GENETIC IMPACT OF MIGRATORY BEEKEEPING:
GENETIC VARIATION BETWEEN STATIONARY AND MIGRATORY
POPULATIONS OF HONEY BEE (APIS MELLIFERA L.) IN TURKEY
A THESIS SUBMITTED TO
THE GRADUATE SCHOOL OF NATURAL AND APPLIED SCIENCES
OF
MIDDLE EAST TECHNICAL UNIVERSITY
BY
EDA GAZEL KARAKAŞ
IN PARTIAL FULFILLMENT OF THE REQUIREMENTS
FOR
THE DEGREE OF MASTER OF SCIENCE
IN
BIOLOGY
AUGUST 2013
Approval of the Thesis
GENETIC IMPACT OF MIGRATORY BEEKEEPING: GENETIC VARIATION
BETWEEN STATIONARY AND MIGRATORY POPULATIONS OF HONEY BEE
(APIS MELLIFERA L.) IN TURKEY
submitted by EDA GAZEL KARAKAŞ in partial fulfillment of the requirements for the
degree of Master of Science in Biology Department, Middle East Technical University
by,
Prof. Dr. Canan Özgen
Dean, Graduate School of Natural and Applied Sciences
Prof. Dr. Gülay Özcengiz
Head of Department, Dept. of Biology, METU
Prof. Dr. Aykut Kence
Supervisor, Dept. of Biology, METU
Examining Committee Members:
Assoc. Prof. Dr. Ergi Deniz Özsoy
Biology Dept., Hacettepe University
Prof. Dr. Aykut Kence
Dept. of Biology, METU
________________
Assoc. Prof. Dr. C. Can Bilgin
Dept. of Biology, METU
Assist. Prof. Dr. Ayşegül Birand
Biology Dept., METU
Assist. Prof. Dr. Murat Telli
Dept. of Biology, Abant İzzet Baysal University
_______________
Date:
23/08/2013
I hereby declare that all information in this document has been obtained and
presented in accordance with academic rules and ethical conduct. I also declare that,
as required by these rules and conduct, I have fully cited and referenced all material
and results that are not original to this work.
Name, Last name: Eda Gazel KARAKAŞ
Signature:
iv
ABSTRACT
GENETIC IMPACT OF MIGRATORY BEEKEEPING:
GENETIC VARIATION BETWEEN STATIONARY AND MIGRATORY
POPULATIONS OF HONEY BEE (APIS MELLIFERA L.) IN TURKEY
Karakaş, Eda Gazel
M.Sc. Department of Biological Sciences
Supervisor: Prof. Dr. Aykut Kence
August 2013, 56 pages
In this study, we analyzed 237 worker honey bees from 24 colonies in 5 provinces in
Turkey. Samples from Kırklareli, Yığılca, Hatay and Artvin were collected from stationary
colonies. Samples from Muğla were collected from both stationary and migratory colonies.
4 ISSR primers were used in total sample. According to pairwise PhiPT values, the closest
stationary populations were Muğla stationary and Yığılca (0.362) and the most distant
populations were Kırklareli and Artvin population (0.612). Pairwise PhiPT value was 0.042
(p < 0.001) and the mean gene diversity value was 0.0351 between Muğla stationary and
Muğla migratory population. In phylogenetic trees, relationships among populations were
illustrated. The differentiation between the populations was also confirmed by PCO.
Results of the study showed that the population structure of honey bees in Turkey is
preserved.
Recently, genetic effect of migratory beekeeping has been attracting more attention of
scientists in terms of conservation of biodiversity. This genetic effect was studied for the
first time by using ISSR primers. Differentiation between Muğla stationary and migratory
population was significant but very low, and gene flow between them was very high.
Future research may also need to focus on how this structuring was preserved. Results of
the study confirm the findings of the previous studies that show the high level of genetic
diversity in honey bees of Turkey and also the need of conservation plans, especially in
transporting of colonies from their local region to different regions and using commercial
queens or colonies which are not native races.
Keywords: ISSR, genetic diversity, dominant marker, AMOVA, PCO
v
ÖZ
GÖÇER ARICILIĞIN GENETİK ETKİSİ:
TÜRKİYE SABİT VE GEZGİN BAL ARISI (APIS MELLIFERA L.)
POPULASYONLARI ARASINDAKİ GENETİK ÇEŞİTLİLİK
Karakaş, Eda Gazel
Y. Lisans, Biyoloji Bölümü
Tez Danışmanı: Prof. Dr. Aykut Kence
Ağustos 2013, 56 sayfa
Bu çalışmada Türkiye’nin 5 bölgesine (Kırklareli, Yığılca, Muğla, Hatay ve Artvin) ait 24
koloniden 237 işçi arı örneği incelenmiştir. Kırklareli, Yığılca, Hatay ve Artvin
bölgelerinin arıları sabit kolonilerden toplanmıştır. Muğla bölgesinden hem sabit hem
göçer kolonilerden örnekler toplanmıştır. Tüm populasyonda 4 ISSR primeri kullanılmıştır.
PhiPT değerlerine göre en uzak sabit populasyonlar Kırklareli ve Artvin (0.612), en yakın
sabit populasyonlar ise Muğla ve Yığılca (0.362) olarak bulunmuştur. Muğla göçer ve
Muğla sabite ait ikili PhiPT değeri 0.042, genetik farklılaşma değeri 0.0351 olarak
bulunmuştur. Filogenetik ağaçlarda populasyonlar arası ilişkiler gösterilmiştir.
Populasyonlar arası farklılaşma PCO analizi ile ayrıca doğrulanmıştır. Bu çalışmanın
sonuçları Türkiye’deki bal arılarının populasyon yapısının korunduğunu göstermiştir.
Son zamanlarda, biyolojik çeşitliliğin korunması açısından göçer arıcılığın genetik etkisi
bilim insanlarının daha fazla dikkatini çekmektedir. Bu genetik etki ISSR primeri
kullanılarak ilk kez bu çalışma ile gösterilmiştir. Çalışmanın sonuçları önceki
araştırmalarda elde edilen Türkiye bal arılarının yüksek seviyedeki genetik çeşitliliği ile
ilgili bulguları doğrulamaktadır. Sonuçlar, koruma planlarına özellikle kolonilerin yerel
bölgelerinden farklı bölgelere taşınması ve yabancı ana arılar ya da kolonilerin kullanılması
gibi konularda duyulan ihtiyacı göstermektedir.
Anahtar Kelimeler: ISSR, genetik çeşitlilik, dominant markır, AMOVA, PCO
vi
To my Parents, Sisters and Burak Han
vii
ACKNOWLEDGMENTS
I would like to express my deepest gratitude to my advisor, Prof. Dr. Aykut Kence for his
excellent and invaluable guidance, educating, caring, patience, and providing me. And I
would like to thank Assoc. Prof. Dr. Meral Kence for her precious contributions, advice
and critically fast equipment and laboratory material support.
I am greatly and sincerely thankful to Prof. Dr. Yaman Örs for his continued support and
invaluable encouragement throughout this process and before.
I am thankful to the members of thesis examining committee, Assoc. Prof. Dr. C. Can
Bilgin, Assoc. Prof. Dr. Ergi Deniz Özsoy, Assist. Prof. Dr. Ayşegül Birand and Assist.
Prof. Dr Murat Telli for critical reading and evaluating this thesis; and their valuable
suggestions and comments to make the final version of this thesis better.
I would like to thank my laboratory mates; to Ayhan Altun for helping me with patience in
lots of steps of my thesis; Okan Can Arslan for teaching and helping me in the lab works;
to Mert Kükrer for his help with statistical softwares and answering my endless questions;
to Mehmet Kayım for his help with statistical softwares and comments; to Mert Elverici for
his friendship and support; to Mehmet Ali Döke for always being helpful, to Cansu Özge
Tozkar for her companionship, and our technician Mustafa Nail Cırık for his priceless help
in the field work and friendship.
I also thank all of my friends, especially my roommates, for being fully supportive,
thoughtful and helpful all the time. My special thanks go to Esma Eren for being more than
a sister in all those years and to Bilgi Güngör for always being helpful and sharing my
worries and also being a peerless guide for me in the writing of this thesis.
I would like to gratefully thank my dear family; my father Doğan Karakaş, my mother
Mürüvvet Karakaş, my sisters Kardelen, Seda and Pınar Karakaş and Burak Han Pehlivan
for believing, supporting, encouraging me endlessly and being always next to me when I
need. Each of them is the reason of the steps I take.
