TITLE: Phylogenetics and phylogenetic diversity of small mammals in a heterogeneous landscape (Hluhluwe-iMfolozi Game Park, South Africa)

NAME: Beth WilliamsSTUDENT NUMBER: 1213956

DEGREE: BiologyTYPE OF PROJECT: Practical

SUPERVISOR: Prof. Mike Bruford

WORD COUNT (Summary: Max 500): 331WORD COUNT (Main document): 6808

BI3006FINAL YEAR PROJECT REPORT

2014-15

School of Biosciences

C1213956Contents

Page number

Abstract.…………………………………………………………………………………............2

Introduction…………………………………………………………………………………….3-6

Experimental procedures……………………………………………………………….…..6-11

Results………………………………………………………………………………..…….11-23

Discussion…………………………………………………………………………..……...23-28

Acknowledgements……………………………………………………………………………28

References……………………………………………………………………..…………..29-35

Appendices……………………………………………………………………………………..36

1

C1213956Phylogenetics and phylogenetic diversity of small mammals in a heterogeneous

landscape (Hluhluwe-iMfolozi game park, South Africa)

Abstract

Background: This study looks at small mammal diversity in Hluhluwe iMfolozi game

park, South Africa, and tests the two main hypotheses of “does the Black iMfolozi River

act as a potential barrier to gene flow” and “is there a correlation between species

diversity and genetic diversity within the game park”. Rodents and other small mammals

play a crucial role in ecosystem processes throughout Africa, with rodents representing

the richest mammalian diversity globally, therefore making them a valuable source of

biodiversity.

Methods: Using a grid system, 89 samples were collected either as bycatch or for

another study and as a follow on from a Master’s study. The sequences of these

samples were analysed using both the 16s and cytochrome b rRNA regions in terms of;

phylogenetic reconstruction, measures of diversity, phylogenetic diversity in relation to

rivers, and the relationship between species diversity and the genetic diversity of

Mastomys natalensis.

Results: There was no difference in the overall phylogenetic diversity for the 16s and

cytochrome b rRNA regions on either side of the Black iMfolozi River. There was no

significant correlation between the alpha diversity of all the species and the proportion

of shared alleles of Mastomys natalensis in each grid, Spearman’s rank (p-value > 0.5).

There was a strong positive correlation (p-value < 0.05, cor = 0.9688947) found

between the alpha diversity of all the species and the proportion of shared alleles of

Mastomys natalensis when the park was split into four geographic sections in relation to

three rivers.

Conclusions: Findings from this study indicate that the Black iMfolozi River does not act

as a potential or actual barrier to gene flow between small mammals. Through

inconclusive results about the relationship between the species diversity and genetic

diversity of Mastomys natalensis, this study highlights the importance of further

research needed before the acceptance of the species-genetic diversity correlation,

especially research on animal communities in a continuous habitat.

Keywords: small mammals, species-genetic diversity correlation, riverine barriers, South

Africa, phylogenetic diversity, biodiversity.

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C1213956Introduction

Biodiversity is the variety of all known and unknown living forms on the planet; it

incorporates species diversity, genetic diversity, and ecosystem diversity (Wilson 1988;

Kearns 2010). Species diversity often includes both species richness (the number of

species) and species evenness (the rarity or abundance of species) (Stirling and Wilsey

2001). Genetic diversity provides the raw material for evolution (Fisher 1930) and is the

total genetic material in a species or population, whereas ecosystem diversity not only

includes all the species in an ecosystem, but also all the abiotic factors such as rainfall,

temperature and soil (Kearne 2010). With biodiversity itself including so many factors, it

is important to try and conserve as much as possible, leading to biodiversity becoming a

global and highly political issue (Adams and Hutton 2007).

Biodiversity is being lost at an alarming rate, with a range of human activities including

habitat loss through deforestation, and climate change contributing to this loss (Diroz

and Raven 2003; Stork 2010). Although conservation efforts by governmental and non-

governmental organisations have had a positive impact on reducing the loss of many

species that may have almost certainly faced extinction (Stork 2010), biodiversity is still

continuing to decline (Butchart et al. 2010). The loss of biodiversity at a global and local

scale has implications on human societies though a reduction in the efficiency of

ecosystem services and functions such as nutrient recycling and the production of

biological resources (Cardinale et al. 2012).

Phylogenetic diversity is a measure of biodiversity based on evolutionary relationships

(Winter et al. 2013) where the quantitative measure of phylogenetic diversity is defined

as ‘the minimum total length of all the phylogenetic branches required to span a given

set of taxa on the phylogenetic tree’ (Faith 1992a). Phylogenetic trees are constructed

from molecular data, including DNA and protein sequences (Hall 2013). By using DNA

sequences to construct a tree, a phylogeny can be produced and the phylogenetic

diversity can be calculated. It has been suggested that conservation efforts should focus

on conserving greater phylogenetic diversity (Faith 1992b), leading to the creation of

conservation hotspots and priority areas. Phylogenetic diversity is an important feature

of communities and thus their habitat and the landscape. Community ecology studies

the factors that influence the community including the abundance of species, the

distribution of species, the community structure, and the biodiversity (Nielsen 2014).

Phylogenetic information can highlight the role evolution has in community assemblages

(Bares et al. 2009) and although ‘phylogenetic community ecology’ is highly debated as

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C1213956a new field of ecology, the importance of phylogenetic diversity in landscapes, habitats

and communities is recognised (Webb et al. 2002).

