studied traits based on Pagel and Blomberg’s statistics. Significance of lambda values are indicated as follows: * = p < 0.05; ** = p < 0.01; *** = p < 0.001; Significance of K values are indicated by confidence intervals ci (see illustrations in Figure 2).
Traits Pagel’s Significanc Blomberg’s K Significance lambda e (p- values (Confidence values values) intervals ci) See also Figure 1 Spines 0.88 < 0.001*** 0.03 ci=0.007-0.012 Wood 0.79 < 0.001*** 0.05 ci=0.005-0.015 density Leaf 0.70 < 0.001*** 0.03 ci=0.006-0.013 thickness Leaf area 0.55 < 0.001*** 0.02 ci=0.007-0.013 Specific 0.42 < 0.001*** 0.02 ci=0.007-0.013 leaf area Maximum 0.30 < 0.001*** 0.02 ci=0.007-0.012 height
66 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Maximum h Wood dens Leaf area 400 200 300 300 200 Frequency Frequency Frequency 200 100 100 100 50 0 0 0
0.006 0.012 0.00 0.02 0.04 0.005 0.015 K values K values K values
Specific lea Spines Leaf thickn 200 200 300 150 150 200 100 Frequency Frequency Frequency 100 100 50 50 0 0 0
0.005 0.015 0.000 0.015 0.030 0.00 0.02 0.04 K values K values K values
Figure 2 Values of Blomberg’s K (dashed red line) for each trait. Histogram bars represent K-values based on 1000 randomisations. All traits show significant signal. Dotted lines indicate the 95% confidence interval.
I also applied a non-parametric test (Abouheif test) of phylogenetic signal. The result was more complex. It supported the presence of significant phylogenetic autocorrelation in all traits, except in leaf area and specific leaf area (Table 2; Figure 3).
67 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Table 2 Test of phylogenetic signal in the studied traits based on Abouheif’s statistics. Significance of the signal is indicated as follows: * = p < 0.05; ** = p < 0.01; *** = p < 0.001; NS = non significant. Std = standard deviation; see text for details about C values.
Traits Observed C Std Observed C P values Maximum height 0.28 6.32 0.001** Wood density 0.35 7.54 0.001** Leaf area 0.004 1.26 0.064NS Specific leaf area 0.02 0.99 0.091NS Spinescence 0.36 7.96 0.001** Leaf thickness 0.12 4.67 0.014*
68 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Figure 3 Abouheif test of phylogenetic signal in the studied traits. The
histogram indicates the frequency distribution of randomised mean C-
statistics calculated from the trait data along the tips of the phylogeny. The
vertical line with a rectangular black dot indicates the position of the observed
mean C-statistic relative to the null hypothesis sampling distribution.
To further test this finding that BM might not be suitable, I conducted a
model fitting test using the AIC statistic. AIC values for each model are
summarised in Table 3. This test confirmed that BM model was not a suitable
model for any of the traits. However, no single model was able to account for the evolution of all traits. Instead, two models, lambda and OU were competing to explain plant trait evolution best. Lambda was best fitted to maximum height and leaf thickness data whereas the evolution of wood density, leaf area, specific leaf area and spinescence was best explained by
OU model. It is important to note that when lambda best fits the evolution of a trait, OU is the second best model and vice-versa. Early Burst (EB) models
showed the highest AIC value for all traits, indicating that EB was the worst-
fitting model for woody plants.
69 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Table 3 Models comparison of traits evolution of woody plants using AIC test. Values indicated are the Akaike Information Criterion
AIC. Values in bold are the lowest AIC scores indicating the best model for each trait. Values underlined indicate the second lowest
AIC scores, i.e. the second best model.
Ecological traits Models Indication of the models Maximum Wood Leaf Specific Spines Leaf height density area leaf area thickness Lambda When close relatives are less similar 497.4933 196.5407 803.2391 807.7841 44.50788 785.2892 than expected, it stretches terminal branches relative to deep branches to fit Delta When Brownian rate parameter speeds 660.9125 260.6225 1177.385 1150.568 92.6299 1008.862 up or slows down over time Kappa When change occurs with each 551.6139 215.8766 852.0965 858.6493 47.2106 831.1721 speciation event, but is not proportional to branch length Early Burst When there is an initial burst of trait 802.5878 273.2451 2330.057 2248.721 105.8249 1823.565 (EB) diversification but less later Ornstein- When taxa diverge less on long 513.8413 193.3471 799.2955 795.8571 1.08184 793.0877 Uhlenbeck branches than expected, due to (OU) stabilising force pulling towards centre Brownian Trait diverge indefinitely, random walk 800.5878 271.2451 2328.057 2246.721 103.8249 1821.565 motion (BM)
70 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
4. Discussion
Demonstrating phylogenetic conservatism in ecological traits is critical in comparative analysis (Webb et al. 2002; Wiens et al. 2010; Krasnov et al.
2011). Several tests have been developed to quantify evolutionary conservatism, but no single test is able to account for all models of evolutionary processes (Ackerly 2009; Wiens et al. 2010; Krasnov et al.
2011). A weight-of-evidence approach, using a combination of tests, is therefore best suited to capture information about phylogenetic autocorrelation of ecological traits where the mode of evolution is unknown and might differ between traits (Krasnov et al. 2011). Identifying the best model of evolution for ecological traits is therefore a prerequisite for comparative analysis.
Here I used multiple approaches to test for evolutionary model that best fits woody plant traits, using the flora of the KNP as a case study. I first tested for phylogenetic signal in plant traits. In aggregate, the tests indicate that plant traits demonstrate significant phylogenetic conservatism, but the mixed results across test statistics suggest that they might not evolve in a BM fashion. A best fit of BM model is indicated by K = 1 (Blomberg et al. 2003) and lambda =
1 (Pagel 1999). For all traits, I observed K << 1, and lambda < 1, results consistent with a non-BM model.
To further test this finding, I used the AIC statistic to compare six commonly used models of character evolution. AIC test also supported the previous finding in that BM model was not the best model for any of the traits.
In addition, no single model was able to account for evolution of all traits, providing support to the mixed results of signal tests. Importantly, I found OU model to be the best model for wood density, leaf area, specific leaf area and
71 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP spinescence whereas lambda might be a suitable model for plant height and leaf thickness.
However, lambda model transforms the phylogeny in an attempt to force the fit of the BM model to the data (Hansen 1997; Pagel 1997;
Freckleton et al. 2002). As a result of phylogeny transformation, phylogenetic branch lengths or interspecific distances are modified (Grafen 1989; Gittleman
& Kot 1990; Pagel 1997). Two major critics are raised against the fit of lambda, which is actually an allied model to BM. First, by transforming trees onto a scale that yield a best fit of BM, comparative methods such as Pagel’s lambda allow distortion of the phylogeny, making it difficult to infer information about the most likely evolutionary process (Freckleton & Harvey 2006).
Second, such methods ignore all ecological processes occurring during evolution (Pagel 1997; Butler & King 2004). Given such limitations, applying lambda to the data of height and leaf thickness in comparative studies of woody plants in the KNP might be misleading. I therefore suggest the use of the OU model, which is the second best model after lambda for these traits.
The very low values of K found for all traits are consistent with deviations from
BM model, likely indicative of underlying ecological mechanisms (e.g. stabilising selection forces).
The best fit of OU model found for other traits provides evidence consistent with this expectation, i.e. ecological or physiological constrains might cause taxa to diverge less on long branches than expected under a BM model (Garland et al. 1993; Hansen & Martins 1996).
Crucially, the non-fit of BM model revealed by all tests (K, lambda and
AIC tests) could be driven by two major factors (Silvertown et al. 2006; Revell
72 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP et al. 2008). The first factor may be linked to adaptive evolution (e.g. adaptive radiation) in which case a niche-filling model is more appropriate (Freckleton
& Harvey 2006). The second factor that can account for non-BM model could be linked to a ‘bounded’ random walk driven by ecological and plant physiological constrains (Ackerly 2009). It is expected that both scenario
(adaptive radiation and presence of constrains) could act as stabilising forces leading to a pattern incompatible with BM model (Butler & King 2004;
Freckleton & Harvey 2006).
Tropical African savannas are characterised by various stressful pressures (e.g. herbivory, water or nutrient deficit, global change etc.) which operate in combination to drive savanna composition (Biaou 2009). As a result, only species that are tolerant to pressures (i.e. with specific traits) are able to survive in this disturbed environment (Westoby et al. 2002; Walther et al. 2005; Lavergne et al. 2006). Furthermore plant survival also required shifts in traits as adaptive response to environmental conditions. The filtering or selection of traits, and the plant adaptive responses could cause significant departure of trait from BM model (Freckleton & Harvey 2006). As such, the application of statistical comparative methods that assume BM model to study woody plant traits in the KNP could be severely compromised.
However, a question can be raised: why did I find significant signal when applying tests that assume BM model although traits do not clearly evolve in a BM fashion? Metrics of phylogenetic signal are excellent measures of the overall evolutionary pattern of a trait, but can be driven by only a specific conserved clade on the phylogeny (Ackerly 2009). For instance, the signal observed could be driven by the most species-rich family alone (here
73 Chapter 3 Brownian motion model is not a suitable model for comparative analysis of plant traits in the KNP
Fabaceae, see phylogeny in Chapter 2). It could also indicate that the traits are highly affected by environmental pressures (biotic and abiotic) leading to a convergent evolution (Blomberg et al. 2003). Another possible reason is that forcing the fit of BM by transforming the phylogeny (e.g. lambda model), could finally lead to a significant signal.
5. Conclusion
Phylogenetic comparative methods are used extensively to investigate phylogenetic signal in traits or to correct for statistical non-independence amongst species that arises as a consequence of common ancestry. The application of these methods requires a specification of a null model traditionally defined as BM model. OU model instead of BM model is more suitable for woody plant traits occurring in the KNP. The assumption of BM should not be used without prior test, especially for traits for which the model of evolution is unknown.
74 Chapter 4 Characterising diversity and phylogenetic structure of woody plant communities in
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Chapter 4
Characterising diversity and phylogenetic structure of woody plant communities in the KNP, South Africa
1. Introduction
How do species assemble is one of the most debated questions in ecology (Vamosi et al. 2009; Elias et al. 2009). Two major contradicting theories known as deterministic and neutral theories have emerged to address this question. The core principle of the deterministic theory (also known as niche theory) is that a given habitat selects for species that share similar phenotypes and physiology (Hutchinson
1959; Webb et al. 2002). Therefore, species assemblages are expected to be constrained by both their fundamental niche (i.e. abiotic requirements) and biotic interactions. The abiotic factors act as filters that dictate the composition of a community by allowing only species that are ecologically similar. As a result, the community will be composed of species sharing phenotypic characteristics that help them pass through the environmental barriers (Webb et al. 2002). The biotic interactions could be either negative (e.g. competition) or positive interactions (e.g. facilitation and mutualism). Negative interactions prevent phenotypically and physiologically similar species from coexisting (Darwin 1859; Webb et al. 2002) whilst positive interactions reduce stress level in local habitats, and in doing so facilitate species coexistence (Bruno et al. 2003; Valiente-Banuet & Verdu 2007).
Conversely, Hubbell (2001) developed a challenging neutral theory according to which niche characteristics are not relevant in assembly process. He referred to communities as just random collections of species driven only by dispersal and
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stochastic demographic processes. As a consequence, differences between species
(e.g. traits differences, competitive ability differences, niche differences, etc.) and
interactions between species and with the environment do not matter in
assemblages processes (Bell 2001; Hubbell 2001; Chave 2004; Vamosi et al. 2009).
Studies that investigated the relative importance of both theories cover several
systems including microbes (e.g. Anderson et al. 2004; Horner-Devine & Bohannan
2006; Newton et al. 2007), arthropods (e.g. Gillespie 2004; Vamosi & Vamosi 2007),
vertebrates (e.g. Peres-Neto 2004; Lovette & Hochachka 2006; Helmus et al.
2007a,b), parasites (Mouillot et al. 2005) and plant communities (e.g. Webb 2000;
Cavender-Bares et al. 2004, 2006; Ackerly et al. 2006; Kembel & Hubbell 2006;
Proches et al. 2006; Silvertown et al. 2006; Slingsby & Verboom 2006; Swenson et
al. 2006; Webb et al. 2006; Hardy & Senterre 2007; Verdu & Pausas 2007; Valiente-
Banuet & Verdu 2007; Silva & Batahla 2009). Of these studies, 71.80% were
conducted in temperate areas and only 28.20% in tropical biomes (see Vamosi et al.
2009 for detailed review). Among studies that focused on plants system, forest
biomes received greatest attention (e.g. Webb 2000; Chazdon et al. 2003;
Cavender-Bares et al. 2004; Hardy & Senterre 2007; Kembel & Hubbell 2006; Kraft
et al. 2007, 2008; Swenson et al. 2006; Webb et al. 2006). However, to my
knowledge, there have been only one study that took place in savanna biomes (Silva
& Batahla 2009). Because forest and savanna biomes are two ecologically different systems (Hoffmann et al. 2005), there is therefore a need of furthering research to fill the gaps of knowledge regarding assembly process in savanna biomes. Due to the ease of their conversion to agriculture or urban uses, and in the face of global change, savannas are the world’s most endangered biomes (Scholes & Archer
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1997). Furthermore, savannas also harbor a large proportion of rangelands and livestock (Scholes & Archer 1997).