This research was financially supported by Scientific and Technical Research Council of
Turkey.
viii
TABLE OF CONTENTS
ABSTRACT........................................................................................................................ V
ÖZ ...................................................................................................................................... VI
ACKNOWLEDGMENTS............................................................................................... VIII
TABLE OF CONTENTS ................................................................................................... IX
LIST OF TABLES ............................................................................................................. XI
LIST OF FIGURES .......................................................................................................... XII
CHAPTERS ......................................................................................................................... 1
1. INTRODUCTION ........................................................................................................... 1
1.1. General Information about Honey Bees ........................................................................ 1
1.2. Phylogenetic Relationships of Honey Bees ................................................................... 1
1.3. Honey Bee Subspecies of Middle East .......................................................................... 2
1.4. Honey Bee Subspecies of Turkey.................................................................................. 3
1.5. Genetic Studies on Honey Bees of Turkey .................................................................... 4
1.6. DNA Markers and Benefits of Using ISSRs ................................................................. 5
1.7. ISSR Studies on Honey Bees ........................................................................................ 6
1.8. Discovering a Problem - Colony Collapse Disorder (CCD) .......................................... 7
1.9. Migratory Beekeeping ................................................................................................... 8
1.10. Beekeeping in Turkey ................................................................................................. 9
1.11. Aim of the Study ....................................................................................................... 10
2. MATERIALS AND METHODS ................................................................................... 13
2.1. Biological Material...................................................................................................... 13
2.2. DNA Isolation ............................................................................................................. 14
2.3. ISSR Markers Amplification by PCR ......................................................................... 14
2.3.1 Agarose Gel Electrophoresis ................................................................................. 15
2.4. Statistical Analyses ..................................................................................................... 15
ix
2.4.1. Data Analyses and Scoring ...................................................................................15
2.4.2. Genetic Variation ..................................................................................................16
2.4.3. Genetic Distance ...................................................................................................16
2.4.4. AMOVA ...............................................................................................................16
2.4.5. Genetic Diversity ..................................................................................................16
2.4.6. Gene Flow.............................................................................................................17
2.4.7. Mantel Test ...........................................................................................................17
2.4.8. Phylogenetic Tree .................................................................................................17
2.4.9. Principal Coordinates Analysis (PCO or PcoA) ....................................................18
2.4.10. Population Structure............................................................................................18
3. RESULTS .......................................................................................................................21
3.1. Genetic Diversity .........................................................................................................21
3.2. Genetic Differentiation ................................................................................................23
3.2.1. Gene Flow.............................................................................................................23
3.2.2. Analysis of Molecular Variance (AMOVA) .........................................................25
3.3. Phylogenetic Trees .......................................................................................................26
3.4. Ordination of Populations ............................................................................................29
3.5. Correlation between Genetic Distance and Geographic Distance ................................33
3.6. Population Structure ....................................................................................................37
4. DISCUSSION .................................................................................................................41
5. CONCLUSION ..............................................................................................................45
REFERENCES ...................................................................................................................47
APPENDIX ........................................................................................................................56
A.
SOLUTIONS ..............................................................................................................56
x
LIST OF TABLES
Table 2.1 Information of honey bee samples...................................................................... 13
Table 2.2 Information of ISSR primers used in the study .................................................. 15
Table 3.1 Number of observed bands and percentage of polymorphic loci of
all populations .................................................................................................................... 22
Table 3.2 Genetic diversity parameters between Muğla stationary and the other stationary
populations ......................................................................................................................... 22
Table 3.3 Total gene diversity (Ht, below diagonal) and the diversity within population
(Hs, above diagonal) values among all populations ........................................................... 23
Table 3.4 Coefficient of gene differentiation (Gst, below diagonal) and gene flow (Nm,
above diagonal) values among all populations ................................................................... 24
Table 3.5 AMOVA results ................................................................................................. 25
Table 3.6 Pairwise PhiPT values of all populations ........................................................... 26
Table 3.7 Nei's genetic identity (above diagonal) and genetic distance (below diagonal)
values among all populations ............................................................................................. 26
Table 3.8 Results of Principal Coordinate Analysis (PCO) of Muğla populations ............. 30
Table 3.9 Results of Principal Coordinate Analysis (PCO) of all populations ................... 31
Table 3.10 Distance matrix based on centroids calculated from principal coordinates ...... 33
xi
LIST OF FIGURES
Figure 1.1 Honey Bee Subspecies of Turkey ...................................................................... 3
Figure 3.1 DNA fingerprinting of 29 honey bee genotypes from based on ISSR marker ...21
Figure 3.2 UPGMA tree of all populations .........................................................................28
Figure 3.3 Unrooted UPGMA tree of all populations .........................................................28
Figure 3.4 Neighbour joining tree of all populations ..........................................................29
Figure 3. 5 3D illustration from principal coordinates analysis of Muğla migratory and
Muğla stationary populations..............................................................................................30
Figure 3.6 2D illustration from principal coordinates analysis of Muğla migratory and
Muğla stationary populations..............................................................................................31
Figure 3.7 3D illustration of principal coordinates analysis of all populations ...................32
Figure 3.8 2D illustration without showing vectors from principal coordinates analysis
of all populations ................................................................................................................32
Figure 3.9 Scatter plot for comparison of genetic and geographic distance between
Muğla stationary and the other stationary populations ........................................................34
Figure 3.10 Histogram chart from results of Mantel test for Muğla stationary and the
other stationary populations ................................................................................................35
Figure 3.11 Scatter plot for comparison of genetic and geographic distance between
Muğla migratory and the other stationary populations ........................................................36
Figure 3.12 Histogram chart from results of Mantel test for Muğla migratory and
the other stationary populations ..........................................................................................36
Figure 3.13 Ln (probabilities) for K values .........................................................................37
Figure 3.14 Rates of change in the likelihood of Ks ...........................................................38
Figure 3.15 DeltaK values ..................................................................................................38
Figure 3.16 Cluster assignments of stationary colonies ......................................................39
Figure 3.17 Cluster assignments of stationary colonies and Muğla migratory ....................39
xii
CHAPTER 1
INTRODUCTION
1.1. General Information about Honey Bees
Honey bees are the most commonly domesticated species owing to their economic and
ecological role. The honey bee, Apis mellifera, is a social insect. It lives in a colony or a
hive, which can compromise 50,000 to 80,000 individuals in a small volume. Ferine honey
bees make their colonies in hollow trees or any other suitable cavities whose roof supports
the construction vertical and parallel wax combs (Kilani, 1999). Only the genus Apis is
formed by true honey bees and is represented by a small part of nearly 20.000 known
species of bees (Winfree, 2010).
Animals pollinate 85% of plant species worldwide (Ollerton, 2011), and in most types of
ecosystems bees are the main pollinators (Neff, 1993). Honey bees are invaluable for
cross–pollination and therefore, the conservation of them is very important (FAO, 2009).
There are lots of plants dependent on particular kinds of bees for their pollination. Bees
have interesting features apart from pollination. For example, bee venom has some
medicinal properties and is used for the treatment of arthritis, multiple sclerosis,
desensitization treatments, and recently cancer, epilepsy and depression (FAO, 2009; Abdu
and Ali, 2012). Because of its unique behavioral traits and social instincts, the honey bee is
an important organism to study. Besides its agricultural importance, it is a model organism
for immunity and diseases of the X chromosome studies. The honey bee is one of the high
priority organisms and its genome was sequenced by Honey Bee Genome Sequencing
Consortium, supported by NHGRI (Weinstock et al., 2006).
1.2. Phylogenetic Relationships of Honey Bees
Bees are insects of the order Hymenoptera. Honey bees are all members of one genus
called Apis belong to the family Apidae, which forms the tribe Apini, the Old World honey
bees. There are 10 species of honey bee which belong to the genus Apis (Han et al., 2012).
By phylogenetic analyses, it was showed that the genus Apis can be divided into three
distinct sub – genera; 1. Subgenus Megapis (or giant bees): Apis dorsata, Apis laboriosa,
Apis binghami, Apis nigrocincta; 2. Subgenus Micrapis (or dwarf bees): Apis florea, Apis
andreniformis (Arias and Sheppard, 2005), and 3. Apis (or cavitynesting bees): Apis
mellifera, Apis cerana, Apis koschevnikovi, Apis nulensis (Han et al., 2012)
1
The last sub-genera, Apis, was regrouped according to their geographical distribution. The
first group is Eastern group consisted of A. cerena, A. koschevnikovi and A. nulensis. From
these species, A. koschevnikovi which inhabits Malaysian and Indonesian Borneo, is well
distinct. A. cerana, is the sister species of A. koschevnikovi found in southern and
southeastern Asia, such as China, India, and Papua New Guinea. A. nigrocincta distributes
the Philippine island of Mindanao as well as the Indonesian islands. Mitochondrial DNA
(mtDNA) and AFLP analysis showed that A. nigrocincta is diverged from A. cerana
(Bodur, 2007).
The second group, Western group, includes A. mellifera L. and is called the European,
Western or Common honey bee in various parts of the world. Its natural distribution
includes Central Asia, Europe, Near East and sub-Saharan Africa and also introduced to
East and Southeast Asia, Australia and the Americas (Ruttner, 1988). It has been adapted to
very different types of climate conditions such as tropical or cold temperature, humid areas
or semi-deserts (Han et al., 2012). Many studies have been performed on A. mellifera about
their morphology, genetics, behavior and biogeography (Arias and Sheppard, 1996; Palmer
et al., 2000; Kandemir et al., 2005; Bodur et al., 2007; Kence et al., 2009).
Up to date, there are at least 29 subspecies of A. mellifera that have been delineated
depending on the morphometric analysis (Han et al., 2012). They are divided into four
major groups which are supported by morphometric, genetic, ecological, physiological, and
behavioral characteristics. Group A contains subspecies of Africa; group M contains
subspecies of western and northern Europe; group C contains subspecies of eastern Europe;
and the last group, group O contains subspecies from Turkey and the Middle East (Ruttner
et al., 1978; Ruttner, 1988; Arias and Sheppard, 1996; Franck et al., 2000). In general,
molecular data analysis (Arias and Sheppard, 1996) are compatible with morphometric data
analysis (Ruttner, 1988), about these phylogenetic relationships; but some studies did not
distinguish between groups C and O and both of them were named as C (Cornuet and
Garnery 1991; Garnery et al., 1992). However, the confirmation of group O has recently
been done by using mitochondrial DNA (mtDNA) and microsatellites studies (Franck et
al., 2000; Kandemir et al., 2006).