Alpha, beta and gamma diversity is a measure of diversity and are principal descriptive

variables of conservation biology and ecology (Jost 2007). Also referred to as three

levels of diversity, gamma diversity is often partitioned into alpha diversity and beta

diversity (Whittaker 1960). Alpha diversity is frequently referred to as the local diversity

and is the diversity within a habitat or sample; the alpha diversity is calculated by the

number of species (species richness) in each sample area (Whittaker et al 2001; 1972;

1960). Beta diversity, also termed turnover diversity, is the amount of compositional

variation in a sample or habitat (differential between samples of habitats), and gamma

diversity is regional or landscape diversity and is the overall diversity in a collection of

sample units (Whittaker et al 2001; 1972; 1960). The relationship between alpha, beta

and gamma diversity is known as Whittaker’s multiplicative law where (alpha diversity) x

(beta diversity) = (gamma diversity) (Whittaker 1972) can be calculated through

diversity indices (Jost 2006). Diversity indexes such as Simpson’s (Simpson 1949) or

Shannon’s (Shannon 1948) diversity index can quantify these terms, however these

indices are entropies and not true diversities, which can lead to confusion and

irregularities. From Shannon’s index, the alpha, beta and gamma entropies are related

by an additive relationship (Ha + Hb = Hg). However the exponential of these leads to

the true diversity, thus showing the multiplicative law between alpha, beta and gamma

diversity (Jost 2006). There is some discrepancy as to what the exact definitions and

the relationships between alpha, beta and gamma diversity are (Tuomisto 2010) such

as the dependence of beta diversity on alpha diversity and whether alpha and beta

diversity should be partitioned into independent components (Jost 2007). For the

purpose of this study I will use Whittaker’s (1972) definition as described above.

South Africa has a diverse range of landscapes rich in biodiversity (Cowling et al. 1997);

these heterogeneous landscapes are home to many species of small mammal,

including rodents and insectivores. Species from the order Rodentia, more commonly

known as rodents, comprises around 36% of African mammals, and included more

species than any other African mammal group with 95% of these species being

endemic to Africa (Happold 2013). Rodentia is the order representing the richest

mammalian diversity globally (Wilson and Reeder 2005) and is therefore a huge source

of biodiversity. Due to the widespread distribution and the amount of species present,

rodents play a crucial role in ecosystem processes throughout Africa; these include

nutrient recycling, seed dispersal, and the creation of seed beds through burrowing, a

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C1213956process known as ecological engineering (Happold 2013). Through a better

understanding of phylogenetic relationships, the species, and genetic diversity of

Rodentia, conservation hotspots can be located, conservation methods implicated, and

greater biodiversity can be conserved.

Landscape characteristics can be physical barriers to gene flow between individuals

from the same species; these physical barriers could range from bodies of water, such

as oceans and rivers to mountain ranges. Riverine barriers have been implicated to the

gene flow in a number of species, and therefore leading to the investigation of the

riverine barrier hypothesis. Both the golden crown sifaka in Madagascar (Quèmèrè et al.

2010) and the alpine stream frog in China (Li et al. 2009) are among species where

riverine barriers have affected past and/or present gene flow. The differences in genetic

structure caused by gene flow can be due to both modern and historic riverine barriers

(Diaz-Muńoz 2012) and can help us understand historic speciation events as well as

current communities and within species diversity.

Many conservation efforts focus on preserving species richness and ecosystem

diversity, with genetic diversity often being overlooked and receiving far less

consideration in biodiversity assessments (Laikre et al. 2009). Genetic variation in a

species is important in preserving that species’ evolutionary potential, and therefore is

crucial factor for the species, to be able to adapt to rapidly changing environments

(Taberlet et al. 2012). With species richness being the focus of many conservational

efforts, the relationship between species diversity and genetic diversity, also known as

the species-genetic diversity correlation (SGDC) has become a highly debated subject,

with many studies producing contradictory results (Vellend et al. 2014).

Taberlet et al. (2012) found no correlation between species richness and genetic

diversity when studying high-mountain flora in the Alps and the Carpathian mountain

ranges. Along with this study, Puşcaş et al. (2008) studied European alpine grasslands

and found no positive correlation between species richness and genetic diversity and

therefore do not support the SGDC. However studies to support the SGDC includes

work by Cleary et al. (2006) who studied butterfly communities in east Kalimantan and

Struebig et al. (2011) who found declines in species richness across fragmented

landscapes were parallel to declines in genetic diversity of some bat species in these

landscapes.

This project continues and develops a previous Masters project (Karame 2013) and

studied the phylogeny (evolutionary relationships) and phylogenetic diversity of small

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C1213956mammals (primarily rodents) in Hluhluwe-iMfolozi Game Park by looking at by-catch of

samples collected for a genetic diversity paper on the genus Mastomys (Natal

multimammate mouse). The aims of this paper are to (1) see if there is a correlation

between the genetic diversity of Mastomys natalensis and the species diversity across

the whole park, and therefore test the species-genetic diversity correlation, (2) calculate

the alpha, beta and gamma species diversity, and (3) to see if there is a correlation

between landscape level characteristics and small mammal diversity across the park.

My primary hypothesis is that there is a positive correlation between the genetic

diversity of Mastomys natalensis and the species phylodiversity across the whole park,

and therefore as the genetic diversity of Mastomys natalensis increase so does the

number of different species (species richness) and the overall phylogenetic diversity. My

secondary hypothesis is that there will be a correlation between phylodiversity and

landscape characteristics such as rivers, due to rivers being a potential barrier to

dispersal of the small mammals throughout the national park and therefore influencing

the dispersion of genes and alleles.