Here I focus on the woody plant communities of the KNP, which is a tropical woodland savanna (Schmidt et al. 2007). Understanding how communities are structured could give insights into to the ecological forces that shape the structure
(Webb et al. 2002; Hardy & Senterre 2007; Hardy 2008; Hardy & Jost 2008;
Cavender-bares et al. 2009), and identifying those forces is a critical step towards the protection of biodiversity patterns (Cavender-Bares et al. 2009).
Two questions are addressed in this chapter. First, is plants spatial distribution in the KNP random with respect to phylogeny? Second, are species within sites more or less related than species from different sites? The main objective of this study is thus to test the current plant community structure against random expectations in a tropical African savanna. Specifically, I test spatial and traits distribution of woody plants against nulls models.
2. Materials and methods
2.1. Dataset
Two data sets were analysed: plant ecological traits and community species composition. Plants traits correspond to those traits indicated in Chapter 3.
Community composition data were collected from March 2008 to 2009, during which
I surveyed 110 2,500m2-plots (50m x 50m). These plots were distributed throughout
the KNP following a south-north transect. They were spread within the 15 ‘eco-
zones’; eco-zones being defined by specific vegetation and soil types (Venter 1990).
To avoid edge and fire effects, plots were situated at least 300 m from the nearest
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track in unburnt areas, but were accessible by gravel roads. In each plot I recorded
all species of trees and shrubs and their abundance (counts of individuals). Overall,
222 species were recorded with an average of 22 species and 309 individuals per
plot.
2.2. Data analysis
Three types of analyses were conducted. First, I characterised the community
composition of the 110 plots in the KNP using three common diversity metrics: the
Shannon index (H) calculated using the ‘diversity’ function implemented in the R
package Vegan 1.17 (Dixon 2003), species richness (SR) and phylogenetic diversity
(PD; Faith 1992), both calculated using the ‘pd’ function implemented in the R
package Picante 1.2 (Kembel et al. 2010).
These three diversity metrics differ in the way diversity features are captured
(Faith 1992; Couteron & Pélissier 2004), but also in their sensitivity to sample size
(Gimaret-Carpentier et al. 1998) and species delimitation (Faith 1992; Isaac et al.
2004; Faith & Williams 2005). For example, species SR weights common and rare species equally, but its dependency on sample size makes it difficult to estimate in species-rich communities (Gotelli & Colwell 2001). In contrast, Shannon index H takes account of the relative abundance of each species, but both metrics (SR and
H) are highly dependent on species definition, which is controversial. The difficulty in species delimitation could cause flaws in diversity measurement (Mace et al. 2003), giving all importance to PD, which captures evolutionary distinctiveness of each taxa, so avoiding flaws in species delimitation (Faith 1992; Isaac et al. 2004; Faith &
Williams 2005). It is therefore crucial to consider a multiple diversity metric approach
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if one wants to capture different patterns of biodiversity. I therefore mapped all
metrics onto the KNP map to generate an overall distribution pattern of plant
diversity along a south-north gradient.
Second, I characterised the within-site community structure using three
approaches. The first approach consists of testing whether there is a significant
phylogenetic signal in species co-occurrence. Therefore, I calculated the frequency
of species co-occurrence using the Schoener's index of co-occurrence “Cij”, and the
phylogenetic distance between species. I then calculated the correlation coefficient
between species co-occurrence and phylogenetic distance using ‘comm.phylo.cor’ function implemented in R package Picante 1.2 (Kembel et al. 2010). A significant positive or negative correlation would indicate that communities are overdispersed/even (distantly related species co-occur more often) or
underdispersed/clustered (closely related species co-occur more often) respectively.
Absence of significant pattern between co-occurrence frequency and phylogenetic
distance is consistent with random community composition. Using the same function
‘comm.phylo.cor’, I also compared the observed correlation to the expected
correlation under a random community composition. This random community was
generated using the null model ‘pool.taxa.labels’ (also implemented in Picante 1.2)
where tip labels have been shuffled 1000 times across all taxa included in the
phylogenetic tree (ML tree in chapter 2).
The second approach consists of comparing patterns of phylogenetic
relatedness within real communities (110 plots) to those expected under specific null
distributions. I therefore calculated two community metrics, the phylogenetic species
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variability index (PSV, Helmus et al. 2007a, b) and the net relatedness index (NRI,
Webb et al. 2002, 2008).
Phylogenetic species variability (PSV) was calculated using the function
“phylostruct” implemented in Picante 1.2. Statistical significance of the PSV values was evaluated by randomising community data matrices 1000 times, based on null models “frequency” (maintaining species occurrence frequency), and “richness”
(maintaining sample species richness). Significance testing using multiple null models that capture different degrees of randomness is crucial to provide confidence in results interpretation. However, null models that involve swapping methods seem to generate more efficiency and reliability statistically than random-fill procedures
(Gotelli & Entsminger 2001).
I therefore further tested significance in PSV value against the null model of
“independent swap”, and calculating 95% confidence intervals (ci). Independent swap algorithm randomises patterns of species co-occurrence in samples without introducing species from the phylogeny pool into the samples. It creates swapped versions of the sample/species matrix and constrains the swapped matrices to have the same row and column totals as the original matrix (i.e. number of species per sample and frequency of occurrence of each species across samples are held constant as species co-occurrences in samples are randomised). The swap algorithm searches the presence/absence matrix for ‘checkerboard’ cells (pairs of species/samples of the form (0...1), (1...0) or vice versa) and swaps these cell contents when it finds them. I set the number of swaps per run to 1000 and conducted 1000 runs.
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Net relatedness index (NRI) was calculated using the ‘ses.mpd’ function in
Picante 1.2. Positive values of NRI indicate that closely related species co-occur more often than predicted by chance (phylogenetic clustering), whereas negative values indicate greater co-occurrence of more distantly related species (phylogenetic evenness). I assessed significance of NRI using 1000 simulations and assuming the complete list of woody species in the KNP as the regional pool (null model
“phylogeny.pool” in Picante 1.2).
The third approach termed ‘Hardy & Senterre’s approach’ characterises among-site community structure (Hardy & Senterre 2007). These approaches are complementary methods rather than alternative statistical techniques for the same question. In the first and second approaches, community structures are based on a hypothetical regional pool (phylogeny), and could vary with changes in this pool
(Hardy & Senterre 2007) and randomisation models used (Gotelli 2000; Miklo &
Podani 2004). In this third approach, regional pool does not really matter, and therefore is believed to provide more robust outcome (Hardy & Senterre 2007).
I therefore computed the among-site community structure metrics PST and IST
(Hardy & Senterre 2007; Hardy 2008; Hardy & Jost 2008) using the function
‘spacodi.calc’ in the R package spacodiR 0.11 (Eastman et al. 2011). Both metrics assess the proportion of the total diversity explained by species turnover among sites based on species abundances. IST expresses the among-site differences in species frequencies whereas PST indicates the gain of phylogenetic divergence among species (Hardy & Senterre 2007; Hardy 2008; Hardy & Jost 2008). When PST
= IST, there is no community phylogenetic structure among sites, whereas PST > IST
(PST < IST) indicates that species found within a same site are more (less) related on
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average than species taken from different sites, indicating a phylogenetic clustering
(overdispersion).
Third, I investigated the phenotypic pattern of plant traits distribution. This analysis is similar to the previous in that it investigates community structure, but differs form them in that it includes only phenotypic data (plant ecological traits), but not phylogenetic information. I therefore calculated the community trait-based
metrics TST and IST (Eastman et al. 2011) using the same package spacodiR. TST >
IST or TST < IST suggests that traits are under- or overdispersed respectively, indicating that habitat differentiation and ecological sorting are key drivers of community composition (Webb et al. 2002; Kraft et al. 2007).
3. Results
3.1. Overall diversity and community structure in the KNP
I found a strong south-north gradient for woody plant community diversity across the
KNP, and this was evident in all three diversity metrics H, SR and PD, with high diversity in the south and extreme north of the park, and low diversity in the centre
(Figure 1). H varied between 1.8 and 3.7 bits; community composition was made of between 13.28 and 31 species, and phylogenetic information accumulated in communities was evaluated between 0.58 and 3.11 (changes/sequence site).
Shifts in community phylogenetic structure paralleled changes in community
diversity, but with lower NRI in the south and north of the park, and higher NRI
towards the centre, indicating that communities in the centre are relatively clustered
(Figure 1).
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Chapter 4 Characterising diversity and phylogenetic structure of woody plant communities in
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Figure 1 General pattern of plant diversity and phylogenetic structure along a south-
north gradient in the KNP. Plant diversity as indexed by SR (species richness), H
(Shannon index) and PD (phylogenetic diversity). Phylogenetic structure is indexed
by net relatedness index (NRI). Colours reflect interpolated values derived from plot
centre points using Ordinary Kriging with a 12-cell neighborhood.
3.2. Community phylogenetic structure within and among sites
Four analyses were performed. First, I calculated the correlation coefficient r
between species co-occurrence and phylogenetic distance between co-occurred species. I found that r was significantly negative (r = - 0.023, p = 0.0003), i.e. as phylogenetic distance between species increased, species co-occurrence frequency decreased. Even compared with the expected co-occurrence frequency under a random community composition, the observed correlation coefficient significantly departed from random expectation (p = 0.01).
Second, I evaluated the PSV value and compared it with the expectation under three different null models. Observed PSV was always lower than the 95%
central PSV distribution interval, indicating species within plots tend to be more
related than expected by chance (phylogenetic clustering; Figure 2)
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Figure 2 Values of observed phylogenetic species variability (PSV) within communities (dashed red line). Histogram bars represent PSV values based on 1000 randomisations performed under three different models: frequency, richness and independent swap. Black dashed lines give the 95% confidence interval.
Third, I calculated the NRI metrics. Across all plots, the vast majority of NRI values were positive (Supplementary Information, Table S2), indicating that co- occurring species in the KNP were generally more closely related than expected by chance, giving further support to the previous findings.
Fourth, I looked at the PST and IST values. I found PST > IST (PST = 0.21; IST =
0.10), indicating that species within plots were more related on average than species from distinct plots; thus giving support to the phylogenetic clustering found previously.
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3.3. Community trait-based structure within and among sites
Similarly, I found for trait structure that TST > IST (TST = 0.44; IST = 0.10), indicative of
a non-random distribution of plant traits within and among communities.
4. Discussion
Plant diversity within the KNP is not randomly distributed, i.e. is strongly spatially
structured. Diversity, as indexed by either species richness, phylogenetic diversity or
Shannon’s index is highest in the north and south of the park, and lowest towards
the centre of the park. A strong correlation between SR and PD is expected in
general (Rodrigues & Gaston 2002), but the co-variation with Shannon’s H indicates
that the increase in diversity is not simply a product of more rare species. Northern
and southern communities are both more species rich and have a more even
representation of species. Strong south-to-north gradients in rainfall within the KNP have been documented (Zambatis 2003) and suggested to be one driver of diversity gradients in the park (Linder 1991). In addition, the physical structure of the landscape, including topographic heterogeneity (Thuiller et al. 2006, 2008) and soil types (Du Toit 2003) are also likely important drivers of species diversity in the savanna biome.
Further to this, I found that both taxa and traits are also not randomly distributed in the KNP. They are in contrary under-dispersed, i.e. closely related species sharing similar traits co-occur more often. This deviation from random distribution may be indicative of underlying ecological mechanisms (Webb et al.
2002; Kraft et al. 2007; Mayfield & Levine 2010; see Vamosi et al. 2009 for further references). This finding contradicts Hubbell’s neutral theory (Hubbell 2001), adding
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to the body of evidence that natural community composition may not result from only a neutral assembly process such as demographic drift, dispersal and speciation
(Webb 2000; Swenson et al. 2006; Hardy & Senterre 2007; but see Proches et al.
2006; Silvertown et al. 2006; Slingsby & Verboom 2006).
Crucially I found that species co-occurring within communities are more related on average than species from different communities. In other words, species within the same community are more closely related, but species from different communities are less related. What could be the major driver of such pattern in the
KNP? Hardy & Senterre (2007) found similar pattern in a tropical forest tree community (Equatorial Guinea) and argued that it could be a result of habitat differentiation i.e. communities occur in contrasted habitats. Similar conclusion could also be drawn here in the KNP. The KNP comprises 15 eco-zones and various habitats probably characterised by various soil types, specific vegetation, topography and climate structured along a south-north gradient (Venter 1990; Linder 1991).
Such habitat differentiation might shape the strong spatially structured communities observed (Figure 1), and could also dictate a specific species to communities of different habitats (habitat filtering, Webb et al. 2002; Kraft et al. 2008).
However, habitat filters might not be limited to habitat characteristics only
(climate, soil types, topography, etc.). They might also include disturbances (Pierce et al. 2007) such as herbivory, which are also considered as key driver of under- dispersion within communities (Pierce et al. 2007; Verdu & Pausas 2007; Cavender-
Bares et al. 2009; Helmus et al. 2010). The KNP is well renowned for its large game animals - including over 150000 antelopes, 35000 buffalos, 20000 zebras, 13000 elephants (census 2009; www.sanparks.org; see also Chapter 5) exerting
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Chapter 4 Characterising diversity and phylogenetic structure of woody plant communities in
the KNP, South Africa
considerable pressures on plant diversity. These pressures could more likely be one
important factor shaping community structure in the park. This is discussed in
Chapter 5. Habitat filtering acts as a barrier that filters out plants missing specific
phenotype that is compatible to environmental requirements. As a result of filtering
processes, communities are under-dispersed i.e. composed of species sharing
similar traits. Because traits are under-dispersed in the KNP, habitat filtering could
be an active force structuring communities in the park.