1.3. Honey Bee Subspecies of Middle East
The distribution of the Middle Eastern honey bee subspecies ranges from central Iran and
Caspian coast to Black Sea area, Caucasian Alpine region and Anatolia. Previous
morphometric analyses showed that there are seven subspecies; Apis mellifera adami, A.
mellifera anatoliaca, A. mellifera armeniaca, A. mellifera caucasica, A. mellifera meda, A.
mellifera cypria, and A. mellifera syriaca (Ruttner, 1988). Zoogeographic differences
showed that A. mellifera syriaca and A. mellifera cypria have yellow body, and are smaller
than northern subspecies, however A. mellifera adami is large as A. mellifera caucasica
which are on the same latitude (Ruttner, 1988). Characteristically, in the southern part
subspecies have short haired, yellow body and are small, whereas in the northern part they
have long haired, tall and dark body. According to morphometric analyses, it was found
2
that there are similarities between A. mellifera macedonica from south Europe and A.
mellifera anatoliaca. Also, the similarities were observed between A. mellifera sicula from
central Mediterranean – North Africa and A. mellifera anatoliaca (Ruttner, 1988). Because
of these similarities, it seems that Anatolia is the genetic center of A. mellifera (Ruttner,
1988; Bodur et al., 2007).
1.4. Honey Bee Subspecies of Turkey
According to an old Hittite tablet from Boğazköy, it is understood that beekeeping
activities in Anatolia have been originated before 1300 B.C. (Ruttner, 1988; Akkaya and
Alkan, 2007). Distribution of five subspecies of A. mellifera has been found in Turkey.
Those subspecies are A. mellifera carnica, A. mellifera anatoliaca, A. mellifera caucasica,
A. mellifera syriaca, and A. mellifera meda (Kandemir et al., 2000). Distribution of the
honey bee subspecies in Turkey was illustrated in Figure 1.1.
Figure 1.1 Honey Bee Subspecies of Turkey (Işık and Oskay, 2011)
A mellifera carnica is distributed from Turkey to Austria, Slovakia and Serbia. Carnica
honey bees found in Thrace region of Turkey (Kandemir et al., 2000). Their bodies are
short haired, brown-grey colored with lighter brown stripes. Chitin of these bees is dark,
some of them have lighter or brown colored rings and dots.
According to Bouga (2011) et al. A. mellifera anatoliaca habitats across Anatolia from
north to south and east to west with locally adapted ecotypes like Muğla, Giresun and
Yığılca. Their bodies are generally yellow colored but soiled orange or brown rings can be
seen on their abdomen. They have broader abdomens and tarsi, short legs and wings. They
are known with their poor nectar collecting, having high power of reproductive abilities,
and high adaptability to various extreme climatic conditions (Ruttner, 1988).
3
A. mellifera caucasica can be observed in northeastern Anatolia, near the Georgian border,
especially in Ardahan and Artvin (Kandemir et al., 2000). Their bodies are small, short
haired and completely dark to plain yellow colored. They have the longest proboscis
among all A. mellifera species (Ruttner, 1988).
A. mellifera syriaca is distributed in southeastern Anatolia, Israel, Lebonan, Jordan and
Syria. They are found in Hatay in Turkey (Kandemir et al., 2000). They are the smallest
honey bee subspecies in Middle East. Their nectar collection is very good but it is hard to
manage colonies of these bees owing to their aggressiveness. Their abdomens are slender
and short haired. Their scutellum and terga are brightly yellow. Their basitarsal and cubital
indices are shorter than oriental Apis mellifera subspecies (Ruttner, 1988).
A. mellifera meda is distributed from eastern Anatolia (Kandemir et al., 2000) to Iran and
Iraq. Color of their scutellum ranges from plain yellow to dark yellow. They have got a
broader abdomen and metatarsi, and narrower forewings.
1.5. Genetic Studies on Honey Bees of Turkey
Based on morphometric data, Ruttner (1988) claimed that Anatolian honey bees, Apis
mellifera anatoliaca can be evaluated as genetic center of eastern for A. mellifera.
Kandemir et al. (2000, 2005), Darendelioğlu and Kence (1992), and Arias et al. (2006)
used morphometric analyses on honey bee races. Despite of migratory beekeeping, it is
found that morphometric and genetic variation in honey bees of Turkey is very high.
Closely clustering between Black Sea and eastern Anatolia honey bees were observed as
well as clustering of central Anatolian, Aegean and Mediterranean honey bees. Thrace and
southern Anatolia honey bees clustered in different units (Kandemir et al., 2000).
In spite of using morphometric analyses in honey bees for discrimination purposes, they
have got some drawbacks that limit their performing. For example, they are not the certain
results of DNA changes and sensitive to environmental changes. Their polygenic
determinism is a strong drawback for the purposes of population genetic studies (Bodur,
2001). In population genetic studies, allozyme analyses are used widely in several plant
and animal species (Kephart, 1990; Hillis et al., 1996). But, allozyme studies in honey bee
races are limited because of low level of allozyme polymorphism (Sheppard, 1986). Pamilo
et al. (1978) claims that haplodiploidy may cause the problem. Studies from worldwide
showed that few enzyme loci were examined as polymorphic (Asal et al., 1995;
Nunamaker et al., 1984). Allozyme studies showed that the highest overall mean
heterozygosity among Apis mellifera populations (0.072 ± 0.007) belonged to honeybees of
Turkey (Kandemir and Kence, 1995). According to Sheppard (1986), overall mean
heterozygosity value among European honey bees is 0.038 for 23 colonies which lower
than Turkey’s honey bees.
Microsatellites are very useful genetic markers used in various study areas such as genetics,
kinship or population studies. Estoup et al. (1993) have found 52 CTn and 23 GTn
microsatellites in honey bee genome. Frequencies of the microsatellites have calculated and
4
were one in every 15 kilobase and 34 kilobase in the genome (Estoup et al., 1993). Estoup
et al. (1995) also supported the data from morphometric analysis and mtDNA and claim
that there are three evolutionary lineages of honey bees which are A, M and C.
Population structure analysis of honey bees with microsatellite markers were also studied
in Turkey (Kandemir, 1999; Bodur et al., 2007; Kence et al., 2009; Tunca, 2009; Yıldız et
al., 2010). As a result of these studies, it is revealed that there is a high genetic structure
and variation in honey bees of Turkey. Also, they are all agreed to divergent population
clusters which show different subspecies. All of the studies emphasized the genetic
variation in Anatolia and Thrace should be protected against to migratory beekeeping with
the help of creating special, isolated areas away from migratory colonies that are possible
threats for the valuable genetic diversity.
1.6. DNA Markers and Benefits of Using ISSRs
DNA markers can be separated in two groups as dominant markers and co-dominant
markers. With dominant markers (e.g. RAPDs or ISSRs), many loci in one sample of DNA
can be analyzed with one time PCR reaction. Dominant markers are high efficient markers
and no previous information is required about their sequence, but cannot distinguish
heterozygous from homozygous. On the other hand, co-dominant markers (e.g.
microsatellites, RFLP) can analyze one locus at one time and a primer that amplifies a codominant marker can produce one targeted product and a heterozygote from each of the
homozygotes can be distinguished. Co-dominant markers require previous information
about the sequence. As a consequence, they are more informative since the allelic
variations of that locus can be distinguished (Guillot and Carpentier-Skandalis, 2011). The
most commonly used DNA markers are restriction fragment length polymorphism
(RFLPs), Random Amplified Polymorphism DNA (RAPD), Microsatellites, Simple
Sequence Repeats (SSR), Inter-Simple Sequence Repeat (ISSR), and Amplified fragment
length polymorphism (AFLP).
RFLP, RAPD, AFLP and microsatellites are among DNA markers used in honeybee
population genetic studies (Suazo et al. 1998; Suazo and Hall 2002; Gustavo, 2006).
Although they have many polymorphic loci, nuclear RFLPs are not very suitable for large
scale population studies because of impractical transfer hybridization detection and probes
not widely available (Sheppard and Smith, 2000). RAPDs are very economical, dominantly
inherited markers but they are difficult to replicate in different laboratories. AFLP method
is very useful at intraspecific level since it reveals high polymorphism and it is repeatable
among laboratories, but not very economical (Vos et al., 1995; Sheppard and Smith, 2000).
Microsatellites have been used efficiently in studies of distribution of honey bees.
Recently, genetic variation between stationary and migratory honey bees of Turkey was
studied by using microsatellites (Kükrer, 2013).
ISSRs combine most of the benefits of AFLPs and microsatellites with the universality of
RAPDs (Nagaoka and Ogihara, 1997). They are highly reproducible possibly owing to
5
their longer primers (16-25mers) comparative to RAPD primers (10mers). This trait allows
following use of higher annealing temperature (45-60◦C) which leads to higher stringency.
92-95% of the scored bands can be repeated in the DNA samples of the same studied
organisms and in different PCR runs (Moreno et al., 1998). Lower template DNA (10ng)
yields the same amplification products as does 25 or 50 ng per 20μl PCR reaction (Gupta et
al., 1994; Ratnaparkhe et al., 1998). ISSR markers are more stable and repeatable than
RAPD markers, because segregation at ISSR loci performs simple Mendelian patterns of
inheritance, they are likely to generate more bands than RAPD, and they are more
informative. Owing to these advantages, ISSR markers are preferred instead of RAPD
markers, lately. However, there is the same drawback of RAPD markers in ISSR markers.