Experimental procedures

Study Site

Hluhluwe-iMfolozi national park is a 900km2 fenced reserve located in KwaZulu-Natal in

South Africa. It is situated between 28°00’ and 28°26’ south, 31°43’ and 32°09 east with

the park bordering on highly populated rural communities. Within the park there are

altitudes ranging from 60-750m above sea level and three main rivers including the

White Umfolozi, Black Umfolozi, and the Hluhluwe. The habitats vary from semi-

deciduous forest in North Hluhluwe to open savannah woodland in South iMfolozi. Daily

temperatures vary between a maximum of 13-35°C while the average annual rainfall

ranges from 608-709mm, with the majority of this rainfall falling between October and

March (Trinkel et al. 2008; Grange et al. 2012; Cromsigt et al. 2009).

Sampling

A landscape grid system was used, splitting Hluhluwe-iMfolozi national park into 27

grids, with each grid measuring approximately 10km North-South and 5km East-West

(see figure 1). Due to only containing a small part of the park and being inaccessible,

grid 8 was not sampled. Sherman traps (H.B. Sherman traps Inc, Florida, USA) were

used and were placed under rocks, logs, and tussocks with sampling taking place for a

maximum of 4 nights on each grid. If there was low success of trapping on the first

night, traps were set up in different locations in the same grid for subsequent trapping;

6

C1213956this was in attempt to increase the success. Ear biopsy tissue samples were collected

for DNA analysis and were stored in 70% ethanol. A range of small mammal were

captured throughout the game park and these are represented in table 1 with the grid

they were captured on, along with these 6 Mastomys natalensis samples collected by

IM Russo were included with these also being represented in table 1.

DNA extraction

A 25mg section of the ear biopsy tissue samples was manually cut; this section was

then manually broken up and minced to break down the tissue. Extraction of the total

genomic DNA from the tissue samples was carried out using a DNeasy ® tissue kit

(QIAGEN ® Hilden, Germany) following the manufactures instructions. The DNA

extraction for all 89 samples was completed prior to this study (Karame 2013).

Figure 1: A map of Hluhluwe-iMfolozi game park, South Africa, showing the location of the park in South Africa (HIP) (a) and the grid system used in sampling (b), including the individuals sampled and used in this study, which are represented by their species or genus as a colour.

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DNA amplification and primers

For the amplification of both the cytochrome b and 16s rRNA regions I used PCR

technology. For the cytochrome b, the following PCR primers were used; L14724 (TGA

YAT GAA AAA YCA TCG TTG) (Pääbo et al. 1988), and H15915 (CAT TTC AGG TTT

ACA AGA C) (Irwin et al. 1991) for the forward and reverse sequences respectively. For

16s amplification the PCR primers 16S ar-L (CGC CTG TTT ATC AAA AAC AT)

(Palumbi et al. 1991) for the forward sequence and 16S br-H (CCG GTC TGA ACT

CAG ATC ACG T) (Palumbi et al. 1991) for the reverse sequence were used. Total

reaction volumes of 25μL included 2μL of DNA extract, 1.25μL of 25mM Magnesium

(MgCl2), 0.5μL of 10μM of both the forward and reverse primers, 5μL of 10x flexi green

PCR buffer, 1.5μL of 0.2mM dNTP mix (Promega, Mad-ison, WI), 0.1μL of Taq

DNApolymerase (Perkin Elmer Cetus, Emeryville, CA). The PCR reactions were

Table 1: The amount and type of species caught and sampled at each grid, shrews were not identified at a species level and are referred to throughout as the genera they are from; Crocidura. Grid 8 was not sampled, while no small mammals were trapped in grids 20 and 27.

8

C1213956performed using a Veriti ® 96 well thermal cycler. Cycling conditions included an initial

denaturation at 95°C for 15 minutes followed by 35 cycles of denaturation at 94oC for 30

seconds, annealing at 54oC for 30 seconds and extension for 30 seconds at 72oC, this

was followed by elongation at 72oC for 10 minutes. The amplifications were then

electrophoresed on 2% agarose gel, stained with ethidium bromide for an hour and

finally were then visualised under UV light. The PCR products were diluted 1:5 and

Eurofins MWG Operon’s DNA sequencing service was then used to sequence the PCR

products in both the forward and reverse directions. The DNA amplification of 69

samples was completed prior to this study (Karame 2013) and therefore for this study

20 samples were amplified and sequenced.

Sequence analysis

Sequencher ® version 3 (Applied Biosystems) was used to evaluate the quality of the

raw sequence data for the 20 newly sequenced individuals and a consensus sequence

from the forward and reverse sequences were determined for each individual. The

consensus sequences for each individual were then exported into a fasta file; BLAST

searches of the consensus sequences were performed to ensure correct species

identification. The sequences were aligned using ClustalX (Larkin et al. 2007), in order

to generate input files for tree construction.

Phylogenetic analysis

Two datasets were analysed: these include the 16s rRNA sequences and cytochrome b

sequences. Along with these sequences, a variety of sequences were selected from

GenBank, which included the species believed to be identified, as well as a selection of

species from the Crocidura family. Three outgroup species were used: The Riverine

rabbit (Bunolagus monticularis), the Cape hare (Lepus capensis), and the Scrub hare

(Lepus saxatilis). These species were chosen to represent the outgroup as they are part

of the family Leporidae that includes all the rabbits and hares. Meredith et al. (2011) has

shown that this family is a sister group to Rodentia.

Neighbour joining (NJ) trees were created for both data sets using PAUP (Phylogenetic

analysis using parsimony) (Swofford 1991), the outgroup species were set and 1000

bootstrap replicas were performed. To create a maximum likelihood (ML) tree for both

datasets separately the pervious nexus file used had to be converted into a Phylip file,

this was completed using the program: PGD spider (version 2.0.7.4) (Lischer and

Excoffier 2012). This Phylip file was subsequently uploaded to the online program

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C1213956PhyML (version 3.0) (Guindon et al. 2010), most of the default settings were used

however 1000 instead of 100 bootstrap tests were performed.