Meanwhile, the under-dispersion structure found in the KNP is in sharp
contrast to the over-dispersion reported by Silva & Batahla (2009) in a Brazilian
woodland savanna. These contrasting results may come from the different spatial scales investigated i.e. 2500 m2 in this study vs. 25 m2 in Silva & Batahla (2009).
Indeed, at small scales competitive interactions are expected to be higher (leading to
overdispersion) than at large scale where niche overlap is less likely and competition
low (Swenson et al. 2006). Furthermore, the contrasting results may also arise from
the differences in approach used to reconstruct the phylogeny of regional pool. I
used DNA data to reconstruct a fully resolved phylogeny (ML tree, Chapter 2). In
Silva & Batahla’s (2009) study, the phylogeny was reconstructed using the
‘Phylomatic’ approach (Webb & Donoghue 2005). Phylomatic program attaches taxa
by genus or family name to a supertree hypothesis for the seed plants constructed
from published phylogenies. If any genus is missing from the supertree, the program
returns a polytomy of genera within that family or polytomy of species in that genus
as the case may be. Although the use of Phylomatic-generated tree is common, it
has been recently showed that such tree could inflate the community structure metric
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Chapter 4 Characterising diversity and phylogenetic structure of woody plant communities in
the KNP, South Africa
(Davies et al., in press), and as a consequence, inferences drawn from analyses
using such tree would be incorrect (Kress et al. 2009).
5. Conclusion
This work is a contribution to the ongoing debate relative to the importance of neutral
vs. niche processes in community assemblages. I show that communities in the KNP
are not random i.e. they are likely structured by deterministic or niche-related
process. Such process may be driven by habitat filtering including herbivory.
Currently, there is a lack of experimental research that can help differentiate between the effects of each of these factors on community structure. The specific role of herbivory is addressed in Chapter 5.
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Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
Chapter 5
The role of large herbivores in shaping the structure of the KNP, a subtropical woodland
1. Introduction
Phylogenetic information is increasingly being used to study patterns and processes of community assemblages (Cavender-Bares et al. 2004, 2009;
Webb et al. 2002, 2008). Here, I focus on the subtropical woodland biome of the KNP. The dynamics of plant communities in this woodland are dictated by various disturbances (Milchunas et al. 1988; Du Toit 2003), including periodical events such as fire (Govender et al. 2006) and more or less continuous pressures from megaherbivores (Carson & Root 2000; Van
Langevelde et al. 2003). Recent work has shown the important role of fire in structuring the savanna ecosystems (Govender et al. 2006), but the impacts of herbivory on woody species diversity are less clear (Van Langevelde et al.
2003). In Chapter 4, I showed that plant communities in the KNP are not neutral, and that ecological filtering might be playing critical role. In the current
Chapter, I intend to investigate one of the potential filters that possibly dictate current community composition – that is megaherbivores. Although megaherbivores have been shown to reduce the three-dimensional structure of vegetation in the KNP (Asner et al. 2009), several studies suggest that herbivores may favour the diversity of woody plants (e.g. Sankaran et al.
2005) while others suggest that frequent and high herbivore pressures has a
90 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities negative impact on woody plant diversity (e.g. Bond & Keeley 2005; Levick &
Rogers 2008).
I employed a phylogenetic framework to characterise the community structure of woody plant species in the KNP, and evaluate the impact of megaherbivore exclusion on community diversity and composition. Different phylogenetic patterns are expected under various regimes of herbivory and plant defences (Cavender-Bares et al. 2009). Firstly, if most megaherbivores are generalist, plant species with generalist defences, such as spines, will tend to dominate the community. If these generalist defences are evolutionarily conserved, such that closely related plant species share similar defences, the community will then tend to be phylogenetically clustered – composed of more closely related species. However, when defence traits are convergent, the community may demonstrate greater phylogenetic evenness, such that community members are less closely related. Secondly, when plants face high specialist herbivory pressures, abundant species are more likely to be targeted, depressing the strength of competitive exclusion, thereby allowing more rare species to persist in the community and elevating plant diversity. Under multiple specialist herbivory pressure, when defence traits are both matched closely to a specific herbivore and tightly conserved within plant clades, the community will be phylogenetically evenly dispersed, but less structured when traits are convergent (Figure 1).
91 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
Figure 1 Relationships between plant phylogeny and megaherbivory. The ML phylogeny of KNP trees and shrubs is depicted on the left (see detailed tree in
Chapter 2), with one defence trait showing conservatism (presence of spines) in blue. On the right, two examples of megaherbivores are illustrated. On the top, kudus are generalist and feed on a variety of plants, here represented by
92 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
14 taxa indicated by green arrows for clarity (following Hooimeijer et al. 2005), but in the KNP they consume up to 148 different species (Apps 2000). Heavy pressure from generalist megaherbivores and conserved defence traits is predicted to result in phylogenetic clustering of plant communities (Cavender-
Bares et al. 2009). On the bottom, giraffes are specialist browsers and feed preferentially on Acacia, Kigelia and Strychnos (Apps 2000) (red arrows).
Heavy pressure from specialist megaherbivores and conserved defence traits is predicted to lead to phylogenetic overdispersion (Cavender-Bares et al.
2009) (Photos by O. Maurin).
Although large mammal herbivores are well recognised as ecosystem engineers (Waldram et al. 2008) or keystone ecosystem species (Owen-Smith
1988), it remains possible that plant communities are largely structured by abiotic factors (Chapter 4), including climate, soil, and disturbances such as fire. The phylogenetic tree generated for all woody species (Chapter 2) was used to characterise the community structure and diversity across a north- south transect spanning approximately 350 km (Chapter 4). I then evaluated the impacts of megaherbivory by contrasting changes in woody plant communities within long-term ecological plots, from which megaherbivores have been excluded for several decades ('exclosures'), to the immediately surrounding park.
93 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
2. Materials and methods
2.1. Study site: Exclosures
The construction of exclosures in the KNP decades ago facilitated the investigation of impacts of megaherbivores on plant communities. These exclosures – where megaherbivores are partly or fully excluded (Figure 2) - have been established in the park for between eight and 43 years. All megaherbivores are fully excluded from three exclosures: Hlangwine (220 ha;
38 years old), Nkuhlu (139 ha; eight years old) and Nwashitsumbe (302 ha;
43 years old). In two partial exclosures, Nkuhlu (139 ha) and Letaba (129 ha), very large mammals such as elephants and giraffes have been excluded for eight years, but smaller megaherbivores can still gain access (the fence goes from 1.50 m upwards, Figure 2).
94 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
Figure 2 Map of the KNP showing major soil types, location of plots and exclosures.
95 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
2.2. Traits of anti-herbivores defences
In Chapter 3, six plant traits were described but only four can be identified as linked to herbivore ‘defence traits’. These include various physical and mechanical properties, low leaf nutrient content and various chemical compounds (Rosenthal & Kotanen 1994). It is important to note that the different traits provide varying degrees of protection, depending upon the specific herbivore. For instance, plant woodiness may be effective against elephant, but it is of less relevance for impala; in contrast spines can protect trees (e.g. Acacia) against impala, but not against giraffes. Considering a diverse set of traits is therefore necessary to account for various feeding behaviors of herbivores in African savannas. I did not consider chemical defences here because woody plants in African savannas invest more in physical and mechanical defences against large herbivores than in chemical defences (Christoph & David 1997).
Physical defences include spines (Cooper & Owen-Smith 1986) and particular growth strategies. Data on plant maximum height and spinescence
(presence/absence) collected are presented in Chapter 3 and used here as measures of plant physical defences. Mechanical defences include resistance to herbivore pressures. I used wood density to quantify resistance to such damage, and specific leaf area to capture leaf economic spectrum (see
Chapter 3 for details).
2.3. Community sampling in the exclosures
In the KNP 110 plots of 50 x 50 m were sampled (Chapter 4). These data are completed here by a sampling of 73 plots of the same size in all exclosures.
96 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
The KNP like any other African savanna, is a heterogeneous environment defined by the variability of its geology, geomorphology, climate, vegetation and soil (Du Toit 2003). The exclosures were designed to capture this heterogeneity. They are constructed from the south to the north to account for climatic south-north variability on the two major soil types: granite and basalt
(Figure 2). Due to cost and practical considerations (i.e. it was not reasonable to establish new exclosures from which megaherbivores could be excluded over several decades), I was restricted to contrasting communities across these pre-existing exclosures. In total, I sampled 183 plots (110 KNP-plots and 73 exclosure-plots), representing richness and abundance estimates for
216 species (the subset of KNP woody plant diversity found in plots) across
457,500 m2.
2.4. Statistical analyses
To test the major hypothesis of this study, which is presented above and pioneered by Cavender-Bares et al. (2009), evolutionary conservatism in defence traits is crucial. This was discussed in Chapter 3 for all traits considered above as defense traits against megaherbivores. Additionally, it is essential to characterise community structure within and outside exclosures.
Patterns of community outside the exclosures, i.e. in the KNP where herbivores have permanent access was presented and discussed in Chapter
4. Here I evaluated the effects of megaherbivores on diversity and community structure by comparing community patterns of plots within exclosures to plots in the adjacent park (KNP-plots). Adjacent plots were defined as those falling within a 25 km radius of each exclosure because this distance was found to
97 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities provide the best compromise between maximising the sample size of included plots and restricting contrasts to between more or less comparable communities (Figure 3).
Figure 3 Location of KNP-plots (small circles) and exclosures (squares) along a south-north transect in the park. Larger circles define areas of 25 km radius around each exclosure. KNP-plots falling within these areas are considered as adjacent KNP-plots to the exclosures. These adjacent KNP-plots were used for pairwise comparisons to evaluate community patterns within versus outside exclosures. See Figure 2 for location and names of exclosures.
3. Results
In a global comparison of plots within exclosures versus plots outside exclosures, I found that diversity was consistently lower inside exclosures
98 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
(Figure 4; t test, p <0.001), but observed no obvious trend in phylogenetic community structure (Figure 4; t test, p = 0.38).
Figure 4 Comparison of plant diversity and phylogenetic structure between all exclosures KNP-plots. Exclosures include 73 plots (full and partial exclosures combined), and are compared with the 110 KNP-plots, where herbivory is unrestricted.
To evaluate in more detail shifts in community composition I used pairwise comparison of plots within exclosures and their adjacent areas.
Confirming the global trend, I found large and significant decreases in diversity within exclosures (Figure 5). SR was significantly lower within exclosures Hlangwine, Nkuhlu and Nwashitsumbe (t test, p < 0.002) in comparison to the adjacent park. In Letaba, although difference in SR
99 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities between exclosure and adjacent KNP-plots was not significant, plots showed a similar trend. The same patterns were also observed for H but significant difference occurred only in Hlangwine (t test, p = 0.03). PD also decreases significantly in exclosures in all sites (t test, p <0.05) except Letaba, where PD within the exclosure and surrounding KNP was similar.
Figure 5 Pairwise comparison of plant diversity between each exclosure and its adjacent area. Full = full exclosure; Partial = partial exclosure; KNP = plots situated within 25 km of each exclosure. Location of exclosure sites (Letaba,
Nwashitsumbe, Hlangwine and Nkuhlu) are shown in Figure 2. The bottom and top of the boxes show the first and third quartiles respectively, the median is indicated by the horizontal line, the range of the data by the vertical dashed line and outliers (points outside 1.5 times the interquartile range) by circles.
100 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
The results for the phylogenetic metrics (NRI) of community structure were more complex (Figure 6). In the South, no difference was found within and outside exclosures (t test, p >0.05). However, in the centre of the park I found that NRI was significantly lower in exclosure (Letaba) than in adjacent
KNP-plots (t test, p = 0.004), whilst in the North of the park I found the opposite pattern, such that NRI values in exclosure (Nwashitsumbe) were significantly higher than in adjacent KNP-plots (t test, p = 0.0003).
Figure 6 Pairwise comparison of plant community structure between each exclosure and its adjacent area.
Exclosures differ in age (8-43 years) and in the megaherbivore species excluded (full exclosures versus partial, the latter only excluding very large animals such as elephants and giraffes). It is possible that differences
101 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities between exclosures might then be explained by plot treatment or history.
However, I found no significant difference in diversity patterns between exclosures of different ages (8 vs. 38 years; Figure 7), and no consistent differences between partial and full exclosures, even when comparing adjacent exclosures of equivalent age (NRIPartial exclosure ≈ NRIFull exclosure in
Nkuhlu, p = 0.27; Figure 8).
Figure 7 Patterns of plant diversity according to age of exclosures contrasted with pattern in the 110 KNP-plots. Ages are in years. SR= Species richness; H
= Shannon index; PD = Phylogenetic diversity.
102 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities
Figure 8 Patterns of plant diversity according to treatment contrasted with pattern in the 110 KNP-plots. SR= Species richness; H = Shannon index; PD
= Phylogenetic diversity. Full = Full exclosure; Partial = Partial exclosure.