ISSR markers are dominant as well as RAPD, so they cannot distinguish heterozygous
from homozygous (Semagn et al., 2006). ISSR primers can be unanchored, 5’ anchored or
3’ anchored. It has been found that 3’ anchored primers yield more clear bands than the
others (Reddy, 2002).
1.7. ISSR Studies on Honey Bees
Inter-simple sequence repeat (ISSR) markers have been originally developed to
differentiate closely related plant cultivars. However, beside plants, they also became
highly powerful for researches in natural populations such as fungi, insects, and vertebrates
(Wolfe, 2005). Population differentiation in honey bees has been also studied successfully
with ISSRs.
Ceksteryte et al. (2012) studied differentiation of ISSR markers in order to assess genetic
variation of Lithuania honey bees and introduced subspecies. They compared two Lithuniabred lines of Apis mellifera carnica with Czech Republic, Slovenia, Caucasus and Buckfast
hybrids. They used four ISSR primers and obtained an UPGMA dendrogram illustrated
four sub-clusters and in the resulted rooted phylogenetic tree, A. m. caucasica and Buckfast
hybrids seperated from the A. m. carnica lines.
Al-Otaibi (2008) studied genetic variability of eleven colonies from three honey bee races,
according to their tolerance to parasitic mite Varroa. First race was Apis mellifera
yementice (Indigenous) which is tolerant to mites. Second race was Apis mellifera carnica
(Carniolan) which is sensitive to mites and third race was hybrids between both of first and
second races, which are moderately tolerant. In this study, ten ISSR primers were used and
resulted UPGMA dendrogram separated into three groups according to their origins. The
third branch which represents the hybrids was between the indigenous branch and the
Carniolan branch.
Paplauskienė et al. (2006) also used eleven ISSR primes and revealed race and line-specific
DNA profiles of Apis mellifera caucasica and Apis mellifera carnica and three lines of A.
m. carnica.
Sylvester (2003) studied genetic variation between Varroa mite resistant Russian honey
bees and non-resistant Italian honey bees. In this study, a new method which involves ISSR
6
and RFLP techniques together was described. Sylvester digested ISSR primers with
restriction enzymes in order to increase the amount of detectable DNA variation and
produce ISSR-RFLPs. This method is particularly important when there is limited number
of ISSR primers. It makes the detection more simplifier and decreases the costs.
1.8. Discovering a Problem - Colony Collapse Disorder (CCD)
In the winter of 2006-2007, unusually high losses of bees were reported by beekeepers
around the world (vanEngelsdorp, 2006). These losses were between 30% and 90% of the
colonies and about 50% of them were with unknown symptoms. Same year, in contrast to
colony losses with less than 20% of previous consecutive years, an average colony loss
percentage of 25,9 was observed in Turkey –with percentages as high as 64,9 in East
Anatolia, 56,8 in Northeast, 39,4 in East Mediterranean, 34,2 in Central Anatolia- (Giray et
al., 2010).
According to reports, there was a sudden loss of worker bees of the colony population. Few
dead worker bees were found around the colony, the queen and the brood was still in the
hives with highly abundant honey and pollen reserves. But, this eventually causes
extinction of the colony. This phenomenon was named as Colony Collapse Disorder
(CCD). When the old records belonged to a century ago were investigated, it was found
that this ‘unusual disappearances’ were observed in some years (vanEngelsdorp, 2009).
Several possible reasons that cause to Colony Collapse Disorder have been discovered.
Some researchers claimed that Varroa mites and insect diseases (e.g. American foulbrood,
Nosema) might cause it (vanEngelsdorp, 2006); and some said environmental change and
stress related to the change (Leita, 1996; Johnson, 2009; Conte, 2008; Memmott, 2007);
limiting colonies to some specific crops resulting in malnutrition (vanEngelsdorp, 2007);
pesticides containing chemicals such as thiamethoxam and imidacloprid (Tapparo, 2012;
Bortolotti, 2003) or migratory beekeeping might cause it (Kence, 2006; Giray et al., 2010).
Other researchers proposed cell phone radiation (Sahib, 2011) or genetically modified
(GM) crops (Oldroyd, 2007) might affect honey bees.
CCD is an important phenomenon relative to its role on agriculture and economy. Honey
bees are invaluable organisms; they play the major role on pollination of agricultural and
wild crops worldwide. The mechanism causing to CCD is still not identified thoroughly.
Many researchers claim that there is not any single cause found but a combination of
factors may be responsible (vanEngelsdorp, 2010). Recently, effects of migratory
beekeeping on the health of honey bee colonies have attracted more attention of scientists.
7
1.9. Migratory Beekeeping
In migratory beekeeping, colonies of honey bees are moved from their locations to
thousands of miles away by stacking on a tractor. This repeated process in every few
months may be stressful for honey bees which are used to their location since orientation to
their hives is important to survive. Also, changing location of honey bees and moving them
from their own region to different places may spread diseases and pathogens into those new
areas (Kence, 2006; Stokstad, 2007; Giray et al., 2010).
Purposes of migratory beekeeping are more honey production and pollination services. It
was showed that migratory beekeepers earn higher honey yield (41.60 kg/colony) than
stationary beekeepers (15.66 kg/colony). Thus, honey yield is three times higher in case of
migratory beekeeping (Sharma and Bhatia, 2001). In pollination services, farmers rent
hives and use them to pollinate their plants and owing this, beekeepers earn extra money.
In commercial beekeeping, diseases may spread very quickly between colonies.
Introducing them to new locations may increase the chance of having them exposed to new
kind of parasites, viruses or bacteria which they had never met (Shen et al., 2005;
Dietemann, 2006; Welch et al., 2009).
Another drawback is the diet that honey bees are fed during the transportation. They are fed
with fructose sugar syrup which is unnatural for bees (Kence, 2006). Previous studies
showed that feeding honey bees with high fructose corn syrup (HFCS) as a monoculture
diet for wintering process may be a factor for CCD syndromes (Mao et al., 2013). Honey
bees which were fed with pollen from various plants had healthier immune system than
honey bees fed with pollen from a single plant species (Stokstad, 2007). Additionally, an
ingredient of honey, p-coumaric acid, helps bees in detoxifying some pesticides. So, honey
bees which are fed with HFCS are in need of p-coumaric acid. Also, HFCS produced from
GM corns can be a possible threat for CCD (Thomas, 2007).
For a colony of honey bees, beginning of a new year can be considered as the time from
September to December. Exposing conditions to colony at these particular times are
especially important to its survival during the next year. Number of individuals increase
just before foraging, then the colony swarms one or more times. Those honey bees spend
the summer for preparing to winter. In the fall, the number of individuals in the colony
decreases and bees for winter are born. Winter bees are responsible from the colony until
spring comes. But, migratory beekeeping breaks this cycle. Transported colonies pollinate
both summer plants and winter plants and get their signals mixed. Changing in the latitude
causes several side effects such as changing in the hours of daylight, temperature, flora,
humidity, and also the pesticides they exposed. And this unnaturally rapid changes and
different signals may cause stress of the honey bees (Kence, 2006; Giray et al. 2007;
Thomas, 2007).
Besides of those drawbacks mentioned above, genetic effect of migratory beekeeping has
also become an important concern to scientists. Recent studies showed that population
structure of honey bees in Europe was lost or strongly disturbed. It is mostly attributed to
8
using commercial queens or colonies, introducing of non-native races and migratory
beekeeping (reviewed in De la R´ua, 2009). Losses of genetic diversity because of genetic
drift or inbreeding are generally encountered in small isolated populations. Those losses
may reduce evolutionary potential and organisms cannot handle environmental changes,
and eventually may lead to extinction (Ellstrand and Elam, 1993; Frankham, 2005). It is a
considerably important issue since many researches indicated that when the genetic
diversity of honey bees is high, they are more productive, have got increased colony
growth, can handle with severe infections more easily and are more adaptable to their niche
(Mattilla and Seeley, 2007; Tarpy, 2003).
1.10. Beekeeping in Turkey
Turkey has an important role on the worldwide beekeeping and has a high number of
domestic colonies (Giray et al., 2010, FAOSTAT, 2011). In Turkey, number of domestic
colonies is more than 6 million, and Turkey is the second country that has the highest
number of domestic colonies after China. The average honey production of a colony is 17
kilograms. The quality and quantity of queen bee production are not sufficient, which is
about 200,000 per year. Migratory beekeeping is very common in Turkey, 86% of the
colonies are moved between regions where forage may abundant (Giray et al., 2010). This
causes natural breeds of several kinds, owing to mating of portable queens and local
drones.
Migratory beekeeping has been introduced to Turkey in 1950s, before that time, regional
honey bee races; Apis mellifera anatoalica, A. m. caucasica, A. m, syriaca, A. m. meda and
A. m. carnica were maintained in their local area and also there were various local races
depending on their flora. For example, Muğla population in Aegean Region is one of the
local races in Turkey which was not studied completely and determined, yet (Kence, 2006;
BfD, 2006). But, migratory beekeeping in order to yield high honey production is still a
threat for them with hybridization and loss of their unique characteristics.
A queen bee mates once in its lifetime and continuity of the colony depends on it. Mating
process occurs in the air, over ten meters above ground. A queen bee may fly away a mile
or more. With the pheromones it excretes, it warns drones for mating. In migratory
beekeeping, this warning may call local drones and cause hybridization. Also, commercial
queen bees are one of the greatest threats for local characteristics. For example, a queen
bee on market is sold as Apis mellifera caucasica but generally they are produced in the
Mediterranean and Central Anatolia. This queen honey bee is not a pure Caucasus honey
bee since there are not any local Caucasian colonies present in their mating areas (Gould
and Gould, 1995; BfD, 2006).