Jmodletest2 (version 2.1.7) (Darriba et al. 2012; Guindon and Gascuel 2003) was used

to estimate the suitable model of substitution including base frequencies. These values

were included in the document executed in MrBayes (Version 3.2) (Ronquist and

Huelsenback 2003; Ronquist et al. 2011). MrBayes used the Bayesian inference

method, and was run for 5 million generations, to analyse the phylogenetic relationships

between individuals to produce Bayesian trees for both cytochrome b and 16s rRNA

sequences separately, Bayesian probability values were calculated along with the

production of the trees. Trees were then visualised in FigTree (Version 1.4.2).

Phylogenetic Diversity in relation to landscape characters

The Black Umfolozi River runs the width of the game park, thus splitting the park into

two geographic regions. Individuals were split into those that were sampled north of the

river and those that were sampled south of the river for both the cytochrome b and 16s

rRNA regions. DnaSP (DNA sequence polymorphism) (Rozas 2009) was used to

identify how many haplotypes were in each new data set for both the 16s and

cytochrome rRNA regions. Haplotypes with more than one individual in had the extra

individuals removed, leaving each haplotype with one individual; this was done to

reduce the bias of the sample sizes. Neighbour Joining trees of the haplotype

sequences were created using PAUP (Swofford 1991) and thus used to create the

phylogenetic reconstructions. Phylogenetic diversity of a tree is the sum of all the

branch lengths (Faith 1992) and using R statistical software (APE package) (Paradis

2012; Paradis et al. 2003) along with the neighbour joining trees the phylogenetic

diversity was calculated for both the cytochrome b and 16s rRNA regions for individuals

sampled north and south of the Black Umfolozi River.

The park has also been divided into four geographical regions using the main rivers as

barriers and following the same method the national park phylogenetic diversity was

then calculated in reference to two more rivers, meaning individuals were split into four

groups depending on the location they were sampled at; north of the Hluhluwe River, in

between the Hluhluwe and Black Umfolozi Rivers, in between the Black Umfolozi and

White Umfolozi Rivers and South of the White Umfolozi River (see figure 1).

Measures of diversity

10

C1213956Using a community matrix, consisting of the species sampled in each grid, R statistical

software was used in order to calculate (vegan and asbio packages) (Aho 2014;

Oksanen et al. 2008) the Alpha and Beta diversity. For the calculation of alpha diversity

in the asbio package the Simpson’s index (Simpson 1949) was used. Grids 8, 20 and

27 where no samples were collected were removed when calculating beta diversity.

Beta diversity was calculated using the Whittaker (1960) method that was applied in R

(Koleff et al. 2003). Gamma diversity was calculated using the average of the calculated

Alpha diversities multiplied by the average of the beta diversities (Whittaker 1972). The

alpha diversity was then calculated using the same method for the four sections split by

the three main rivers in the park.

Genetic diversity, phylogenetic diversity and species richness relationships

Using data collected by IM Russo for a paper on the landscape genetics of Mastomys

natalensis the proportion of shared alleles (POSA) was calculated for each grid. A

Spearman’s Rank correlation test in R statistical software was then performed on this

data along with the alpha diversity data for each grid to test for an association. A

Spearman’s Rank test was used as the data was not normally distributed according to

Shapiro-Wilk test for normality and could not be transformed (POSA: W= 0.7768, P-

value = <0.0005 Alpha: W=0.6361, P-value= <0.0001). The average percentage of

shared alleles was then calculated for the four sections split by the three main rivers,

and along with the alpha diversity for these data a Pearson’s correlation test was

performed.

Species richness was calculated for each site in reference to the three rivers from the

community matrix used to calculate the Alpha diversity. Using this and the phylogenetic

diversity scores for both cytochrome b and 16s rRNA data sets a line graph was made

in excel.

Results

DNA extraction

There was a 100% success rate of the samples collected in the field having DNA

successfully extracted from them, this was shown by the presence of bands, as each

sample was checked using gel electrophoresis.

Phylogenetic analysis

The results from Jmodeltest2 (Darriba et al. 2012; Guindon and Gascuel 2003)

demonstrated that the best fit model of nucleotide substitution for both the 16S and 11

C1213956cytochrome b datasets was HKY + I + G. Nucleotide frequencies for A, T, C and G were

also reported for cytochrome b - 0.4004, 0.2063, 0.1059 and 0.2874, and 16s - 0.3887,

0.1897, 0.1002 and 0.3214, respectively.

Bayesian Inference phylogenetic trees were produced for both the Cytochrome b and

16S rRNA region (see figure 2 and 3). Phylogenetic trees produced for both the

Cytochrome b and 16s rRNA regions by the maximum likelihood method (figure 4 and

5) along with the cladograms produced for both rRNA regions by the neighbour joining

method (figure 6 and 7)

The species used for the outgroups worked successfully for each tree producing

method, as they were together and separate from the rodents and shrew species. Due

to the absence of Bunolagus monticularis 16S sequences on GenBank this could not be

used as an outgroup in the creation of tress for the 16S dataset.