4. Discussion
The KNP represents an important biome that provides crucial ecosystem services to Africa and beyond, and is included within current lists of the
Earth's biodiversity hotspots (Maputaland-Pondoland-Albany biodiversity hotspot; Perera et al. 2011). The role of megaherbivores in maintaining these ecosystems has been debated, particularly the population sizes that the
103 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities biome can sustain (Du Toit 2003). There is a concern that megaherbivores
(especially elephants) impact negatively on plant biodiversity in the KNP
(Levick & Rogers 2008). However, there may be a critical megaherbivore density below which negative impacts reported elsewhere (e.g. Trollope et al.
1998; Levick & Rogers 2008) are not felt (Baxter & Getz 2005). In this study I combined diversity information across a series of exclosures and compared patterns with adjacent sites across the KNP. I showed that when megaherbivores are excluded, diversity generally decreases (see also Kalwij et al. 2010), but impacts on plant community structure were more nuanced.
4.1. Exclusion of megaherbivores and plant diversity
In the pairwise comparisons across exclosures, I found a consistent trend for lower diversity as indexed by SR and Shannon’s H although the strength of this trend varied among sites. Notably, differences in H were only significant for Hlangwine in the South of the park. By contrast, differences within and outside exclosures were less for Letaba, located in the centre of the park.
Further, despite the tight positive correlation between PD and SR, PD was marginally higher within the Letaba exclosure. One possible explanation for differences between sites might be variation in their age of establishment; for example, the Hlangwine exclosure was created 38 years ago, whilst Letaba has only been in existence for 8 years. However, I found no evidence for a bias with age, and diversity for plots in exclosures of 8 and 38 years was statistically indistinguishable. A second possible explanation for site differences is in the size class of megaherbivores excluded. Partial exclosures allow access to all but the very largest megaherbivores, whereas the full
104 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities exclosures are more restrictive, also excluding rhino and antelopes. However, once again I found no difference in diversity between exclosure types when comparing partial and full exclosures at the same sites.
4.2. Exclusion of megaherbivores and phylogenetic structure
Across the KNP I found significant phylogenetic clustering of plant communities. This is discussed in Chapter 4. One recent theory predicts phylogenetic clustering of plant communities under heavy pressure from generalist herbivores when plant defence traits are evolutionarily conserved
(Cavender-Bares et al. 2009). The majority of megaherbivore browsers in the
KNP are generalist. In addition, most defence traits demonstrate some significant phylogenetic conservatism (Chapter 3). The findings of phylogenetic clustering of plant communities, significant conservatism in defence traits, and evidence for strong generalist herbivory pressure imposed by megaherbivores fit well theoretical predictions (Cavender-Bares et al.
2009), and indicate that megaherbivory has a significant role in shaping the phylogenetic structure of plant communities in the park (Helmus et al. 2010).
However, other drivers of community structure, such as habitat filtering might still be important (Chapter 4).
To evaluate the effect of removing megaherbivores, I compared phylogenetic community structure between plots inside exclosures to those in the adjacent park. If megaherbivores drive community clustering, I would then expect communities in exclosures to be more evenly dispersed (i.e.
NRIexclosure < NRIKNP). In addition, because some megaherbivores can access the partial exclosures, I might also expect that partial exclosures would
105 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities demonstrate intermediate clustering, weaker than the KNP plots with unrestricted herbivore access (i.e. NRIPartial exclosure < NRIKNP), but greater than the plots in full exclosures (i.e. NRIPartial exclosure > NRIFull exclosure). However, across exclosures I did not find evidence for any consistent trend towards less clustering within versus outside exclosures or between full and partial exclosures.
Nonetheless, exclosures had an important impact on plant community structure. Where communities under unrestricted megaherbivore pressure were the most evenly dispersed (i.e. in the North of the park), excluding megaherbivores shifted communities to become more clustered. By contrast, where communities were initially more clustered (i.e. in the centre of the park), the exclusion of megaherbivores shifted community structure towards greater evenness. These results therefore indicate that impacts of megaherbivore exclusion on community structure are contingent upon the initial community structure.
5. Conclusion
This study shows not only that megaherbivores are key to maintaining a diversity of species of trees and shrubs, but also that they might impose a specific phylogenetic structure on plant communities, likely important for ecosystem functioning. However, excluding megaherbivores has mixed effects on community structure; the phylogenetic structure of the community shifts, but the direction depends on the initial community structure. Results were similar for both partial and full exclosures, suggesting that it is the very
106 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities largest megaherbivores (i.e. elephants and giraffes) that are responsible for driving these changes.
The restricted number of exclosures limits the statistical inference one can draw with regard to mechanism. Nonetheless, the results are critical for predicting potential losses of phylogenetic diversity (PD) from the woodland biome of southern Africa as megaherbivores decline across much of the continent. Phylogenetic diversity is a valuable measure because it can capture genetic and functional diversity, representing options in an uncertain future (Faith 1992; Mace et al. 2003; Forest et al. 2007). Loss of PD might therefore have larger ecosystem consequences for future biodiversity than loss of species richness per se. These results indicate that losses of PD are likely to be exacerbated for communities that shift towards greater phylogenetic clustering, whereas losses may be less for communities shifting towards greater phylogenetic evenness. I observe this trend in my comparison between exclosures. In Nwashitsumbe, communities become more clustered and losses of PD are large, whereas in Letaba, communities become more evenly dispersed, and there is a weak trend for higher PD in the exclosures, despite their lower species richness.
Characterising the structure of a wider range of communities using phylogenetic trees, which can now be easily estimated from DNA barcode data (Lahaye et al. 2007; Kress et al. 2009), will help us predict community responses. The results in this study have important implications for management of African woodlands. Critically, under-herbivory might be as damaging to plant communities as over-herbivory. As large herbivores are lost
107 Chapter 5 The role of megaherbivores in shaping the structure of subtropical plant communities from these ecosystems, I predict a subsequent reduction in plant species diversity.
108 Chapter 6 General conclusion
Chapter 6
General conclusion
1. Major findings, discussion and contribution to literature
1.1. Phylogenetic information database for KNP
A molecular database of plant DNA barcodes (matK and rbcLa; CBOL Plant Working
Group 2009) was produced for 93% of tree and shrub species of the KNP. This
molecular matrix represents the largest so far made available for a tropical African
woodland savanna. The use of this database to reconstruct the phylogeny of trees
and shrubs in the KNP proves reliable, as it helps generate a phylogeny highly
congruent with the latest phylogenetic studies of angiosperms (APG III 2009; Soltis
et al. 2011). As phylogenetic approach is increasingly acknowledged as key and
unique tool of ecological investigations, this DNA database constitutes an important
tool of sustaining the biodiversity of the park.
More importantly, it provides ecologists with an invaluable tool to investigate ecological long-standing questions. These questions comprise the assembly theory, the sustainability of biodiversity and ecosystem functions under global change
(Davies et al. 2008; Willis et al. 2008, 2010), the dynamic nature of species pool, the primary process that group species together (in situ evolution vs. dispersal ability)
(Ackerly et al. 2006), the feedback influence of within-community interactions on trait diversity (Cavender-Bares et al. 2009), etc.
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Chapter 6 General conclusion
1.2. Ornstein-Uhlenbeck is more suitable for phylogenetic comparative analysis of plant traits in the KNP
How species traits evolve over time might be critical to understand what drives species co-existence. Here I tested the fitness of several evolutionary models to the ecological data of plants in the KNP using various approaches. I showed that the conventional mode of evolution, i.e. Brownian model was not the best model. Rather,
I found that Ornstein-Uhlenbeck model was more appropriate for comparative study in the KNP. This model suggests that a random walk constrained by ecological forces towards a stabilising selection describes plant traits better.
1.3. Plant community assemblages in the KNP are not neutral
This study revisited the most influential theories i.e. the neutral and niche theories of community assembly process. Here I mainly investigated whether community composition is neutral from a phylogenetic perspective. I found that (1) species within a given community are more phylogenetically related than expected under various null expectations; and (2) that species of two different communities are less related. This finding is consistent with a pattern of phylogenetic underdispersion, which is most likely driven by habitat filtering (Webb et al. 2002; Hardy & Senterre
2007). This provides additional evidence to the body of literature that natural community assemblages are not neutral, but are dictated by niche characteristics.
1.4. Megaherbivores leave distinct signature on plant community structure
Megaherbivores are well recognised as ecosystem engineers; however, their impact on plant community structure and diversity remains debated. Several studies suggest megaherbivores may favour the diversity of woody plants, while others
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Chapter 6 General conclusion
report a negative impact on plant diversity. Here, I asked how plant communities
respond to megaherbivores pressure in an African woodland savanna, using the
KNP as a case study.
To date, attempts to address such a question have been hampered by a lack of
long-term experiments. For instance, Cavender-Bares et al. (2009) predicted outcomes of herbivores impacts using phylogenetic approach, but lacks experimental test; Asner et al. (2009) investigated the impacts of megaherbivores on the three-dimensional structure of plant community, but lacks phylogenetic information; Helmus et al. (2010) focused on shift in community structure under disturbance factors in general, but lack information on specificity of disturbances caused by megaherbivores.
The present thesis provides a more comprehensive study about megaherbivores and their impacts on plant community structure. I evaluated the impacts of megaherbivory by contrasting changes in communities within long-term ecological plots, from which megaherbivores have been excluded for several decades. I found that diversity decreases where megaherbivores have been excluded. However, shifts in community structure were more complex. Communities initially clustered become more evenly dispersed when megaherbivores are excluded, whilst communities initially more even become more clustered. This indicates that megaherbivory is essential for maintaining plant diversity; however, impacts on community structure can only be predicted when we have information on the initial structure. Crucially, the finding that shifts in community structure are contingent upon the initial community composition is novel to our understanding of how plant communities could respond to disturbances.
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Chapter 6 General conclusion
I believe the results presented here will receive considerable attention from
scientists and conservationists. For example, the role of elephants in structuring the
vegetation in South Africa is hotly debated, and culling has been put forward as an
urgent necessity - my results challenge this view.
Furthermore, the results also have important implications for management of
African woodlands, particularly given the continental decline in megaherbivores. As large herbivores are lost from these ecosystems, I predict a subsequent reduction in plant diversity, whilst the impact on plant community structure will depend upon the initial composition. Critically, I also showed that the loss of phylogenetic diversity (a surrogate for functional diversity) will depend on the relative shifts in phylogenetic community structure, information that has never been considered before in management strategy.
The KNP is part of a biodiversity hotspot and represents an important biome that provides crucial ecosystem services beyond Africa. The preservation of this important biome requires continued commitment to understand how its flora will respond to future changes, including climate shifts, and the continued continental decline in megaherbivores. The study provides significant novel information in this regard.
2. Future challenges
The emerging field of community phylogenetics has proved promising in addressing long-standing controversies in ecology such as how species assemble, how species interactions influence evolutionary process, and how communities and ecosystems respond to global change (Willis et al. 2008; Cavender-Bares et al. 2009). It is now clear that evolutionary relationships among co-occurring taxa play key role in the
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Chapter 6 General conclusion
assembly process. Meanwhile, despite the improvement phylogenetics brought into
the ecologically important debate, key questions still remain poorly understood.
First, numerous ecological data were gathered by various researchers investigating various ecological questions in the KNP. Now there is a need to reinvestigate (where possible) these questions from a phylogenetic perspective. This thesis provides researchers with phylogenetic tool (DNA database) to do so, which will more likely bring additional key information about how to sustain the biodiversity of the park.
Second, information on how plant communities and ecosystems are responding to ongoing global change (especially climate change) is lacking in tropics. Establishing a predictive model in this regard will help mitigate global change effects on biodiversity. In recent studies conducted by Willis et al. (2008, 2010) in temperate regions, they demonstrated a phylogenetic selectivity effect of climate change on biodiversity. In other words, they showed that climate change is causing loss of species in some specific clades, and concluded that if the ‘tree of life’ is continuously pruned due to climate change, we might end up losing a huge amount of evolutionary diversity.
In tropical zones especially in tropical Africa, there is a conspicuous lack of information on how climate change operates within and among clades. This required a serious commitment from researchers. The increasing volume of DNA database
(e.g. DNA barcode project) is a crucial step forward in providing molecular information with which several questions related to biodiversity could be investigated.
Third, little is known about edge effects on community phylogenetic structure.
Finally, although this study showed a positive correlation between megaherbivores and species diversity, it is more likely that an increasing population
112
Chapter 6 General conclusion of megaherbivores will end up having negative effects. This raises the necessity of conducting studies that provide conservation officers in the KNP with information regarding the population size of megaherbivores than can be compatible with plant diversity. Baxter et al. (2005) provided general information for African savanna in this regards, and suggested that a negative effect on plant diversity could only be observed at a density of elephant higher than one elephant/km2. Specific studies that address this question in the KNP are urgently needed for management purposes.
Given the patchy distribution of megaherbivores (e.g. elephants) in the park due to patchy distribution of resources, such studies should look at the question in high- density area vs. low-density area of the park.
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Chapter 7
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Supplementary information
Supplementary Information Table S1 Voucher information and GenBank/EBI accession numbers for trees and shrubs of the
Kruger National Park.