9
1.11. Aim of the Study
Recently, it was showed that population structure of honey bees in Europe was lost or
strongly disturbed (reviewed in De la R´ua, 2009). Cá’novas et al. (2011) showed the high
level of homogenization in Iberian honey bees of Spain with microsatellite primers.
Dall’Olio et al. (2007) also used microsatellite markers in order to show the losses of
populations structuring of honey bees in Italy which was observed in the past and all of the
peninsular populations united as a single population. In Greece, Bouga et al. (2011) showed
that there is not any significant differentiation among native subspecies of A. m. adami, A.
m. macedonica, and A. m. cecropia based on mitochondrial and allozyme data, in spite of
observing morphological differences. These losses of honey bee population structure are
mostly attributed to using commercial queens or colonies, introducing of non-native races
and migratory beekeeping. This attribution has been confirmed by scientists from different
parts of the world with population genetics studies on their local honey bees. In Sudan, ElNiweiri and Moritz (2010) showed the introgression of non-native DNA in wild honey bee
populations. They compared regions with and without apiculture and showed that the
regions without apiculture have lower level of gene flow than the regions with apiculture
have. In Saharan oases, Shaibi and Moritz (2010) also showed genetic foot prints of
introgression in isolated regions caused by commercial beekeeping with microsatellites.
Genetic diversity has got a key role for adapting of organisms to the changes in their
environment by natural selection. Populations with little genetic variation are more
defenseless when they are exposed to new pests, pathogens, new climatic conditions or
habitat destroyed by human. Those effects can cause eventually a decline in honey bee
population and furthermore, extinction. Locally adapted variants may be encountering less
stress. Resilience of the honey bees may be lying in the adaptations that they accumulated
over thousands of years, and in the new potentials resided in their genetic diversity.
Because of these reasons, adaptive genetic diversity belonged to the honey bee populations
should be protected (Frankham, 2005).
In Turkey, migratory beekeeping is very common in order to obtain higher honey
production and for pollination practices. Genetic effect of this beekeeping practice has not
been extensively studied. It became a necessity to demonstrate it in terms of taking more
concrete steps in conservation of the high genetic variability of honey bee subspecies
observed in Turkey.
The main aim of the study is testing the hypothesis about any genetic difference of Muğla
stationary and Muğla migratory population by using ISSR primers and to show genetic
effect of migratory beekeeping on Muğla migratory population. In order to support the
obtained genetic differences between Muğla populations, each of them was also compared
with the other stationary populations (Kırklareli, Yığılca, Hatay, and Artvin). Expected
outcome of those comparings is high genetic difference among Muğla stationary and the
other stationaries; and lower genetic difference among Muğla migratory and the other
stationaries. While comparing all stationary populations, one of the aims is to demonstrate
and verify the outcomes of previous studies on high genetic diversity of honey bee
populations in Turkey. Other purposes of the study are to evaluate any possible population
10
structuring and to compare with the outcomes of previous studies; to show phylogenetic
relationships of the populations; to test ISSR primers for their discrimination ability on
honey bee populations in Turkey.
11
12
CHAPTER 2
MATERIALS AND METHODS
2.1. Biological Material
In biological studies, especially in population genetics, sampling is critically important. In
order to get better results, random samples that were collected should be close to reflect the
actual variation in natural populations. The individual honeybee workers of colonies are
generally descended from a single queen, thus, a location to be sampled extensively from
few colonies is not a good representation. Instead, collecting a few worker bees from a high
number of colonies provides better representation.
For this study, a total of 237 honeybee workers from 24 colonies in 5 provinces (Kırklareli,
Yığılca, Muğla, Hatay and Artvin) were collected by random. Samples from Kırklareli,
Yığılca, Hatay, and Artvin were collected from stationary colonies. Samples from Muğla
were collected from both stationary and migratory colonies. Muğla samples were obtained
from ‘’Development of Resistance to American foulbrood in Honey Bees (Apis mellifera
anatoliaca) of Muğla (Project No: TAGEM 11/AR-GE/13)’’. The apiaries, that stationary
samples were collected, have not used commercial queens or colonies and have not been
used for migratory practices for 10-30 years. 137 individuals of 237 honey bees were
stationary and 120 were migratory honey bees. Names of the provinces, locations, and
number of bees collected from each province were given in Table 2.1. Samples have been
kept in –80 oC deepfreeze until DNA isolation.
Table 2.1 Information of honey bee samples
Provinces
Kırklareli
Yığılca
Muğla stationary
Muğla migratory
Hatay
Artvin
Total
Number of Number of individuals
colonies
sampled from each
colony
4
5
4
5
4
10
4
30
4
5
4
5, 5, 5, 2
24
237
13
Total number of
individuals
20
20
40
120
20
17
237
2.2. DNA Isolation
Fermentas 512 DNA purification kit for DNA isolation was used. Bee heads were
immediately removed after taking bees out of –80 oC deepfreeze. Each head was added into
a 1.5 ml tube which included 100 μL TE buffer (Appendix A) and comminuted to get
homogeny in order to prepare the samples for DNA isolation. After homogenization, 400
μL lysis solution was put to each sample and then they were incubated for 10 minutes in a
water bath at 65 oC. During incubation, tubes containing samples were occasionally
inverted. After that, 600 μL chloroform was put to each sample and they were centrifuged
at 11.000 rpm for 5 minutes.
After centrifugation, upper aqueous phases of each tube that contains DNA were
transferred to the new tubes. 720 μL sterile deionized water was mixed with 80 μL of
supplied 10X concentrated precipitation solution and 1X diluted precipitation solution was
obtained. And this new precipitation solution was added to the new tubes containing DNA.
Admixture was gently inverted at room temperature for 3 minutes and then centrifuged at
11.000 rpm for 5 minutes. After removing supernatants, the DNA pellet of each tube was
dissolved in 100 μL of 1.2 M NaCl solution.
After being sure that the pellet is completely dispersed, 300 μL of pure cold ethanol was
added and samples were stored at – 20 oC for 2 hours to let the DNA precipitate. Then
samples were centrifuged at 11.000 rpm for 5 minutes. Finally, supernatants were removed
and remaining pellets were dissolved in 100 μL deionized water. Solutions containing
DNA were examined with spectrophotometer. In order to determine absorption of DNA
and protein, 260 nm and 280 nm were used, respectively.
2.3. ISSR Markers Amplification by PCR
Four 3’ anchored ISSR primers; UBC818, UBC825, UBC827, UBC828 were tested in this
study whose core regions and primer sequences were given in Table 2.2. 18,4 μL of
amplification reactions were performed with 1,5 μL (80-100 ng) of genomic DNA, 1050
pmol of primer, 0.20 mM dNTPs, 2.7 mM MgCl2, 0,85 U Taq Ploymerase and 2 μL of 10X
Taq Buffer with KCl. PCR was began with a denaturation step of 2 minutes at 94 oC and
continued with 35 cycles consist of a 30 seconds denaturation segment at 94 oC, a 30
seconds annealing segment at 50 oC, a 2 minutes elongation segment at 72 oC. Final
elongation step was extended to 5 minutes to permit all of the products to be completely
extended.
14
Table 2.2 Information of ISSR primers used in the study
Primer Name
UBC818
UBC825
UBC827
UBC828
Core Region
(CA)8G
(AC) 8T
(AC) 8G
(TG) 8A
Sequences
CAC ACA CAC ACA CAC AG
ACA CAC ACA CAC ACA CT
ACA CAC ACA CAC ACA CG
TGT GTG TGT GTG TGT GA
2.3.1 Agarose Gel Electrophoresis
The PCR products were separated by gel electrophoresis on 1.4 % agarose gel in 1X TBE
buffer (Appendix A). 2.7 gr agarose was put into 150 ml 1 X TBE buffer and the mixture
was boiled for 3 minutes in microwave. When the gel cooled to 55 oC, it was poured into
an electrophoresis gel tray with the well comb. After 20 minutes in the room temperature,
the gel polymerized. The well comb was taken away and the gel was put in the gel tank. 5
μL of the each PCR product was used for well loading of the gel. During loading of the
samples, 1 μL of loading buffer (Appendix A) was added to each 5 μL of PCR product and
the mixture of two was loaded. Each well represented only one sample. 1 X TBE buffer
was used as running buffer. 4 μL of O’RangeRuler™ 100+500 bp DNA Ladder, ready-touse (Fermentas), was used as DNA fragment size marker. Reproducibility of the DNA
profiles was tested by repeating the PCR amplifications with at least two times.
Electrophoresis was run at 100 V for 1.5 h by using double entering power supply. After
separation of the PCR products by electrophoresis, the gels were stained with ethidium
bromide (10 μL/ 1ml) for 15 minutes, destained in destile water for 20 minutes and
visualized under UV.
2.4. Statistical Analyses
2.4.1. Data Analyses and Scoring
Three samples from a single colony were used for initial primer screening. After initial
screening, four of the eleven ISSR primers (UBC818, UBC825, UBC827, UBC828) were
chosen. The PCR-generated band profiles were scored manually based on their presence
(1) or absence (0) corresponding to the situation of co-migrating fragments for all
individuals and were transformed into a binary matrix. O’RangeRuler™ 100+500 bp DNA
Ladder was used as DNA marker for scoring of bands.