Tree topology - Species support and within species clades

The Bayesian Inference (BI) phylogenetic tree produced using the cytochrome b rRNA

region (see figure 2) grouped all individuals of the species together with strong

supporting Bayesian probability scores of above 0.9, there was also strong nodal

support shown for within species clades, Lemniscomys rosalia (Lem), Saccostomys

campestris (Sac) and Mastomys natalensis (Mas) having a number of within species

clades with strong support from the Bayesian probability values. The BI phylogenetic

tree for the 16s rRNA region (see figure 3) also group the majority of individuals from

the same species together with high Bayesian probability values. However L.

capensisKC9684209 and L. saxatilisKC984207 were grouped together in a deeper

clade while L.capensisKC9684206 was not grouped in this clade. Irrespective of this,

these three outgroup species were still group together.

Both phylogenetic trees produced using the maximum likelihood method for cytochrome

b and 16s rRNA regions (see figure 4 and 5) showed low nodal support for within

species clades, however individuals from the same species were grouped together with

strong supporting bootstrap values. The neighbour joining cladogram for the

cytochrome b rRNA region (see figure 6) did not group all Aethomys ineptus (Aeth)

together due to the grouping of the individual from GenBank - A.Aeth_AY585876. This

individual was grouped in a clade containing all individuals belonging to Saccostomys

campestris and Steatomy pratensis (Stea). However, apart from this one individual, all

other individuals from the same species were grouped together, most with supporting

bootstrap values of above 0.7. The neighbour joining cladogram for the 16s rRNA

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C1213956region (see figure 7) also group individuals of the same species together, most of which

have supporting bootstrap values of above 0.7. Both the NJ cladograms showed high

support in bootstrap values of above 0.7 for a number of within species

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Figure 2: Bayesian inference phylogenetic tree for the Cytochrome b data set. Probability values of 0.7 or above are reported, illustrating strong support for species being grouped together, with the samples names adjacent to the branches and being represented as the species or genera name and the grid where they were trapped and sampled.

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Figure 3: Bayesian inference phylogenetic tree for the 16s data set. Probability values of 0.7 or above are reported, illustrating strong support for species being grouped together, with the samples names adjacent to the branches and being represented as the species or genera name and the grid where they were trapped and sampled.

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Figure 4: The maximum likelihood phylogenetic tree, produced using the cytochrome b rRNA region for the small mammal individuals. Only bootstrap values of 70 and above were chosen to be presented, illustrating the supported clades. The individuals are represented by an abbreviation of their species or genera names, along with the grid they were sampled in, and are adjacent to the branches.

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Figure 5. The maximum likelihood phylogenetic tree, produced using the 16s rRNA region for the small mammal individuals. Only bootstrap values of 70 and above were chosen to be presented, illustrating the supported clades. The individuals are represented by an abbreviation of their species or genera names, along with the grid they were sampled in, and are adjacent to the branches.

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Figure 6: The neighbour joining cladogram produced using the cytochrome b rRNA region for the individuals sampled. Only bootstrap values of 70 or above were reported, and represent the supported clades. Individuals are represented by an abbreviation of their species or genus and grid numbers next to the branches.

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Figure 7: The neighbour joining cladogram produced using the 16s rRNA region for the individuals sampled. Only bootstrap values of 70 or above were reported, and represent the supported clades. Individuals are represented by an abbreviation of their species or genus and grid numbers next to the branches.

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C1213956clades for Lemniscomys rosalia and Mastomys natalensis. There were limited within species clades with supporting bootstrap values for both Saccostomys campestris and Aethomys ineptus.

Tree topology – Family and genus groupings

The small mammal individuals sampled represent 4 families: Muridae, Nesomyidae,

Gliridae, and Soricidae. The Muridae family includes the sampled species: Aethomys

ineptus, Lemniscomys rosalia, Micaelamys namaquensis (Mic), Mastomys natalensis,

Otomys angoniensis (Oto) and Mus minutoides (Mus). The Nesomyidae family includes

the sampled species: Saccostomys campestris and Steatomy pratensis. Graphiurus

murinus is the only species included in the family Gliridae in the small mammals

sampled while the family Soricidae is represented by the individuals from the genus

Crocidurinea (Croc).

The Bayesian Inference phylogenetic tree produced for the cytochrome b rRNA region

showed strong support for each family, with the species from the family Muridae being

grouped together with a Bayesian probability value of 0.99 and the other three families

were supported by Bayesian probabilities of 1. All the species of Shrew samples (Croc)

came from the same genus and these were grouped together with a Bayesian

probability of 1. The Bayesian Inference tree produced using the 16s rRNA region

showed little support for family groupings and genus with species from the family

Nesomyidae and Gliridae being grouped within the family Muridae.

The neighbour joining cladograms for both 16s and cytochrome b rRNA regions group

families together, with strong support represented by bootstrap values for the grouping

of the Nesomyidae family on both trees. The grouping of the other families has mixed

support, with support being shown from the cladogram using the 16s rRNA region and

not the cladogram produced from the cytochrome b rRNA region and vice versa.

The phylogenetic trees produced using the maximum likelihood for the cytochrome b

region showed strong support for the grouping of families with bootstrap values ranging

from 72 to 99. The maximum likelihood tree for the 16s rRNA region showed similar

results to the neighbour joining tree for the same region with species from separate

families being grouped in deeper clades than the species in the same family.

The species Aethomys ineptus and Micaelamys namaquensis were previously part of

the same taxa (Aethomys). Although this is no longer true, these species should show

closer evolutionary relationships to each other than to other species in the Muridae

21

Table 3: PD scores reported following the national parks division into 4 blocks. Cytochrome b datasets have a higher PD score than their corresponding 16s dataset. South of the White iMfolozi has the highest PD score for both 16s and Cytb whereas between the Black and White iMfolozi has the lowest PD scores for both 16s and Cytb.

C1213956family; however, there is no support showing that they are sister taxa within the Muridae

family.