GenBank Accession
Family APGIII Genus & Species Voucher (Herbarium) Numbers
rbcLa matK
Acanthaceae Anisotes formosissimus (Klotzsch) Milne-Redh. OM 868 (JRAU) JF265288 JF270643
Acanthaceae Anisotes rogersii S.Moore OM 865 (JRAU) — —
Acanthaceae Barleria albostellata C.B.Clarke OM 899 (JRAU) JF265299 JF270653
Acanthaceae Barleria rotundifolia Oberm. OM 1327 (JRAU) JF265300 JF270654
Acanthaceae Duvernoia aconitiflora A.Meeuse OM 1816 (JRAU) JF265402 JF270752
Acanthaceae Duvernoia adhatodoides E.Mey. OM 1759 (JRAU) JF265403 JF270753
Acanthaceae Metarungia longistrobus (C.B.Clarke) Baden CS 15 (JRAU) JF265518 JF270864
Acanthaceae Ruspolia hypocrateriformis (Vahl) Milne-Redh. OM 777 (JRAU) JF265577 JF270920
Acanthaceae Ruttya ovata Harv. OM 1150 (JRAU) JF265578 JF270921
139 Supplementary information
Achariaceae Kiggelaria africana L. OM 1978 (JRAU) JF265491 JF270838
Achariaceae Xylotheca kraussiana Hochst. OM 1825 (JRAU) JF265662 JF271003
Amborellaceae Amborella trichopoda Baill. — L12628 AJ506156
Anacardiaceae Harpephyllum caffrum Bernh. ex Krauss OM 1555 (JRAU) JF265467 JF270814
Anacardiaceae Lannea discolor Engl. RL 1235 (JRAU) JF265496 JF270843
Anacardiaceae Lannea edulis Engl OM 1971 (JRAU) JF265497 JF270844
Anacardiaceae Lannea schweinfurthii Engl. RL 1108 (JRAU) JF265498 JF270845
Anacardiaceae Ozoroa engleri R.Fern. & A.Fern. OM 1154 (JRAU) JF265536 JF270879
Anacardiaceae Ozoroa obovata (Oliv.) R.Fern. & A.Fern. OM 840 (JRAU) JF265537 JF270880
Anacardiaceae Ozoroa sphaerocarpa R.Fern. & A.Fern. OM 940 (JRAU) JF265538 JF270881
Anacardiaceae Sclerocarya birrea Hochst. subsp. caffra (Sond.)
J.O.Kokwaro OM 498 (JRAU) JF265586 JF270929
Anacardiaceae Searsia chirindensis (Baker f.) Moffett OM 1987 (JRAU) JF265588 JF270931
Anacardiaceae Searsia gueinzii (Sond.) F.A.Barkley OM 248 (JRAU) JF265589 JF270932
140 Supplementary information
Anacardiaceae Searsia leptodictya (Diels) T.S.Yi, A.J.Mill. &
J.Wen RBN 205 (JRAU) JF265590 JF270933
Anacardiaceae Searsia magalismontana (Sond.) Moffett OM 1836 (JRAU) JF265591 JF270934
Anacardiaceae Searsia pentheri (Zahlbr.) Moffett OM 942 (JRAU) JF265592 JF270935
Anacardiaceae Searsia pyroides (Burch.) Moffett OM 713 (JRAU) JF265593 JF270936
Anacardiaceae Searsia transvaalensis (Engl.) Moffett OM 943 (JRAU) JF265594 JF270937
Annonaceae Annona senegalensis Pers. OM 1214 (JRAU) JF265289 JF270644
Annonaceae Artabotrys brachypetalus Benth. OM 1298 (JRAU) JF265293 JF270647
Annonaceae Hexalobus monopetalus Engl. & Diels OM 753 (JRAU) JF265472 JF270819
Annonaceae Monanthotaxis caffra (Sond.) Verdc. OM 276 (JRAU) JF265520 JF270866
Annonaceae Monodora junodii Engl. & Diels RBN 288 (JRAU) — —
Annonaceae Uvaria gracilipes N.Robson OM 1960 (JRAU) JF265642 JF270983
Annonaceae Uvaria lucida Boj. ex Sweet RBN 138 (JRAU) JF265643 JF270984
Annonaceae Xylopia parviflora Benth. RBN 255 (JRAU) JF265661 JF271002
141 Supplementary information
Apiaceae Heteromorpha arborescens Cham. & Schltdl. OM 592 (JRAU) JF265470 JF270817
Apiaceae Steganotaenia araliacea Hochst. OM 566 (JRAU) JF265604 JF270946
Apocynaceae Acokanthera oppositifolia (Lam.) Codd OM 1782 (JRAU) JF265265 JF270622
Apocynaceae Acokanthera rotundata (Codd) Kupicha OM 2009 (JRAU) JF265266 JF270623
Apocynaceae Adenium multiflorum Klotzsch OM 786 (JRAU) JF265270 JF270626
Apocynaceae Adenium swazicum Stapf OM 538 (JRAU) JF265271 JF270627
Apocynaceae Carissa bispinosa Desf. OM 253 (JRAU) JF265326 JF270679
Apocynaceae Carissa edulis Vahl OM 1692 (JRAU) JF265327 JF270680
Apocynaceae Carissa tetramera Stapf OM 1492 (JRAU) JF265328 JF270681
Apocynaceae Diplorhynchus condylocarpon (Müll. Arg.) Pichon OM 1498 (JRAU) JF265394 JF270746
Apocynaceae Holarrhena pubescens Wall. OM 1926 (JRAU) JF265476 JF270823
Apocynaceae Landolphia kirkii Dyer RBN 295 (JRAU) — —
Apocynaceae Pachypodium saundersii N.E.Br. OM 463 (JRAU) JF265539 JF270882
Apocynaceae Rauvolfia caffra Sond. RBN 216 (JRAU) JF265569 JF270912
142 Supplementary information
Apocynaceae Strophanthus kombe Oliv. RBN 128 (JRAU) JF265607 JF270949
Apocynaceae Strophanthus petersianus Klotzsch OM 1616 (JRAU) JF265608 JF270950
Apocynaceae Tabernaemontana elegans Stapf RBN 176 (JRAU) JF265618 JF270961
Apocynaceae Tabernaemontana ventricosa Hochst. ex A.DC. OM 1877 (JRAU) JF265619 —
Apocynaceae Wrightia natalensis Stapf RBN 337 (JRAU) JF265654 JF270995
Araliaceae Cussonia natalensis Sond. OM 975 (JRAU) JF265381 JF270733
Araliaceae Cussonia spicata Thunb. OM 1553 (JRAU) JF265382 JF270734
Arecaceae Borassus aethiopium Mart. OM 1318 (JRAU) JF265306 JF270659
Arecaceae Hyphaene coriacea Gaertn. OM 755 (JRAU) JF265482 JF270829
Arecaceae Hyphaene petersiana Klotzsch ex Mart. OM 236 (JRAU) JF265483 JF270830
Arecaceae Phoenix reclinata Jacq. OM 274 (JRAU) JF265548 JF270891
Asparagaceae Dracaena aletriformis (Haw.) J.J.Bos OM 1778 (JRAU) JF265398 —
Asphodelaceae Aloe excelsa A.Berger OM 1621 (JRAU) JF265284 JF270640
Asphodelaceae Aloe marlothii A.Berger OM 1490 (JRAU) JF265285 JF270641
143 Supplementary information
Asphodelaceae Aloe spicata L.f. OM 1522 (JRAU) JF265286 JF270642
Asteraceae Brachylaena huillensis O.Hoffm. OM 247 (JRAU) JF265311 JF270664
Asteraceae Brachylaena transvaalensis Hutchinson ex
Phillips & Schweick. OM 571 (JRAU) JF265312 JF270665
Asteraceae Vernonia aurantiaca N.E.Br. OM 877 (JRAU) JF265648 JF270989
Asteraceae Vernonia colorata Drake OM 980 (JRAU) JF265649 JF270990
Balanitaceae Balanites maughamii Sprague OM 223 (JRAU) JF265296 JF270650
Balanitaceae Balanites pedicellaris Mildbr. & Schltr. OM 901 (JRAU) JF265297 JF270651
Bignoniaceae Kigelia africana Benth. OM 217 (JRAU) JF265490 JF270837
Bignoniaceae Markhamia zanzibarica K.Schum. OM 629 (JRAU) JF265516 JF270862
Bignoniaceae Rhigozum zambesiacum Baker OM 1324 (JRAU) JF265571 JF270914
Bignoniaceae Tecomaria capensis (Thunb.) Spach OM 1475 (JRAU) JF265623 JF270965
Boraginaceae Cordia caffra Sond. OM 1561 (JRAU) JF265366 JF270719
Boraginaceae Cordia grandicalyx Oberm. OM 837 (JRAU) JF265367 JF270720
144 Supplementary information
Boraginaceae Cordia monoica Roxb. OM 1423 (JRAU) JF265368 JF270721
Boraginaceae Cordia ovalis R.Br. ex DC. OM 1311 (JRAU) JF265369 JF270722
Boraginaceae Cordia sinensis Lam. OM 354 (JRAU) JF265370 JF270723
Boraginaceae Ehretia amoena Klotzsch OM 1123 (JRAU) JF265404 JF270754
Boraginaceae Ehretia rigida Druce OM 1235 (JRAU) JF265405 JF270755
Burseraceae Commiphora africana (A.Rich) Engl. OM 769 (JRAU) JF265357 JF270710
Burseraceae Commiphora edulis Engl. RBN 341 (JRAU) JF265358 JF270711
Burseraceae Commiphora glandulosa Schinz RBN 160 (JRAU) JF265359 JF270712
Burseraceae Commiphora harveyi Engl. OM 1415 (JRAU) JF265360 JF270713
Burseraceae Commiphora marlothii Engl. OM 1587 (JRAU) JF265361 JF270714
Burseraceae Commiphora mollis Engl. OM 328 (JRAU) JF265362 JF270715
Burseraceae Commiphora neglecta Verdoorn RL 1343 (JRAU) JF265363 JF270716
Burseraceae Commiphora pyracanthoides Engl. OM 621 (JRAU) JF265364 JF270717
Burseraceae Commiphora schimperi Engl. OM 1361 (JRAU) JF265365 JF270718
145 Supplementary information
Canellaceae Warburgia salutaris (G.Bertol.) Chiov. OM 1853 (JRAU) JF265653 JF270994
Cannabaceae Celtis africana Burm.f. OM 1225 (JRAU) JF265333 JF270686
Cannabaceae Trema orientalis Blume RBN 246 (JRAU) JF265631 JF270972
Capparaceae Boscia albitrunca Gilg & Benedict OM 312 (JRAU) JF265307 JF270660
Capparaceae Boscia angustifolia Harv. RBN 268 (JRAU) JF265308 JF270661
Capparaceae Boscia foetida Schinz OM 296 (JRAU) JF265309 JF270662
Capparaceae Boscia mossambicensis Klotzsch RL 1212 (JRAU) JF265310 JF270663
Capparaceae Cadaba termitaria N.E.Br. OM 1930 (JRAU) JF265318 JF270671
Capparaceae Capparis fascicularis DC. subsp. fascicularis OM 1640 (JRAU) JF265323 JF270676
Capparaceae Capparis sepiaria subsp. glabrata OM 1604 (JRAU) JF265324 JF270677
Capparaceae Capparis tomentosa Lam. OM 232 (JRAU) JF265325 JF270678
Capparaceae Maerua angolensis DC. OM 1186 (JRAU) JF265507 JF270853
Capparaceae Maerua cafra Pax OM 1975 (JRAU) JF265508 JF270854
Capparaceae Maerua decumbens (Brongn.) DeWolf OM 1928 (JRAU) JF265509 JF270855
146 Supplementary information
Capparaceae Maerua juncea Pax subsp. crustata (Wild) Wild OM 1905 (JRAU) JF265510 JF270856
Capparaceae Maerua parvifolia Pax OM 1108 (JRAU) JF265511 JF270857
Capparaceae Maerua rosmarinoides Gilg & Benedict OM 1998 (JRAU) JF265512 JF270858
Capparaceae Thilachium africanum Lour. OM 823 (JRAU) JF265628 JF270970
Celastraceae Catha edulis (Vahl) S.Endlicher OM 482 (JRAU) JF265331 JF270684