15
2.4.2. Genetic Variation
Currently, there are lots of computer programs available for population genetic analysis.
Information about population structure, diversity and differentiation can be obtained by
those programs. PopGene is a Windows-based computer package which is very userfriendly. One can obtain free downloadable version on http://www.ualberta.ca/~fyeh.
Binary data matrices of present – absent scale based on manually scoring of the gels were
examined by using PopGene v1.32 (Yeh et al., 1997), assuming Hardy–Weinberg
equilibrium. Input options which were selected in the initial menu were dominant character
of the markers, multiple population, diploid character of the honey bees and grouping.
Groupings were made in order to calculate pairwise Nm and Gst values.
According to Nei’s formula (Nei, 1973) genetic diversity within and between populations
with their standard errors was examined by the following indices by PopGene software and
TFPGA: mean number of observed alleles per locus (na), mean number of effective alleles
(ne), percentage of polymorphic loci, mean number of expected heterozygosity (h), and
Shannon’s information index of diversity.
2.4.3. Genetic Distance
In the case of the multiple alleles, Gst is equivalent to the weighted average of Fst for all
alleles. Pairwise and overall Gst values were computed with PopGene v1.32 software (Yeh
et al., 1999) and TFPGA (Miller, 1997). Also, Nei’s standard genetic distance values,
which are useful in order to construct phylogenetic trees, were calculated by using
PopGene software.
2.4.4. AMOVA
AMOVA test was used for ISSR analyses in GenAlex v6.5 (Peakall and Smouse, 2012)
and Arlequin (Excoffier et al., 2005). Four different AMOVA were performed in order to
test population structurings among samples. PhiPT which is an Fst analogue that can be
used for dominant data analyses was calculated. Arlequin software is very useful in
analyses of co-dominant markers but is limited in the case of dominant markers. In order to
achieve AMOVA analyses, binary matrix of dominant data was assumed as RFLP data.
2.4.5. Genetic Diversity
Genetic diversity levels of the populations were examined by using PopGene v1.32. An
ideal index must discriminate clearly and accurately between samples, differences in
sample sizes must not considerably affect it, and also it must be relatively simple to
16
calculate. The Shannon index, which is employed with both large and small sample sizes,
was calculated for all populations (Shannon and Weaver, 1949).
2.4.6. Gene Flow
Gene flow is known to decrease genetic divergence and cause to homogenization. Nm
estimates for the total population and pairwise Nm estimates for population pairs can be
used to calculate gene flow. The average private allele frequencies to estimate the effective
number of migrants per generation (Nm) are used by this method (Slatkin, 1985). PopGene
v1.32 was used to calculate Nm values based on ISSR data. To evaluate the Nm values,
following statements was used: Local populations belong to one panmictic (randomly
mating) population in the case of when Nm > 4 (Wright, 1931). When Nm < 2 then, there is
still a considerable opportunity for genetic divergence among subpopulations and Nm < 1.0
shows the limited genetic exchange among populations (Hartl and Clark, 1997).
The following equation can be used to estimate gene flow from multiple loci:
Nm = 0.5(1 - Gst)/Gst
where N is the effective population size, m is the effective proportion of immigrants, and
Gst is the coefficient of genetic differentiation, which measures variation in allele
frequencies among populations (McDermott, and McDonald, 1993).
2.4.7. Mantel Test
Correlation between genetic and geographical distances was measured by Mantel (1967)
test in studies. The null hypothesis claims that there is no link between genetic and
geographical distances. Pearson correlation (r) which ranges from -1 to 1 can be used to
measure strength of relationship between geographical distance and genetic distance. The
significance can be tested using t statistic. Mantel test was performed using Microsoft®
Excel 2008/XLSTAT©-Pro (Version 7.2, 2003, Addinsoft, Inc., Brooklyn, NY, USA). The
distance matrices were obtained from Nei’s standard genetic distance matrix estimated
from ISSR data and geographic distance matrix between the regions.
2.4.8. Phylogenetic Tree
Nei’s standard genetic distance (1972) values generated by PopGene were used in order to
construct Neighbour – Joining (NJ) and Unweighted Pair Group Method with Arithmetic
17
Mean (UPGMA) trees of all populations. PHYLIP v3.8 (Felstein, 1992) was used for NJ
tree and PopGene v1.32 was used for UPGMA trees. Visualizations were generated by
TreeView (Page, 1996).
2.4.9. Principal Coordinates Analysis (PCO or PcoA)
Since the obtained data from dominant marker is not quantitative but is qualitative,
Factorial Correspondence Analysis (FCA), Principle component analysis (PCA) or other
ordination methods which are not suitable could not be performed. Instead of it, PCO,
which gives efficient results with qualitative data, was performed for ordination analysis.
The procedure is performed with a distance matrix which is a similarity or a dissimilarity
matrix and attributes each item to a location in a low-dimensional space which is a 3D
graphics. Principal Coordinate Analysis (Gower, 1966) was performed using Jaccard’s
similarity index calculated using NTSYS-pc v2.20e (Rohlf, 2000). It is important to choose
a suitable index of similarity between individuals for clustering and analyzing diversity
within and among populations, since different dissimilarity indices can cause conflicting
outcomes (Kosman and Leonard, 2005). Jaccard coefficient which is commonly used in
literature was preferred in order to perform Principal Coordinate Analyses, thus, it is more
proper with the data of dominant marker which is binary (Landry, 1996); has easy
interpretation which is a rate between the number of coincidences and the total number of
bands; and it does not consider negative co-occurrences (Meyer et al., 2004). Analysis was
made by using the modules: SIMQUAL, DCENTER, EIGEN, and MOD3D in order to
obtain 3D illustration. Also, MxPlot module for 2D illustration was used. PCO is more
informative in terms of distances among major groups and Cluster Analyses can be also
validated by it.
Centroid distances based on PCO were performed by using PCO software (Anderson,
2003). Principal coordinate analysis of any symmetric distance matrix can be calculated by
PCO software in the manner of Gower (1966).
2.4.10. Population Structure
According to Evanno et al. (2005)’s study, the number of individual of Muğla populations
was arranged to the general individual number. Generally, populations had 20 individuals.
So, 20 out of 120 individuals of Muğla migratory and 20 out of 40 individuals of Muğla
stationary were randomly picked. STRUCTURE 2.3.3 (Pritchard et al., 2000) was used for
population structure analyses. Firstly, probabilities of different K values of 1 to 10, which
are the assuming of the number of distinct populations, were calculated. All runs for each
single K (number of populations or clusters) were replicated 10 times. To ensure
convergence of the Monte Carlo Markov Chain (MCMC), 20.000 burn-ins followed by
50.000 iterations was used. Different MCMC and burn-ins were also performed (20.000
burn-ins followed by 150.000 iterations; 50.000 burn-ins followed by 250.000 iterations;
18
50.000 burn-ins followed by 500.000 iterations). Secondly, STRUCTURE HARVESTER
software (Earl and von Holdt, 2012) was used to analyze the results of the different K
values and to obtain the most probable K values. After finding of the most possible K
value, STRUCTURE 2.3.3 was run again for computing individual membership
coefficients which are the probabilities of individuals assigning to different clusters. This
most probable K was replicated 20 times with the same MCMC and iteration parameter
settings of previous.
After the last 20 times replication of the most possible K value, CLUMPP software
(Jakobsson and Rosenberg, 2007) was performed for permutation of the membership
coefficients of individuals. This step is used in order to correct the errors of values
calculated by STRUCTURE 2.3.3. Finally, DISTRUCT software (Rosenberg, 2004) was
used in order to illustrate the results of CLUMPP.
19
20
CHAPTER 3
RESULTS
3.1. Genetic Diversity
237 individuals from 5 provinces which are Kırklareli, Yığılca, Muğla, Hatay and Artvin
were examined and a total of 25 reproducible bands were observed from 4 primers (Figure
3.1). 10 loci were polymorphic in at least one population of overall 25 polymorphic loci.
Figure 3.1 DNA fingerprinting of 29 honey bee genotypes from based on ISSR marker
using UBC818. Lane 1-10: Muğla stationary population, Lane 11-29: Muğla migratory
population. M: Molecular marker, Lane 30: Negative control
Firstly, analyses at the population level were made between stationary and migratory
population of Muğla honey bee. Secondly, stationary population of Muğla were compared
with the other stationary populations, which are Hatay, Kırklareli, Artvin and Yığılca.
Also, thirdly, migratory population of Muğla was compared with the other stationary
populations without Muğla stationary. And finally, statistical analyses were examined at
the species level with the overall molecular data.
Number of observed bands ranged in overall molecular data between 13 and 17 with the
percentages of polymorphic loci 40% and 60%. Hatay population had the lowest values
whereas Muğla migratory had the highest values (Table 3.1). The mean number of
21
observed alleles (Na) ranged between 1.6 and 1.4 and the mean number of effective alleles
(Ne) varied between 1.4122 and 1.1848, in all populations. Nei’s gene diversity (He) values
for stationary honey bee populations varied between 0.2287 and 0.1146. Finally, Shannon’s
information index (I) values ranged from 0.3316 to 0.1778. In general, the highest values of
genetic variability measurements belonged to Kırklareli population, while only the highest
mean number of observed alleles value (1.600) belonged to Muğla migratory population
(Table 3.2). And the lowest values belonged to Hatay population. It can be seen from the
Table 3.2 that, values of Muğla migratory are relatively greater than values of Muğla
stationary.