Phylogenetic Diversity in relation to landscape characters

DnaSP (Rozas 2009) calculated the number of haplotypes in the 4 different data sets;

north and south of the Black Umfolozi River for both 16s and Cytochrome b. For the

Cytochrome b datasets there were 30 haplotypes north of the river and 26 south of the

river, and for the 16s dataset there were 19 haplotypes north of the river and 16 south.

The phylogenetic diversity (PD) scores were then calculated and are reported in table 2.

After the grids and therefore samples were further divided to represent the dividing of

the park by the Hluhluwe River, the Black iMfolozi River, and the White iMfolozi river the

new datasets and number of haplotypes were as follows: North of the Hluhluwe had 18

haplotypes for cytochrome b and 12 for 16s datasets, in-between the Hluhluwe and

Black iMfolozi had 16 and 12 haplotypes for cytochrome b and 16s datasets

respectively, between the Black and White iMfolozi had 13 haplotypes for cytochrome b

and for 16s there were 9, and finally for south of the White iMfolozi there were 16

haplotypes for cytochrome b and 12 for 16s datasets. These haplotypes were then used

to calculate the PD scores (table 3)

Table 2: Phylogenetic diversity scores reported to 2 d.p for both Cytochrome b and 16s, north and south of the Black IMfolozi River. The Cytochrome b dataset reports a higher overall phylogenetic diversity score from the samples collected north of the river, whereas the 16s dataset reports a higher PD score for south of the river.

Dataset PD scores Cytb North 10.48Cytb South 9.8416S North 6.7816S South 6.89

Dataset location PD scores for 16s PD scores for CytbNorth of Hluhluwe 5.64 8.18Between Hluhluwe and Black iMfolozi

5.82 7.13

Between Black and White iMfolozi

5.5 6.0

South of White iMfolozi 6.27 8.2

22

C1213956

Measures of diversity

Due to no small mammals being trapped and sampled in grids 8, 20, and 27, there were

no alpha diversity results for these and therefore alpha diversity scores had values for

the remaining 24 grids, (table 4) with an average alpha diversity of 0.3486 and for the

four sections of the national park that were split up by the three main rivers (table 5)

with an average alpha diversity of 0.6275. Beta diversity calculations generated a table

of the beta diversity between each of the remaining girds (appendix 1) and therefore

276 diversity scores were generated with an average beta diversity of 0.6585. The

gamma diversity for Hluhluwe-iMfolozi national park is 0.2296 in respect to the gird

system used.

Table 4: The alpha diversity reported to 2 decimal places for each grid in Hluhluwe-iMfolozi national park that had small mammals successfully trapped and sampled. Grids 2, 3, 5 and 23 each have the highest alpha diversity reported (0.72) while 10 grids have an alpha diversity of 0 and therefore the lowest reported.

Grid Alpha diversity1 0.672 0.723 0.724 05 0.726 0.57 09 0.5610 011 0.3812 0.513 014 015 016 0.6317 018 0.6719 021 022 0.523 0.7224 0.4625 0.6326 0

23

Table 5: The alpha diversity for each section of the national park split by the three main rivers reported to 2 decimal places. The area north of the Hluhluwe River has the highest alpha diversity while the area in between the Black and White iMfolozi River had the lowest alpha diversity.

C1213956

Genetic diversity, phylogenetic diversity, and species richness relationships

The Spearman’s rank test for a correlation found there was no significant correlation

between the alpha diversity of grids and the percentage of shared alleles of Mastomys

natalensis in each grid (S= 2597.673, p-value > 0.5, rho= -0.129). The Pearson’s

correlation test reported a significant, strong, positive correlation between the alpha

diversity of each park location split by the rivers and the percentage of shared alleles of

Mastomys natalensis in each of these sections (t = 5.5369, d.f. = 2, p-value < 0.05, r=

0.9688947). This correlation is demonstrated in figure 3 with the relationship between

phylogenetic diversity and

species richness being

demonstrated in figure 4.

0.50 0.55 0.60 0.65 0.70 0.75

0.50

0.55

0.60

0.65

PD

PO

SA

Figure 8: The relationship between the percentages of shared alleles of Mastomys natalensis (POSA) in each of the four sections of the park divided by three main rivers and the alpha diversity of these four sections. This graph supports the Pearson’s correlation test that as the alpha diversity increases so does the percentages of shared alleles of Mastomys natalensis.

POSA vs. Alpha Diversity

ALPHA DIVERSITY

Park location Alpha diversity North of Hluhluwe 0.77Between Hluhluwe and Black iMfolozi 0.58Between Black and White iMfolozi 0.46South of White iMfolozi 0.70

24

C1213956

Discussion

This study investigates two highly discussed hypotheses in relation to species diversity,

while also covering several other topics. Through addressing the “riverine barrier

hypothesis” and the “species-genetic diversity correlation”, current and future

conservation projects can be tailored to effectively protect the small mammal diversity in

South Africa, while also providing either support or opposing evidence for conservation

strategies globally.

Sampling

There were a number of species that were trapped and sampled, the individuals

belonging to the genus Crocidura were not identified to species level in the field. Over

all there were 9 species of rodents sampled and one genus of shrews. This is slightly

higher than other studies on small mammal diversity in national parks and game parks

have recorded within South Africa. Wandrag et al. (2002) recorded a total of seven

species of rodents and insectivores in the Seekoei-vlei Provincial nature reserve, 5

rodent species were sampled at Kruger national park (MacFadyen et al. 2012) and

Lynch and Watson (1990) recorded seven rodent and two insectivore species in

Sehlabathebe National Park, Lesotho. From the tree groupings within this genus no

conclusion could be made as to what species they belong to. Therefore, to further the

Figure 9: This line graph represents the relationship between species richness and phylogenetic diversity of the four sections split by the three rivers for both their 16s (blue) and Cytochrome b (red) datasets. There is no distinct correlation shown with species richness of the same value in cytochrome data sets producing different PD scores.