Celastraceae Elaeodendron transvaalense (Burtt Davy)
R.H.Archer OM 403 (JRAU) JF265407 JF270757
Celastraceae Gymnosporia heterophylla (Eckl. & Zeyh.) Loes. OM 623 (JRAU) JF265458 JF270805
Celastraceae Gymnosporia maranguensis Loes. OM 1637 (JRAU) JF265459 JF270806
Celastraceae Gymnosporia oxycarpa (N.Robson) Jordaan OM 1913 (JRAU) JF265460 JF270807
Celastraceae Gymnosporia pubescens (N.Robson) M.Jordaan OM 1929 (JRAU) JF265461 JF270808
Celastraceae Gymnosporia putterlickioides Loes. ex Engl. RBN 242 (JRAU) JF265462 JF270809
Celastraceae Gymnosporia senegalensis Loes. RBN 294 (JRAU) JF265463 JF270810
Celastraceae Gymnosporia sp. C NQ 5 (JRAU) JF265464 JF270811
147 Supplementary information
Celastraceae Loeseneriella crenata (Klotzsch) R. Wilczek ex
N. Hallé OM 1270 (JRAU) JF265504 JF270851
Celastraceae Maytenus undata (Thunb.) Blakelock OM 1469 (JRAU) JF265517 JF270863
Celastraceae Mystroxylon aethiopicum subsp. schlechteri
(Loes.) R.H.Archer OM 243 (JRAU) JF265523 JF270869
Celastraceae Pristimera indica (Willd.) A.C. Sm. OM 1925 (JRAU) — —
Celastraceae Pristimera longipetiolata (Oliv.) N. Hallé OM 393 (JRAU) JF265558 JF270901
Celastraceae Putterlickia verrucosa Sim OM 1404 (JRAU) JF265566 JF270909
Celastraceae Salacia kraussii Harv. RBN 102 (JRAU) JF265579 JF270922
Chrysobalanaceae Parinari curatellifolia Planch. ex Benth. RBN 357 (JRAU) JF265541 JF270884
Clusiaceae Garcinia livingstonei T.Anders. OM 246 (JRAU) JF265444
Combretaceae Combretum apiculatum Sond. subsp. apiculatum OM 1068 (JRAU) JF265344 JF270697
Combretaceae Combretum celastroides Welw. ex M.A.Lawson
subsp. orientale Exell OM & MvdB 27 (JRAU) — —
148 Supplementary information
Combretaceae Combretum cf. mkuzense J.D.Carr & Retief RBN 154.2 (JRAU) JF265345 JF270698
Combretaceae Combretum collinum Fresen. subsp. taborense
(Engl. & Diels) OM 1610 (JRAU) JF265346 JF270699
Combretaceae Combretum erythrophyllum (Burch.) Sond. RL 1466 (JRAU) JF265347 JF270700
Combretaceae Combretum hereroense Schinz OM 238 (JRAU) JF265348 JF270701
Combretaceae Combretum imberbe Wawra RBN 336 (JRAU) JF265349 JF270702
Combretaceae Combretum kraussii Hochst. OM 1536 (JRAU) JF265350 JF270703
Combretaceae Combretum microphyllum Klotzsch OM 205 (JRAU) JF265351 JF270704
Combretaceae Combretum molle Engl. & Diels OM 1526 (JRAU) JF265352 JF270705
Combretaceae Combretum mossambicense (Klotzsch) Engl. RL 1537 (JRAU) JF265353 JF270706
Combretaceae Combretum padoides Engl. & Diels OM 1285 (JRAU) JF265354 JF270707
Combretaceae Combretum woodii Dummer OM 1421 (JRAU) JF265355 JF270708
Combretaceae Combretum zeyheri Sond. OM 295 (JRAU) JF265356 JF270709
Combretaceae Pteleopsis myrtifolia Engl. & Diels OM 1283 (JRAU) JF265563 JF270905
149 Supplementary information
Combretaceae Terminalia phanerophlebia Engl. & Diels OM 1179 (JRAU) JF265624 JF270966
Combretaceae Terminalia prunioides M.Laws. OM 1061 (JRAU) JF265625 JF270967
Combretaceae Terminalia sericea Burch. ex DC. OM 478 (JRAU) JF265626 JF270968
Ebenaceae Diospyros dichrophylla (Gand.) De Winter OM 1758 (JRAU) JF265388 JF270740
Ebenaceae Diospyros lycioides Desf. OM 965 (JRAU) JF265389 JF270741
Ebenaceae Diospyros mespiliformis Hochst. ex A.DC. OM 218 (JRAU) JF265390 JF270742
Ebenaceae Diospyros natalensis (Harv.) Brenan OM 1763 (JRAU) JF265391 JF270743
Ebenaceae Diospyros villosa (L.) De Winter OM 1575 (JRAU) JF265392 JF270744
Ebenaceae Diospyros whyteana (Hiern) F.White OM 1805 (JRAU) JF265393 JF270745
Ebenaceae Euclea daphnoides Hiern OM 1381 (JRAU) JF265422 JF270771
Ebenaceae Euclea divinorum Hiern OM 1102 (JRAU) JF265418 JF270767
Ebenaceae Euclea natalensis A.DC. OM 211 (JRAU) JF265420 JF270769
Ebenaceae Euclea natalensis A.DC. RL 1166 (JRAU) JF265419 JF270768
Ebenaceae Euclea undulata Thunb. OM 1206 (JRAU) JF265423 JF270772
150 Supplementary information
Erythroxylaceae Erythroxylum delagoense Schinz OM 1499 (JRAU) JF265416 JF270765
Erythroxylaceae Erythroxylum emarginatum Thonn. OM 1531 (JRAU) JF265417 JF270766
Euphorbiaceae Acalypha glabrata Thunb. OM 1375 (JRAU) JF265264 JF270621
Euphorbiaceae Alchornea laxiflora Pax & K.Hoffm. RBN 144 (JRAU) JF265282 JF270638
Euphorbiaceae Clutia pulchella L. RBGK 5876 (K) AM234976 —
Euphorbiaceae Croton gratissimus Burch. OM 785 (JRAU) JF265374 JF270727
Euphorbiaceae Croton madandensis S.Moore OM 979 (JRAU) JF265375 —
Euphorbiaceae Croton megalobotrys Müll.Arg. OM 774 (JRAU) JF265376 JF270728
Euphorbiaceae Croton menyharti Pax RL 1503 (JRAU) JF265377 JF270729
Euphorbiaceae Croton pseudopulchellus Pax RBN 186 (JRAU) JF265378 JF270730
Euphorbiaceae Croton steenkampianus Gerstner RBN 364 (JRAU) JF265379 JF270731
Euphorbiaceae Croton sylvaticus Hochst. OM 1550 (JRAU) JF265380 JF270732
Euphorbiaceae Euphorbia cooperi N.E.Br. ex A.Berger OM 1464 (JRAU) JF265425 JF270774
Euphorbiaceae Euphorbia espinosa Pax RBN 189 (JRAU) JF265426 JF270775
151 Supplementary information
Euphorbiaceae Euphorbia rowlandii R.A.Dyer RBN 263 (JRAU) JF265427 JF270776
Euphorbiaceae Euphorbia tirucalli L. RBN 274 (JRAU) JF265428 JF270777
Euphorbiaceae Ricinus communis L. OM 1359 (JRAU) JF265575 JF270918
Euphorbiaceae Spirostachys africanus Sond. OM 254 (JRAU) JF265602 JF270944
Euphorbiaceae Suregada africana Kuntze OM 1839 (JRAU) JF265615 JF270957
Euphorbiaceae Synadenium cupulare (Boiss.) Wheeler ex
A.White OM 757 (JRAU) JF265616 JF270958
Fabaceae Acacia ataxacantha DC. OM 1046 (JRAU) JF265242 JF270600
Fabaceae Acacia borleae Burtt Davy OM 314 (JRAU) JF265243 JF270601
Fabaceae Acacia brevispica Harms RL 1333 (JRAU) JF265244 JF270602
Fabaceae Acacia burkei Benth. OM 705 (JRAU) JF265245 —
Fabaceae Acacia caffra Willd. RBN 177 (JRAU) JF265246 JF270603
Fabaceae Acacia davyi N.E.Br. in Burrt-D RL 1315 (JRAU) JF265247 JF270604
Fabaceae Acacia erubescens Welw. ex Oliver OM 780 (JRAU) JF265248 JF270605
152 Supplementary information
Fabaceae Acacia exuvialis Verdoorn OM 260 (JRAU) JF265249 JF270606
Fabaceae Acacia gerrardii Benth. RL 1103 (JRAU) JF265250 JF270607
Fabaceae Acacia grandicornuta Gerstner OM 337 (JRAU) JF265251 JF270608
Fabaceae Acacia karroo Hayne RL 1282 (JRAU) JF265252 JF270609
Fabaceae Acacia luederitzii Engl. var. luederitzii RL 1306 (JRAU) JF265253 JF270610
Fabaceae Acacia nigrescens Oliver OM 225 (JRAU) JF265254 JF270611
Fabaceae Acacia nilotica (L.) Delile OM 626 (JRAU) JF265255 JF270612
Fabaceae Acacia robusta Burch. subsp. clavigera (E. Mey.) RBN 354 (JRAU) JF265256 JF270613
Fabaceae Acacia schweinfurthii Brenan & Exell OM 604 (JRAU) JF265257 JF270614
Fabaceae Acacia senegal Willd. OM 255 (JRAU) JF265258 JF270615
Fabaceae Acacia sieberiana DC. OM 966 (JRAU) JF265259 JF270616
Fabaceae Acacia swazica Burtt Davy RL 1327 (JRAU) JF265260 JF270617
Fabaceae Acacia tortilis Hayne OM 261 (JRAU) JF265261 JF270618
Fabaceae Acacia welwitschii Oliver subsp. delagoensis OM 239 (JRAU) JF265262 JF270619
153 Supplementary information
(Harms) J.H.Ross & Brenan
Fabaceae Acacia xanthophloea Benth. OM 298 (JRAU) JF265263 JF270620
Fabaceae Adenopodia spicata C.Presl SA 193.5 (JRAU) JF265272 JF270628
Fabaceae Afzelia quanzensis Welw. OM 291 (JRAU) JF265273 JF270629
Fabaceae Albizia adianthifolia W.Wight OM 1811 (JRAU) JF265274 JF270630
Fabaceae Albizia anthelmintica Brongn. OM 363 (JRAU) JF265275 JF270631
Fabaceae Albizia brevifolia Schinz OM 826 (JRAU) JF265276 JF270632
Fabaceae Albizia forbesii Benth. OM 228 (JRAU) JF265277 JF270633
Fabaceae Albizia harveyi Fourn. OM 1402 (JRAU) JF265278 JF270634
Fabaceae Albizia petersiana Oliver OM 745 (JRAU) JF265279 JF270635
Fabaceae Albizia tanganyicensis Baker f. OM 1972 (JRAU) JF265280 JF270636
Fabaceae Albizia versicolor Welw. ex Oliver OM 560 (JRAU) JF265281 JF270637
Fabaceae Baphia massaiensis Taub. RBN 130 (JRAU) JF265298 JF270652
Fabaceae Bauhinia galpinii N.E.Br. OM 279 (JRAU) JF265301 —
154 Supplementary information
Fabaceae Bolusanthus speciosus Harms OM 240 (JRAU) JF265305 JF270658
Fabaceae Burkea africana Hook. RBN 235 (JRAU) JF265317 JF270670
Fabaceae Calpurnia aurea (Lam.) Benth. OM 1532 (JRAU) JF265320 JF270673
Fabaceae Cassia abbreviata Oliver OM 235 (JRAU) JF265329 JF270682
Fabaceae Colophospermum mopane (J.Kirk ex Benth.)
J.Léonard OM 778 (JRAU) JF265343 JF270696
Fabaceae Cordyla africana Lour. OM 1210 (JRAU) JF265371 JF270724
Fabaceae Crotalaria laburnifolia L. subsp. australis (Baker
f.) Polhill OM 608 (JRAU) JF265373 JF270726
Fabaceae Dalbergia armata E.Mey. OM 1414 (JRAU) JF265383 JF270735
Fabaceae Dalbergia melanoxylon Guill. & Perr. OM 268 (JRAU) JF265384 JF270736
Fabaceae Dichrostachys cinerea (L.) Wight & Arn. subsp.