Table 3.1 Number of observed bands and percentage of polymorphic loci of all populations
Population name
Sample
size
Kırklareli
Yığılca
Muğla stationary
Muğla migratory
Hatay
Artvin
Total
20
20
40
120
14
16
237
Number of Percentage
of
observed
polymorphic
bands
loci
15
56.00 %
16
56.00 %
16
48.00 %
17
60.00 %
14
40.00 %
13
48.00 %
25
100.00 %
Table 3.2 Genetic diversity parameters between Muğla stationary and the other stationary
populations. Na, the mean number of observed alleles; Ne, the mean number of effective
alleles; He, Nei’s gene diversity; I, Shannon’s information index
Population name
Kırklareli
Sample
size
20
Yığılca
20
Muğla stationary
40
Muğla migratory
120
Hatay
20
Artvin
17
Total population
237
Na
Ne
He
I
1.5600
±0.5066
1.5600
±0.5066
1.4800
±0.5099
1.6
±0.50
1.4
±0.5000
1.4800
±0.5099
2
±0.00
1.4122
± 0.4216
1.2949
± 0.3541
1.2742
± 0.3503
1.3313
± 0.3766
1.1848
± 0.3000
1.2970
± 0.3599
1.4044
± 0.3363
0.2287
± 0.2225
0.1785
± 0.1905
0.1644
± 0.1950
0.1931
± 0.2068
0.1146
± 0.1719
0.1767
± 0.1993
0.2507
± 0.1636
0.3316
± 0.3154
0.2735
± 0.2747
0.2477
± 0.2842
0.2882
± 0.2951
0.1778
± 0.2528
0.2641
±0.2906
0.3964
± 0.2076
22
3.2. Genetic Differentiation
3.2.1. Gene Flow
At the population level, Muğla migratory population was compared to Muğla stationary
population. The data showed that the diversity within population (Hs) of Muğla migratory
population (0.1931) was higher than Muğla stationary population (0.1644). The pairwise
value of total gene diversity (Ht) and the pairwise value of Hs of all populations were
illustrated in Table 3.3. The mean level of genetic differentiation (Gst) between Muğla
populations was very low but cannot be negligible. The average gene flow (Nm) value was
very high and indicates more than thirteen migrants per generation into a population (Table
3.4). While Nm value between Muğla populations was higher than 4, it can be said that
they belong to one panmictic (randomly mating) population (Wright, 1931).
Table 3.3 Total gene diversity (Ht, below diagonal) and the diversity within population
(Hs, above diagonal) values among all populations
Pop name
Kırklareli
Yığılca
Muğla
stationary
Muğla
migratory
Hatay
Artvin
Kırklareli
****
Yığılca
Muğla
stationary
0.2871
±0.0370
0.2972
±0.0345
0.2036
±0.0237
****
0.1966
±0.0235
0.1715
±0.0211
****
0.2109
±0.0306
0.1858
±0.0265
0.1788
±0.0355
0.1717
±0.0248
0.1466
±0.0189
0.1395
±0.0207
0.2027
±0.0216
0.1776
±0.0186
0.1706
±0.0215
Muğla
migratory
0.2917
±0.0388
0.1853
±
0.0370
0.2052
±0.0369
0.2483
±0.0376
****
0.1539
±0.0255
0.1849
±0.0234
0.2070
±0.0373
0.2461
±0.0365
****
0.1457
±0.0256
****
Hatay
0.2107
±0.0318
0.2182
±0.0354
0.2505
0.2328
±0.0377 ±0.0359
Artvin
0.2923
0.2630
±0.0341 ±0.0338
Average Ht = 0.2906 ±0.0227
23
0.2358
±0.0463
Average
Hs =
0.1760
±0.0140
Table 3.4 Coefficient of gene differentiation (Gst, below diagonal) and gene flow (Nm,
above diagonal) values among all populations
Pop name
Kırklareli
Yığılca
Muğla
stationary
Muğla
migratory
Hatay
Artvin
Kırklareli
****
1.2204
0.9763
1.3057
1.0887
1.1311
Yığılca
0.2906
****
2.1859
2.8719
0.8501
1.0404
Muğla
stationary
Muğla
migratory
Hatay
0.3387
0.1862
****
13.7380
1.0621
1.0975
0.2769
0.1483
0.0351
****
1.4477
1.5112
0.3147
0.3703
0.3201
0.2567
****
0.8082
Artvin
0.3065
0.3246
0.3130
0.2486
0.3822
****
Average
Nm =
0.7684
Average Gst = 0.3942
When stationary population of Muğla was compared with the other stationary populations,
pairwise total gene diversity (Ht) ranged between 0.2052 and 0.2972. The highest value
was observed between Muğla stationary and Kırklareli population and the lowest value
belonged to Muğla stationary and Hatay. Pairwise diversity within population (Hs) values
ranged between 0.1395 and 0.2036, which belonged to Hatay and Muğla stationary,
Kırklareli and Yığılca, respectively (Table 3.3). The highest pairwise Gst value was
calculated between Hatay and Artvin and the lowest pairwise Nm value was also calculated
between the former ones. The lowest pairwise Gst value was detected between Muğla
stationary and Yığılca population and the highest gene flow also belonged to them (Table
3.4). The average values of total gene diversity, diversity within population, genetic
differentiation and gene flow of all stationary populations were 0.3001, 0.1726, 0.4248 and
0.6769, respectively. Nm < 1.0 shows the limited genetic exchange among populations.
Muğla migratory population was compared with the other stationary populations without
Muğla stationary. Pairwise Hs, Ht and Nm values were relatively higher and Gst value was
relatively lower than the values between Muğla stationary population and the other
stationary populations. Ht values ranged from 0.2070 and 0.2923, which belonged to
Muğla migratory - Hatay and Kırklareli - Artvin population, respectively. Hs values were
varied between 0.1457 and 0.2109, belonged to Artvin - Hatay and Muğla migratory Kırklareli population, respectively (Table 3.3). The average Ht and Hs values were 0.2969
and 0.1783, respectively. The highest pairwise Gst and the lowest pairwise Nm value
belonged to Artvin - Hatay population whereas the lowest pairwise Gst value and the
highest pairwise Nm value belonged to Muğla migratory – Yığılca (Table 3.4). The average
24
Gst value was 0.3993 and the average Nm value was 0.7521 which shows the limited
genetic exchange among populations.
3.2.2. Analysis of Molecular Variance (AMOVA)
For the purposes of the study, four different AMOVA results have been obtained. In the
first analysis, Muğla stationary and Muğla migratory population were compared. Secondly,
all of the stationary populations were compared according to their supposed
subspecies/clusters. Thirdly Muğla migratory and the other stationary populations without
Muğla stationary were compared similar as the second analysis. Lastly, Muğla stationary
and Muğla migratory population were united as a single Muğla population and were
compared with the other stationaries similar as second and third analysis (Table 3.5).
All comparisons performed by AMOVA were significant. They showed high population
structurings except comparison of Muğla populations. At the first analysis PhiPT was
calculated as 0.042 (p < 0.001). According to Wright (1978)’s suggestion, the range 0.0 to
0.05 can be considered as indicating little genetic differentiation. PhiPT value was also
close to the coefficient of gene differentiation between the same populations (Gst =
0.0351).
PhiPT value of second analysis was 0.544 (P < 0.001) whereas PhiPT value of third
analysis was 0.497 (p < 0.001) and the last PhiPT value was 0.438 (p < 0.001). PhiPT
values were also calculated by Arlequin software and the same significant values were
found (p = 0.00).
Table 3.5 AMOVA results
Populations
Muğla Stationary vs
Migratory
All Stationary Populations
Muğla Migratory vs the
Stationaries
without
Stationary
United Muğla Populations
Other Stationaries
Variation
Populations
Among
Variation
Populations
Muğla 4.2%
95.8%
54.4%
Other 49.7%
Muğla
45.6%
50.3%
vs the 43.8%
56.2%
25
Within
All pairwise PhiPT values of all populations were calculated by using AMOVA. They
ranged between 0.042 and 0.612. The closest populations were Muğla stationary and
Muğla migratory and the most distant populations were Kırklareli and Artvin population
(Table 3.6). It can be seen from the Table 3.6 that Muğla migratory population is relatively
closer to the other stationary populations than Muğla stationary population is.
Table 3.6 Pairwise PhiPT values of all populations (p < 0.001)
Population name
Kırklareli Yığılca
Kırklareli
Yığılca
Muğla stationary
Muğla migratory
Hatay
Artvin
0.000
0.549
0.606
0.572
0.545
0.612
0.000
0.362
0.323
0.563
0.556
Muğla
Muğla
Hatay
stationary migratory
0.000
0.042
0.517
0.563
0.000
0.442
0.518
0.000
0.579
Artvin
0.000
3.3. Phylogenetic Trees
Pairwise Nei’s standard genetic distances and identities (1972) were calculated and were
showed in Table 3.7. From data it is revealed that genetic distance between Muğla
stationary and Muğla migratory is 0.0158 and genetic identity is 0.9843 which indicate that
they are local races (Nei, 1972).