Species richness vs PD

PD

Species Richness

25

C1213956understanding of the diversity of insectivores with reference to species belonging to the

genus Crocidura, further research will need to be carried out. There were 89 individuals

included in this study, with the individuals caught and sampled that were not from the

species Mastomys natalensis being caught as by-catch to another investigation, and

therefore to improve the sample size of this study, sampling could focus primarily on the

trapping and sampling of all small mammal species.

Phylogenetic reconstruction

Both the 16s and cytochrome b regions succeeded in identifying all 89 species

correctly. These species being grouped together consistently in phylogenetic trees

produced by the Bayesian Inference and maximum likelihood methods, and the

cladogram produced by the neighbour joining method. Phylogenetic reconstructions

using the cytochrome b rRNA region provided more definite family groupings, and

higher supporting values for these groupings in the form of Bayesian probability scores

of bootstrap values, than phylogenetic trees and cladograms produced using the 16s

rRNA region. In Praomyini tribe rodents it was found that cytochrome b genes are better

DNA markers than 16s genes, this was due to 16s genes being 2.5 times less variable

than cytochrome b and therefore having a smaller discriminatory power (Nicolas et al.

2012) therefore suggesting the cytochrome b gene should be targeted primarily when

studying small mammal diversity.

On one occasion in the cladogram created by the neighbour joining method using the

cytochrome b rRNA regions, an Aethomys ineptus individual that was taken from

GenBank was grouped separately from the other individuals in its species and grouped

with individuals from the family Nesomyidae. Although this was on one occasion, A.

ineptus is widespread in Southern Africa. A clinial variation in this species has been

found which is believed to be correlated to a longitudinal effect, likely to be Southern-

westerly to Northern-easterly (Chimimba 2001). The grouping of this individual may

have been caused due to this diversity and further research could be conducted looking

at this. However this was only the case in the neighbour joining tree and is not the main

focus point of this study.

Phylogenetic Diversity in relation to landscape characters

There was no difference found in the phylogenetic diversity on either side of the Black

iMfolozi river. The scores calculated for the individuals using the cytochrome b rRNA

region showed north of the river as having a slightly higher phylogenetic diversity (10.48

vs 9.84) whereas scores calculated using the 16s rRNA region showed a slightly higher

26

C1213956phylogenetic diversity score for south of the river (6.89 vs 6.78). Many individuals from

both sides of the river have formed within species clades, with the example of the

phylogenetic tree produced using cytochrome b genes (see figure x), which showed a

clade between the individual Sac2.1, which was sampled north of the river, and the

individual Sac21.2, which was sampled south of the river, with a Bayesian probability

score of 0.7961 supporting this relationship. This was also the case for the individuals

Sac15.1 which were sampled north, and Sac19.2 which was sampled south of the river,

Lem2.1 which was sampled north and Lem24.1 which was sampled south, and Lem5.1

which sampled north and Lem25.3 which was sampled south of this river, with all three

of these clades being supported by Bayesian probabilities of >0.9. Although DnaSP

(Rozas 2009) was used to identify the haplotypes present and thus reduce the sample

bias between the individuals used to calculate the phylogenetic scores north and south

of the Black iMfolozi River, there was still bias towards the north of the river having

more samples for cytochrome b (30 vs. 26) and 16s (19 vs. 16). Therefore to improve

this and erase sampling bias in future studies, bootstrap methods could be used.

Research looking at whether rivers act as potential or actual barriers to small mammals

and therefore the testing of the riverine barrier hypothesis in relation to small mammals

have found mixed results, which could be due to the diversity of rivers. The results

found from this study suggest that the Black iMfolozi River does not act as a potential

barrier to gene flow for small mammals, which is supported by the research done by

Gascon et al. (2000) who found geographic distance and habitat type were the most

significant factors in community similarity in small mammals where no difference was

found in species richness across the Juruá River. Contradictory to our results rivers in

Africa have been found to act as barriers to gene flow between small mammal and

rodent species, Jacquet et al. (2014) found some West African rivers could be

constitutional barriers to the West African pygmy shrew where sister clades were

separated by certain rivers. The Volta River, Ghana was found to be a barrier to gene

flow between two sister species of the rodent genus Pragomys, with other rivers also

acting as barriers between clades (Nicolas et al. 2011).

Although rivers in Africa have been found to be barriers to gene flow for small

mammals, from the results presented I am concluding that the Black iMfolozi River is

not a potential barrier to gene flow for small mammals in Hluhluwe-iMfolozi Game Park;

this may be due to both biotic and abiotic factors. A river may not be a potential barrier

to gene flow in small mammals due to their ability to swim. This would be affected by

abiotic factors such as the flow and width of the river. The ability of African rodents to

27

C1213956swim has been documented multiple times (Cook et al. 2001; Dagg and Windsor 1972),

although the duration and swimming ability of each species varies (Hickman and

Machine 1986). The width and seasonal change of the river will also affect whether it is

a potential barrier to gene flow. Droughts are not uncommon to many of the game parks

and national parks in South Africa, with it not being unusual for rivers to dry up in these

periods of drought (Walker et al. 1987), thus meaning in periods of drought the Black

iMfolozi River will no longer be present and therefore animals will be able to cross it.

Measures of diversity

The alpha diversity calculations were crucial in determining the relationship in this study

between the species diversity and proportion of shared alleles of Mastomys natalensis.