africana Brenan & Brummitt RBN 359 (JRAU) JF265387 JF270739
Fabaceae Elephantorrhiza burkii Benth. OM 1635 (JRAU) JF265408 JF270758
155 Supplementary information
Fabaceae Elephantorrhiza elephantina Skeels OM 483 (JRAU) JF265409 JF270759
Fabaceae Elephantorrhiza goetzei (Harms) Harms OM 812 (JRAU) JF265410 JF270760
Fabaceae Erythrina humeana Spreng. OM 741 (JRAU) JF265413 JF270763
Fabaceae Erythrina latissima E.Mey. OM 1428 (JRAU) JF265414 —
Fabaceae Erythrina lysistemon Hutch. RBN 329 (JRAU) JF265415 JF270764
Fabaceae Faidherbia albida (Delile) A.Chev. RBN 165 (JRAU) JF265429 JF270778
Fabaceae Guibourtia conjugata (Bolle) J.Léonard OM 1287 (JRAU) JF265457 JF270804
Fabaceae Indigofera fulgens Baker RBN 155 (JRAU) JF265484 JF270831
Fabaceae Indigofera tinctoria L. var. arcuata J.B. Gillett OM 1933 (JRAU) JF265485 JF270832
Fabaceae Mundulea sericea (Willd.) A.Chev. OM 338 (JRAU) JF265522 JF270868
Fabaceae Newtonia hildebrandtii (Vatke) Torre SA 191 (JRAU) — —
Fabaceae Ormocarpum trichocarpum (Taub.) Engl. OM 1437 (JRAU) JF265535 JF270878
Fabaceae Peltophorum africanum Sond. OM 271 (JRAU) JF265546 JF270889
Fabaceae Philenoptera violacea (Klotzsch) Schrire OM 242 (JRAU) JF265547 JF270890
156 Supplementary information
Fabaceae Piliostigma thonningii (Schumach.) Milne-Redh. OM 277 (JRAU) JF265551 —
Fabaceae Pseudarthria hookeri Wight & Arn. OM 1473 (JRAU) JF265559 JF270902
Fabaceae Pterocarpus angolensis DC. OM 490 (JRAU) JF265564 JF270906
Fabaceae Pterocarpus rotundifolius Druce RL 1181 (JRAU) JF265565 JF270907
Fabaceae Pterolobium stellatum (Forssk.) Brenan RBN 219 (JRAU) — JF270908
Fabaceae Schotia brachypetala Sond. OM 252 (JRAU) JF265583 JF270926
Fabaceae Schotia capitata Bolle OM 1159 (JRAU) JF265584 JF270927
Fabaceae Senna petersiana (Bolle) Lock OM 987 (JRAU) JF265596 JF270938
Fabaceae Xanthocercis zambesiaca (Baker) Dumaz-le-
Grand OM 500 (JRAU) JF265655 JF270996
Fabaceae Xeroderris stuhlmannii (Taubert) Mendonca &
E.P.deSousa RBN 158 (JRAU) JF265656 JF270997
Fabaceae Xylia torreana Brenan RBN 171 (JRAU) JF265660 JF271001
Gentianaceae Anthocleista grandiflora Gilg OM 262 (JRAU) JF265290 JF270645
157 Supplementary information
Hernandiaceae Gyrocarpus americanus Jacq. subsp. africanus
Kubitzki OM 874 (JRAU) JF265465 JF270812
Icacinaceae Apodytes dimidiata E.Mey. ex Arn. OM 1560 (JRAU) JF265292 JF270646
Icacinaceae Cassinopsis ilicifolia (Hochst.) Kuntze OM 1892 (JRAU) JF265330 JF270683
Kirkiaceae Kirkia acuminata Oliver OM 758 (JRAU) JF265492 JF270839
Kirkiaceae Kirkia wilmsii Engl. RL 1230 (JRAU) JF265493 JF270840
Lamiaceae Clerodendrum glabrum E.Mey. OM 768 (JRAU) JF265341 JF270694
Lamiaceae Karomia speciosa (Hutch. & Corbish.) R.Fern. OM 700 (JRAU) JF265489 JF270836
Lamiaceae Leonotis nepetifolia (L.) R.Br. RBN 258 (JRAU) JF265501 JF270848
Lamiaceae Premna mooiensis W.Piep. OM 1424 (JRAU) JF265557 JF270900
Lamiaceae Pycnostachys reticulata (E.Mey.) Benth. OM 1992 (JRAU) JF265567 JF270910
Lamiaceae Tetradenia riparia (Hochst.) Codd OM 881 (JRAU) JF265627 JF270969
Lamiaceae Tinnea rhodesiana S.Moore OM 1303 (JRAU) JF265629 JF270971
Lamiaceae Vitex ferruginea Schumach. & Thonn. RBN 141 (JRAU) JF265650 JF270991
158 Supplementary information
Lamiaceae Vitex harveyana H.Pearson OM 528 (JRAU) JF265651 JF270992
Lamiaceae Vitex patula E.A.Bruce OM 1300 (JRAU) JF265652 JF270993
Linaceae Hugonia orientalis Engl. RBN 145 (JRAU) JF265478 JF270825
Loganiaceae Strychnos decussata (Pappe) Gilg OM 292 (JRAU) JF265609 JF270951
Loganiaceae Strychnos madagascariensis Poir. RL 1538 (JRAU) JF265610 JF270952
Loganiaceae Strychnos potatorum L.f. RBN 179 (JRAU) JF265611 JF270953
Loganiaceae Strychnos pungens Solered. MvdB 22 (JRAU) JF265612 JF270954
Loganiaceae Strychnos spinosa Lam. OM 220 (JRAU) JF265613 JF270955
Loganiaceae Strychnos usambarensis Gilg ex Engl. OM 2006 (JRAU) JF265614 JF270956
Lythraceae Galpinia transvaalica N.E.Br. OM 319 (JRAU) JF265443 JF270791
Malpighiaceae Acridocarpus natalitius A.Juss. OM 2034 (JRAU) JF265267 JF270624
Malpighiaceae Triaspis hypericoides Burch. subsp. nelsonii
(Oliv.) OM 1263 (JRAU) JF265632 JF270973
Malvaceae Abutilon angulatum (Guill. & Perr.) Mast. OM 822 (JRAU) JF265241 JF270599
159 Supplementary information
Malvaceae Adansonia digitata L. OM 747 (JRAU) JF265268 JF270625
Malvaceae Azanza garckeana (F.Hoffm.) Exell & Hillc. OM 1865 (JRAU) JF265294 JF270648
Malvaceae Dombeya cymosa Harv. OM 898 (JRAU) JF265395 JF270747
Malvaceae Dombeya rotundifolia Planch. RL 1161 (JRAU) JF265396 JF270748
Malvaceae Gossypium herbaceum L. OM 907 (JRAU) JF265447 JF270794
Malvaceae Grewia bicolor Juss. OM 329 (JRAU) JF265448 JF270795
Malvaceae Grewia caffra Meisn. RBN 182 (JRAU) JF265449 JF270796
Malvaceae Grewia flavescens Juss. OM 323 (JRAU) JF265450 JF270797
Malvaceae Grewia gracillima Wild OM 870 (JRAU) JF265451 JF270798
Malvaceae Grewia hexamita Burret OM 351 (JRAU) JF265452 JF270799
Malvaceae Grewia inaequilatera Garcke OM 872 (JRAU) JF265453 JF270800
Malvaceae Grewia microthyrsa K.Schum. ex Burret OM 1286 (JRAU) — —
Malvaceae Grewia monticola Sond. OM 1954 (JRAU) JF265454 JF270801
Malvaceae Grewia sulcata Mast. OM 871 (JRAU) JF265455 JF270802
160 Supplementary information
Malvaceae Grewia villosa Willd. OM 392 (JRAU) JF265456 JF270803
Malvaceae Hibiscus calyphyllus Cav. OM 381 (JRAU) JF265473 JF270820
Malvaceae Hibiscus micranthus L.f. OM 435 (JRAU) JF265474 JF270821
Malvaceae Sterculia murex Hemsl. RL 1229 (JRAU) JF265605 JF270947
Malvaceae Sterculia rogersii N.E.Br. OM 1227 (JRAU) JF265606 JF270948
Malvaceae Triumfetta pilosa Roth RBN 231 (JRAU) JF265638 JF270979
Meliaceae Ekebergia capensis Sparrm. OM 1540 (JRAU) — JF270756
Meliaceae Ekebergia pterophylla (C. DC.) Hofmeyr OM 2017 (JRAU) JF265406 —
Meliaceae Entandrophragma caudatum Sprague OM 794 (JRAU) JF265412 JF270762
Meliaceae Trichilia dregeana Harv. & Sond. OM 1793 (JRAU) JF265635 JF270976
Meliaceae Trichilia emetica Vahl OM 1178 (JRAU) JF265636 JF270977
Meliaceae Turraea floribunda Hochst CS 21 (JRAU) JF265639 JF270980
Meliaceae Turraea nilotica Kotschy & Peyr. OM 1497 (JRAU) JF265640 JF270981
Meliaceae Turraea obtusifolia Hochst. OM 744 (JRAU) JF265641 JF270982
161 Supplementary information
Melianthaceae Bersama lucens Szyszył. OM 1562 (JRAU) JF265304 JF270657
Moraceae Ficus abutilifolia Miq. OM 280 (JRAU) — —
Moraceae Ficus burkei Miq. OM 972 (JRAU) JF265432 JF270781
Moraceae Ficus glumosa Delile RL 1407 (JRAU) JF265433 —
Moraceae Ficus ingens Miq. OM 593 (JRAU) JF265434 JF270782
Moraceae Ficus petersii Warb. OM 1850 (JRAU) JF265435 JF270783
Moraceae Ficus salicifolia Vahl OM 1981 (JRAU) JF265436 JF270784
Moraceae Ficus stuhlmannii Warb. OM 749 (JRAU) JF265437 JF270785
Moraceae Ficus sur Forssk. OM 1556 (JRAU) JF265438 JF270786
Moraceae Ficus sycomorus L. OM 485 (JRAU) JF265439 JF270787
Moraceae Ficus tettensis Hutchinson OM 1354 (JRAU) JF265440 JF270788
Moraceae Maclura africana (Bur.) Corner OM 1935 (JRAU) JF265506 JF270852
Myricaceae Myrica pilulifera Rendle OM 2024 (JRAU) JF265521 JF270867
Myrtaceae Eugenia natalitia Sond. OM 1796 (JRAU) JF265424 JF270773
162 Supplementary information
Myrtaceae Heteropyxis natalensis Harv. OM 559 (JRAU) JF265471 JF270818
Myrtaceae Syzygium cordatum Hochst. ex C.Krauss RBN 304 (JRAU) JF265617 JF270959
Myrtaceae Syzygium guineense (Willd.) DC. OM 204 (JRAU) — JF270960
Ochnaceae Ochna inermis (Forssk.) Schweinf. OM 1196 (JRAU) JF265529 —
Ochnaceae Ochna natalitia Walp. OM 286 (JRAU) JF265530 —
Ochnaceae Ochna pulchra Hook. RBN 307 (JRAU) JF265531 —
Olacaceae Olax dissitiflora Oliver OM 970 (JRAU) JF265532 JF270875
Olacaceae Ximenia americana L. OM 1185 (JRAU) JF265658 JF270999
Olacaceae Ximenia caffra Sond. OM 263 (JRAU) JF265659 JF271000
Oleaceae Chionanthus foveolatus (Meyer) Stearn OM 1832 (JRAU) JF265336 JF270689
Oleaceae Chionanthus peglerae (C.H.Wright) Stearn OM 1766 (JRAU) JF265337 JF270690
Oleaceae Jasminum fluminense Vell. OM 456 (JRAU) JF265486 JF270833
Oleaceae Jasminum multipartitum Hochst. OM 1541 (JRAU) JF265487 JF270834
Oleaceae Jasminum stenolobum Rolfe OM 1325 (JRAU) JF265488 JF270835
163 Supplementary information
Oleaceae Olea europaea L. subsp. africana (Mill.)
P.S.Green OM 269 (JRAU) JF265533 JF270876
Oleaceae Schrebera alata Welw. OM 318 (JRAU) JF265585 JF270928
Onagraceae Ludwigia octovalvis (Jacq.) P.H.Raven OM 213 (JRAU) JF265505 —
Passifloraceae Adenia spinosa Burtt Davy OM 1618 (JRAU) JF265269 —
Passifloraceae Paropsia braunii Gilg — DQ123395 —
Pedaliaceae Sesamothamnus lugardii N.E.Br. ex Stapf OM 1622 (JRAU) JF265597 JF270939
Phyllanthaceae Antidesma venosum E.Mey. ex Tul. OM 808 (JRAU) JF265291
Phyllanthaceae Bridelia cathartica Bertol.f. OM 294 (JRAU) JF265314 JF270667
Phyllanthaceae Bridelia micrantha Baill. OM 1435 (JRAU) JF265315 JF270668
Phyllanthaceae Bridelia mollis Hutchinson OM 1632 (JRAU) JF265316 JF270669
Phyllanthaceae Flueggea virosa (Willd.) Royle OM 1145 (JRAU) JF265442 JF270790
Phyllanthaceae Hymenocardia ulmoides Oliver RBN 178 (JRAU) JF265479 JF270826
Phyllanthaceae Margaritaria discoidea subsp. nitida (Pax) OM 1922 (JRAU) JF265515 JF270861
164 Supplementary information
G.L.Webster
Phyllanthaceae Phyllanthus pinnatus (Wight) G.L.Webster OM 843 (JRAU) JF265549 JF270892
Phyllanthaceae Phyllanthus reticulatus Poir. OM 224 (JRAU) JF265550 JF270893
Phyllanthaceae Pseudolachnostylis maprouneaefolia Pax RBN 100 (JRAU) JF265560 —
Picrodendraceae Androstachys johnsonii Prain OM 1912 (JRAU) JF265287 —
Pittosporaceae Pittosporum viridiflorum Sims OM 1784 (JRAU) JF265552 JF270894
Plumbaginaceae Plumbago auriculata Lam. OM 1686 (JRAU) — JF270896
Plumbaginaceae Plumbago zeylanica L. RBN 352 (JRAU) JF265554 JF270897
Polygalaceae Securidaca longipedunculata Fresen. OM 1965 (JRAU) JF265595 —
Portulacaceae Portulacaria afra Jacq. OM 1257 (JRAU) JF265555 JF270898
Primulaceae Maesa lanceolata Forssk. OM 2020 (JRAU) JF265513 JF270859
Proteaceae Faurea rochetiana Chiov. ex Pic.Serm. OM 1413 (JRAU) JF265430 JF270779
Proteaceae Faurea saligna Harv. MvdB 27 (JRAU) JF265431 JF270780
Putranjavaceae Drypetes gerrardii Hutch. OM 1840 (JRAU) JF265399 —
165 Supplementary information
Putranjavaceae Drypetes reticulata Pax RBN 270 (JRAU) JF265400 JF270750
Ranunculaceae Clematis brachiata Thunb. OM 1974 (JRAU) JF265340 JF270693
Rhamnaceae Berchemia discolor Hemsl. OM 1175 (JRAU) JF265302 JF270655
Rhamnaceae Berchemia zeyheri (Sond.) Grubov OM 600 (JRAU) JF265303 JF270656
Rhamnaceae Helinus integrifolius (Lam.) Kuntze OM 2015 (JRAU) JF265469 JF270816
Rhamnaceae Rhamnus prinoides L'Hér. OM 1744 (JRAU) JF265570 JF270913
Rhamnaceae Ziziphus mucronata Willd. OM 258 (JRAU) JF265666 JF271007
Rhamnaceae Ziziphus rivularis Codd OM 1383 (JRAU) JF265667 JF271008
Rubiaceae Breonadia salicina (Vahl) Hepper & J.R.I.Wood RL 1194 (JRAU) JF265313 JF270666
Rubiaceae Canthium inerme Kuntze OM 1742 (JRAU) JF265321 JF270674
Rubiaceae Canthium setiflorum Hiern OM 882 (JRAU) JF265322 JF270675
Rubiaceae Catunaregam spinosa (Thunb.) Tirveng. OM 1406 (JRAU) JF265332 JF270685
Rubiaceae Cephalanthus natalensis Oliver OM 1583 (JRAU) JF265334 JF270687
Rubiaceae Coddia rudis (E.Mey. ex Harv.) Verdc OM 1292 (JRAU) JF265342 JF270695
166 Supplementary information
Rubiaceae Crossopteryx febrifuga Benth. OM 1924 (JRAU) JF265372 JF270725
Rubiaceae Gardenia resiniflua Hiern OM 864 (JRAU) JF265445 JF270792
Rubiaceae Gardenia volkensii K. Schum. OM 1100 (JRAU) JF265446 JF270793
Rubiaceae Heinsia crinita (Afzel.) G.Taylor RBN 129 (JRAU) JF265468 JF270815
Rubiaceae Hymenodictyon parvifolium Oliver OM 789 (JRAU) JF265480 JF270827
Rubiaceae Hyperacanthus amoenus (Sims) Bridson OM 1457 (JRAU) JF265481 JF270828
Rubiaceae Kraussia floribunda Harv. OM 924 (JRAU) JF265494 JF270841
Rubiaceae Lagynias dryadum Robyns OM 896 (JRAU) JF265495 JF270842
Rubiaceae Leptactina delagoensis K.Schum. OM 1598 (JRAU) JF265502 JF270849
Rubiaceae Pavetta catophylla K.Schum. OM 1103 (JRAU) JF265542 JF270885
Rubiaceae Pavetta edentula Sond. OM 1431 (JRAU) JF265543 JF270886
Rubiaceae Pavetta lanceolata Eckl. OM 2001 (JRAU) JF265544 JF270887
Rubiaceae Pavetta schumanniana F.Hoffm. ex K.Schum. RBN 251 (JRAU) JF265545 JF270888
Rubiaceae Plectroniella amata Robyns OM 962 (JRAU) JF265553 JF270895
167 Supplementary information
Rubiaceae Psydrax locuples (K.Schum.) Bridson OM 1639 (JRAU) JF265561 JF270903
Rubiaceae Pyrostria hystrix (Bremek.) Bridson OM 234 (JRAU) JF265568 JF270911
Rubiaceae Rothmannia fischeri (K.Schum.) Bullock in
Oberm. OM 888 (JRAU) JF265576 JF270919
Rubiaceae Tarenna supra-axillaris (Hemsl.) Bremek. OM 1951 (JRAU) JF265620 JF270962
Rubiaceae Tarenna zygoon Bridson OM 1908 (JRAU) JF265621 JF270963
Rubiaceae Tricalysia junodii (Schinz) Brenan var. junodii OM 1399 (JRAU) JF265633 JF270974
Rubiaceae Tricalysia lanceolata Burtt Davy OM 1765 (JRAU) JF265634 JF270975
Rubiaceae Vangueria infausta Burch. OM 377 (JRAU) JF265644 JF270985
Rubiaceae Vangueria madagascariensis J.F. Gmel. OM 2018 (JRAU) JF265645 JF270986
Rutaceae Calodendrum capensis Thunb. OM 1540 (JRAU) JF265319 JF270672
Rutaceae Clausena anisata (Willd.) Hook. f. ex Benth. Logie C. FBG 67 (NBG) AM235116 —
Rutaceae Ptaeroxylon obliquum Radlk. OM 815 (JRAU) JF265562 JF270904
Rutaceae Teclea pilosa Verdoorn OM 359 (JRAU) JF265622 JF270964
168 Supplementary information
Rutaceae Toddaliopsis bremekampii Verdoorn RBN 366 (JRAU) JF265630 —
Rutaceae Vepris lanceolata (Lam.) G.Don OM 1528 (JRAU) JF265646 JF270987
Rutaceae Vepris reflexa Verdoorn OM 244 (JRAU) JF265647 JF270988
Rutaceae Zanthoxylum capense Harv. OM 1775 (JRAU) JF265663 JF271004
Rutaceae Zanthoxylum humile (E.A.Bruce) Waterman OM 431 (JRAU) JF265664 JF271005
Rutaceae Zanthoxylum leprieurii Guill. & Perr. RBN 152 (JRAU) JF265665 JF271006
Salicaceae Dovyalis caffra Warb. OM 349 (JRAU) JF265397 JF270749
Salicaceae Flacourtia indica (Burm.f.) Merr. OM 581 (JRAU) JF265441 JF270789
Salicaceae Homalium dentatum Warb. OM 1508 (JRAU) JF265477 JF270824
Salicaceae Oncoba spinosa Forssk. OM 631 (JRAU) JF265534 JF270877
Salicaceae Salix mucronata Thunb. OM 1198 (JRAU) JF265580 JF270923
Salicaceae Scolopia zeyheri (Nees) Harv. OM 1552 (JRAU) JF265587 JF270930
Salicaceae Trimeria grandifolia (Hochst.) Warb. OM 1549 (JRAU) JF265637 JF270978
Salvadoraceae Azima tetracantha Lam. OM 300 (JRAU) JF265295 JF270649
169 Supplementary information
Salvadoraceae Salvadora australis Schweick. OM 1317 (JRAU) JF265581 JF270924
Salvadoraceae Salvadora persica L. OM 824 (JRAU) JF265582 JF270925
Sapindaceae Allophylus decipiens Radlk. OM 1846 (JRAU) JF265283 JF270639
Sapindaceae Deinbollia oblongifolia Radlk. OM 1774 (JRAU) JF265385 JF270737
Sapindaceae Deinbollia xanthocarpa Radlk. RBN 275 (JRAU) JF265386 JF270738
Sapindaceae Hippobromus pauciflorus Radlk. OM 1551 (JRAU) JF265475 JF270822
Sapindaceae Pappea capensis Eckl. & Zeyh. OM 380 (JRAU) JF265540 JF270883
Sapindaceae Stadmannia oppositifolia Lam. OM 863 (JRAU) JF265603 JF270945
Sapotaceae Englerophytum magalismontanum (Sonder)
T.D.Penn. RBN 290 (JRAU) JF265411 JF270761
Sapotaceae Manilkara concolor (Harv.) Gerstner RL 1218 (JRAU) — —
Sapotaceae Manilkara mochisia Dubard OM 1392 (JRAU) JF265514 JF270860
Sapotaceae Manilkara sp OM 1271 (JRAU) —
Sapotaceae Mimusops zeyheri Sond. OM 1220 (JRAU) JF265519 JF270865
170 Supplementary information
Sapotaceae Sideroxylon inerme L. OM 1760 (JRAU) JF265598 JF270940
Solanaceae Nicotiana glauca Graham OM 2010 (JRAU) JF265524 JF270870
Solanaceae Solanum catombelense Peyr. OM 934 (JRAU) JF265599 JF270941
Solanaceae Solanum lichtensteinii Willd. OM 1904 (JRAU) JF265600 JF270942
Solanaceae Solanum panduriforme Dunal OM 326 (JRAU) JF265601 JF270943
Stilbaceae Halleria lucida L. CS 16 (JRAU) JF265466 JF270813
Stilbaceae Nuxia congesta R.Br. OM & MvdB 52 (JRAU) JF265525 JF270871
Stilbaceae Nuxia floribunda Benth. OM 2025 (JRAU) JF265526 JF270872
Stilbaceae Nuxia oppositifolia Benth. OM 206 (JRAU) JF265527 JF270873
Ulmaceae Chaetachme aristata Planch. OM 275 (JRAU) JF265335 JF270688
Urticaceae Obetia tenax (N.E.Br.) Friis OM 1416 (JRAU) JF265528 JF270874
Urticaceae Pouzolzia mixta Solms OM 572 (JRAU) JF265556 JF270899
Velloziaceae Xerophyta retinervis Baker OM 516 (JRAU) JF265657 JF270998
Verbenaceae Duranta erecta L. OM 939 (JRAU) JF265401 JF270751
171 Supplementary information
Verbenaceae Lantana camara L. OM 739 (JRAU) JF265499 JF270846
Verbenaceae Lantana rugosa Thunb. OM 1238 (JRAU) JF265500 JF270847
Verbenaceae Lippia javanica Spreng. OM 215 (JRAU) JF265503 JF270850
Vitaceae Cissus cactiformis Gilg OM 594 (JRAU) JF265338 JF270691
Vitaceae Cissus cornifolia Planch. OM 1110 (JRAU) JF265339 JF270692
Vitaceae Rhoicissus revoilii Planch. OM 1258 (JRAU) JF265572 JF270915
Vitaceae Rhoicissus tomentosus (Lam.) Wild &
R.B.Drumm. OM 1546 (JRAU) JF265573 JF270916
Vitaceae Rhoicissus tridentata subsp. cuneifolia (Eckl. &
Zeyh.) N.R.Urton OM 249 (JRAU) JF265574 JF270917
172 Supplementary information
Supplementary Table S2. Values of NRI in 110 KNP-plots, with indication of plot labels; (A-P) indicate ecozones where the plots were laid out), and p- values.
Plots NRI p-values A1 2.119855 0.03* A2 1.685609 0.056 NS A3 3.583059 0.005** A4 3.209846 0.009** A5 1.889003 0.04* A6 1.251039 0.106 NS A7 3.234434 0.007** A8 1.541846 0.065 NS B1 0.764178 0.204 NS B2 2.177118 0.028* B3 0.312845 0.339 NS B4 2.415209 0.022* B5 2.889419 0.008** B6 0.823867 0.176 NS B7 -0.02045 0.486 NS B8 1.994717 0.033* B9 1.607806 0.056 NS C1 3.130329 0.01* C2 2.692955 0.014* C3 3.477835 0.005** C4 1.480792 0.069 NS C5 1.387529 0.082 NS C6 3.799116 0.005** D1 1.97448 0.035* D2 1.557799 0.064 NS D3 2.164704 0.021* D4 1.400358 0.082 NS D5 1.658455 0.06 NS D6 3.258605 0.002** D7 1.50431 0.07 NS D8 3.424889 0.002** D9 0.141103 0.42 NS D10 2.40758 0.02* D11 2.071436 0.041* D12 3.56253 0.001** D13 3.780273 0.002** D14 2.071787 0.032* E1 4.370929 0.001** E2 3.203047 0.005** E3 1.444869 0.076 NS
173 Supplementary information
E4 3.266814 0.004** E5 1.17905 0.124 NS E6 1.759509 0.056 NS F1 1.077502 0.135 NS F2 1.590732 0.066 NS F3 0.060194 0.462 NS F4 1.291868 0.106 NS F5 1.493413 0.07 NS F6 0.658327 0.202 NS F7 0.9793 0.141 NS F8 2.108971 0.036* F9 2.960389 0.006** F10 2.302632 0.022* F11 2.151061 0.034* F12 2.089669 0.034* F13 2.15774 0.027* G1 3.613605 0.001** G2 3.468257 0.002** G3 2.554069 0.018* G4 3.329771 0.004** G5 1.961138 0.033* I1 2.423625 0.022* I2 0.34229 0.363 NS I3 2.669247 0.018* I4 1.15163 0.11 NS I5 0.167753 0.43 NS I6 1.934234 0.043* I7 2.189815 0.029* J1 3.75438 0.006** J2 2.571347 0.008** J3 2.489878 0.016* K1 2.799909 0.007** K2 2.69683 0.018* K3 1.130086 0.124 NS K4 2.77477 0.015* K5 3.170609 0.012* L1 4.021254 0.004** L2 2.864477 0.011* L3 2.423077 0.021* L4 1.950028 0.046* L5 2.433123 0.032* L6 -0.58905 0.744 NS L7 0.131402 0.388 NS L8 2.103888 0.034* L9 2.058029 0.036* L10 0.194093 0.405 NS L11 1.298553 0.101 NS L12 0.585283 0.252 NS
174 Supplementary information
L13 1.203987 0.107 NS L14 -0.26139 0.589 NS L15 2.294206 0.017* M1 1.920008 0.037* M2 3.205931 0.009** M3 1.429129 0.069 NS N1 1.029717 0.139 NS N2 0.339476 0.345 NS N3 0.739511 0.194 NS N4 0.217091 0.395 NS N5 -1.42182 0.926 NS N6 1.674839 0.057 NS N7 0.405026 0.301 NS N8 2.423483 0.022* O1 1.662468 0.047* O2 1.772225 0.049* O3 -0.32367 0.621 NS O4 3.087537 0.008** O5 1.056014 0.141 NS P1 0.253312 0.396 NS P2 2.144656 0.024* P3 3.832837 0.004**
175