As well as this due to the Simpson’s diversity index (Simpson 1949) being used to

calculate the alpha diversity, the average probability of choosing two individuals

consecutively, of different species (Aho 2014), in a grid was calculated at 0.3486, thus

supporting the variety of species that were sampled throughout the park. The large

range of alpha diversity (0-0.72) show the large range of species richness and

abundance from gird to grid, with a large proportion of the grids having an alpha

diversity of 0 due to only one species being sampled there. This range could be due to

the habitat preference or the species, with work by Rodriquez and Ojeda (2011) finding

a higher alpha diversity of small mammal species in shrub land and Prosopis woodland

compared to the alpha diversity found in salt flats. Further research could investigate

the cause of the species distribution by researching the habitat type of each grid and the

habitat preference of the small mammal species. The average alpha diversity was

higher when calculated using the four geographic sections compared to the average

alpha diversity using the grid system, this could be due to the four geographic sections

being a larger area and therefore a larger spatial scale than the grids (Rodriquez and

Ojeda 2011; Whittaker et al. 2001).

Beta diversity was calculated using Whittaker (1960) and therefore the beta diversity

was calculated between all pairs of grids, and incorporates; the number of species

shared between sites, the number of species in site ‘a’ and the number of species in site

‘b’ (Koleff et al. 2003), therefore our result of an average beta diversity of 0.6585 shows

a high beta diversity and therefore turnover diversity when comparing grids throughout

the park.

28

C1213956Genetic diversity, phylogenetic diversity and species richness relationships

The proportion of shared alleles was used as a measure of genetic diversity in

Mastomys Natalensis, and this was then correlated to the alpha diversity of the park.

The use of the proportion of shared alleles is commonly used when investigating

genetic diversity, with this including a number of studies looking at the evolution of

humans (Rosenberg et al. 2001; Mountain and Cavalli-Sforza 1997). The species

diversity measurement used in this correlation was the alpha diversity or either each

gird or each geographical section formed by the splitting of the park by the Hluhluwe

and Black and White iMfolozi Rivers. The use of alpha diversity as a measure of

species diversity may have led to inaccurate results due to individuals from the genus

Crocidura not being categorised at a species level, and therefore highlighting the

importance in future studies to classify each individual into a species. With this being

said in future studies to improve the reliability of conclusions drawn from our results

about the relationship between species and genetic diversity it would be advised to also

use additional measurements of both species diversity and genetic diversity. Nazareno

and Jump (2012) found that when assessing the species-genetic diversity correlation

the data needs to be sufficient, in order to ensure the conclusions being drawn are

robust due to the ability of small samples sizes leading to bias.

This study found no significant correlation between the alpha diversity of each grid and

the proportion of shared alleles of Mastomys Natalensis in each gird, however a

significant, positive correlation was found between these variables in the 4 sections

between the three main rivers. Therefore the results from this study are inconclusive, as

to whether to accept or reject the species-genetic diversity correlation (SGDC).

There have been a number of studies showing support for the SGDC (Papadopoulou et

al. 2011; Struebig et al. 2011; Vellend 2003) and therefore supporting the correlation

found using the four geographical areas. All three of these studies above used discrete

sampling units, where gene flow between these samples would not take place.

Papadopoulou et al. (2011) and Vellend (2003) both studied the SGDC using an

Archipelago or chain of islands, and Struebig et al. (2011) used forest fragments.

Vellend et al (2014) concluded that the SGDC was more positively correlated and

significantly stronger in studies such as those mentioned that used discrete sampling

units. As concluded in this study the Black iMfolozi River is not a potential barrier to

gene flow between small mammals and with this being the primary river in the park, the

park as a whole is a continuous habitat to small mammals, and therefore the sampling

units used in this study were not discrete. 29

C1213956The studies that found no significant correlation between species diversity and genetic

diversity include (Taberlet et al. 2012; Odat et al. 2004; Silverton et al. 2009), and

therefore support the results of no significant correlation found using the grid system,

which used arbitrarily delineated areas. All three of these studies looked at plant

communities in grasslands, and therefore with sample units not being discrete. Future

research should focus on the investigation of the SGDC using animals in a ‘continuous’

habitat, with non-discrete sample units.

In conservation a positive correlation between species diversity and genetic diversity

(within species) would be a valuable relationship with the conservation of biodiversity at

one level, with this most commonly being species diversity acting as a surrogate for the

conservation of biodiversity at another level, this would be within species genetic

diversity (Kahilainen et al. 2014). The acceptance of this correlation and application of

conservation methods tailored to this could have long term negative effects on the

genetic diversity of many species, if species diversity is the primary focus and the

SGDC is not true for the specific species and habitat. Therefore more extensive

research needs to be done on a variety of habitats looking at a variety of orders and

families before this relationship can be accepted. This study showed inconclusive

results in relation to the SGDC, while highlighting the need for further research.

Conclusions

Both the primary and secondary hypothesis laid out in the introduction cannot be

accepted. This is due to inconclusive results being found when investigating the species

genetic diversity correlation and the rejection of the Black iMfolozi as being a potential

barrier to the gene flow between small mammals. This study highlights the importance

of further research using both discrete and non-discrete sampling in the bid to accept or

reject the SGDC.

Acknowledgements

I would like to thanks Prof. Mike Bruford for being my supervisor and allowing me to

complete this project. I would like to thank Prosper Karame for completing the DNA

extractions and the majority of the sequencing. Finally I would like to thank Dr Isa-Rita

Russo for all the help, knowledge and advice she has given to me throughout this

project, which I am very grateful for, as well as carrying out all the sampling.

30

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Appendices

Appendix 1: Beta diversity scores between each grid

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