Table 3.7 Nei's genetic identity (above diagonal) and genetic distance (below diagonal)
values among all populations
Pop name
Kırklareli
Yığılca
Muğla
stationary
Muğla
Hatay
migratory
Artvin
Kırklareli
Yığılca
Muğla
stationary
Muğla
migratory
Hatay
Artvin
****
0.2346
0.2877
0.7909
****
0.0994
0.7500
0.9053
****
0.7955
0.9206
0.9843
0.8116
0.7985
0.8477
0.7756
0.7924
0.8126
0.2288
0.0828
0.0158
****
0.8753
0.8499
0.2088
0.2541
0.2250
0.2327
0.1652
0.2075
0.1332
0.1626
****
0.2363
0.7896
****
26
Nei’s standard genetic distances and identities between Muğla stationary and the other
stationary populations were calculated and genetic distances ranged from 0.0994 and
0.2877 and genetic identities ranged from 0.7500 to 0.9053. Muğla and Yığılca population
had the lowest genetic distance value and highest genetic identity value and were the most
close populations whereas the highest genetic distance value and the lowest genetic identity
value belonged to Kırklareli and Muğla population which were the most distant (Table
3.7).
When Muğla migratory and the other stationary populations were compared for Nei’s
genetic distance and identity, results showed some differences. Values of genetic distance
varied between 0.0828 and 0.2541 and identity values ranged from 0.9206 and 0.7756.
Muğla migratory and Yığılca population had the lowest genetic distance value and highest
genetic identity value and were the closest populations whereas the highest genetic distance
value and the lowest genetic identity value belonged to Kırklareli and Artvin populations
which were the most distant (Table 3.7).
It can also be seen that genetic distance values between Muğla migratory and the other
stationary populations are lower than Muğla stationary and the other stationary populations
and genetic identity values of Muğla migratory population are higher than the ones of
Muğla stationary, as well.
UPGMA (Unweighted Pair Group Method with Arithmetic Mean) and Neighbour Joining
trees of all populations were constructed based on Nei’s (1972) standard genetic distances
(Figure 3.2, Figure 3.3, and Figure 3.4). In UPGMA dendrogram, two main branches were
observed. First main branch were divided into two branches. Kırklareli clustered separately
from all other populations by constructing the main first branch. In the second branch,
Artvin clustered separately from other populations by constructing a branch itself. Muğla
stationary, Muğla migratory and Yığılca clustered together and Hatay separated from them
(Figure 3.2 and Figure 3.3).
27
Figure 3.2 UPGMA tree of all populations
Figure 3.3 Unrooted UPGMA tree of all populations
28
In the Neighbour Joining dendrogram, Kırklareli constructed branch itself and separated
from other populations. The relativity of other stationary populations is illustrated in the
dendrogram (Figure 3.8.).
Figure 3.4 Neighbour joining tree of all populations
3.4. Ordination of Populations
Principal coordinate analysis (PCO) was performed to obtain a better explanation of the
relationship among individuals. The expressed percentages of each axis pointed out the
importance of the related variance of each axis relative to the total variance. Firstly, the
data obtained from Muğla migratory and Muğla stationary population by using 4 ISSR
primers that examined in PCO (Gower, 1966) with Jaccard coeffient. The first 21 axes
explained 90.1418% of the total variance. The first three axes explained 36.0197% of the
total variance, meaning that markers are plotted over different parts of the genome (Table
3.8). If the first two or three axes explain 10 - 20% of total variance, it indicates that this
may not be proper for graphical illustrations but points out proper sampling of primers
from the all genome (Siahsar et al., 2010). Eigenvalues, proportions of variances expected
by using broken-stick model, relative percentages, and cumulative of first seven
coordinates belong to Muğla populations are showed in Table 3.8. 3D illustration and 2D
illustration belong to Muğla stationary and Muğla migratory populations are showed in Fig.
3.5 and Fig. 3.6, respectively.
29
Table 3.8 Results of Principal Coordinate Analysis (PCO) of Muğla populations
i
1
2
3
4
5
6
7
Eigenvalue
7.60900112
5.94009231
5.48290211
4.60566520
4.22970545
3.26322672
2.92937490
Expected
3.5347
2.9097
2.5972
2.3889
2.2326
2.1076
2.0034
Percent
14.4007
11.2421
10.3769
8.7166
8.0051
6.1759
5.5441
Cumulative
14.4007
25.6428
36.0197
44.7363
52.7414
58.9173
64.4614
Figure 3. 5 3D illustration from principal coordinates analysis of Muğla migratory and
Muğla stationary populations
30
Figure 3.6 2D illustration from principal coordinates analysis of Muğla migratory and
Muğla stationary populations
Secondly, the data obtained from all populations by using 4 ISSR primers was examined in
PCO (1966) with Jaccard coeffient. The first 37 axes explained 90.2041% of the total
variance. The first three coordinates explained 31.5259% of the total variance which means
that markers are plotted over different parts of the genome (Table 3.9). 3D illustration
(Figure 3.7) and 3D illustration (Figure 3.8) of all populations are obtained. The groupings
obtained in Cluster Analysis were confirmed by PCO. Eigenvalues, proportions of
variances expected by using broken-stick model, relative percentages, and cumulative of
first seven coordinates are showed in Table 3.9.
Table 3.9 Results of Principal Coordinate Analysis (PCO) of all populations
i
1
2
3
4
5
6
7
Eigenvalue
16.42749496
10.64547217
8.65610028
8.13085167
6.61716880
6.15421152
5.07185309
Expected
2.5516
2.1297
1.9187
1.7781
1.6726
1.5882
1.5179
Percent
14.4950
9.3931
7.6378
7.1743
5.8387
5.4302
4.4752
31
Cumulative
14.4950
23.8881
31.5259
38.7002
44.5389
49.9692
54.4444
Figure 3.7 3D illustration of principal coordinates analysis of all populations
Figure 3.8 2D illustration without showing vectors from principal coordinates analysis of
all populations
32
Distance matrix was obtained by using PCO software based on centroids calculated from
principal coordinates. Distance values ranged from 0.66834 to 0.08777. According to
matrix, the most distant populations were Artvin and Kırklareli (0.66834), whereas the
closest populations were Muğla stationary and Muğla migratory population (0.08777)
(Table 3.10).
Table 3.10 Distance matrix based on centroids calculated from principal coordinates
Pop
name
Kırklareli
Kırklareli
Yığılca
0.59670
Yığılca
Muğla
Muğla
Hatay
stationary migratory
Artvin
0.0000
0.0000
Muğla
0.58229
stationary
Muğla
0.55644
migratory
Hatay
0.55390
0.34599
0.0000
0.33872
0.08777
0.0000
0.60622
0.46727
0.43800
0.0000
Artvin
0.63223
0.53902
0.52442
0.62089 0.0000
0.66834
3.5. Correlation between Genetic Distance and Geographic Distance
Mantel test was performed using Nei’s standard genetic distance and geographic distance
matrices. Firstly, it was examined between Muğla stationary and the other stationary
populations. The p-value was calculated as 0.570 by using the distribution of r (0.207)
estimated from 10000 permutations and it is revealed that p-value is lower than the
significance level alpha= 0.05 which indicates that the matrices are not correlated.
A scatter plot showing matrix comparison of genetic and geographic distance matrices of
Muğla stationary and the other stationary populations and a histogram chart showing the
distribution from which the p-value was obtained are illustrated in Figure 3.9 and Figure
3.10.
33
Figure 3.9 Scatter plot for comparison of genetic and geographic distance between Muğla
stationary and the other stationary populations
34
Figure 3.10 Histogram chart from results of Mantel test for Muğla stationary and the other
stationary populations
Secondly, Mantel test was examined between Muğla migratory and the other stationary
populations. The p-value was calculated as 0.720 by using the distribution of r (0.132)
estimated from 10000 permutations. p value was lower than the significance level
alpha= 0.05 which indicates that the matrices are also not correlated. A scatter plot of
Muğla migratory and the other stationary populations showing matrix comparison of
genetic and geographic distance matrices and a histogram chart showing the distribution
from which the p-value was obtained are illustrated in Figure 3.11 and Figure 3.12.
35
Figure 3.11 Scatter plot for comparison of genetic and geographic distance between Muğla
migratory and the other stationary populations
Figure 3.12 Histogram chart from results of Mantel test for Muğla migratory and the other
stationary populations
36
3.6. Population Structure
STRUCTURE software was run for arranged data with different K values of 1 to 10 in
order to estimate the most probable number of populations. Firstly, the ISSR data belonged
to stationary populations that was used in all other statistical analyses in this study was
performed with the program. Then STRUCTURE HARVESTER program was used to
obtain the distribution of K likeliness’ (Figure 3.13, and Figure 3.14). According to the
results, the best K values were chosen by STRUCTURE HARVESTER. For the final step,
the best value of K was assumed as 4 and the membership coefficients for individuals
determined (Figure 3.15). Then, they were permuted for consistency. Secondly, the same
procedure was performed for overall data. Results for stationary populations as well as the
overall data were presented in Figure 3.16 and Figure 3.17, respectively.
Figure 3.13 Ln (probabilities) for K values
37
Figure 3.14 Rates of change in the likelihood of Ks
Figure 3.15 DeltaK values
38
Figure 3.16 Cluster assignments of stationary colonies
Figure 3.17 Cluster assignments of stationary colonies and Muğla migratory
39
40
CHAPTER 4
DISCUSSION
According to previous studies, five subspecies of Apis mellifera has been distributed in
Turkey. Those subspecies are A. mellifera caucasica in North Eastern, A. mellifera meda in
Eastern , A. mellifera carnica in Thrace, A. mellifera syriaca in South Eastern and A.
mellifera anatoliaca in Central Anatolia (Kandemir et al., 2000; Bodur et al., 2007).
Genetic variation and differentiation of honey bees of Turkey have been studied based on
morphometry, allozymes, microsatellites, or other genetic markers (Bouga et al., 2011).
In this study, we used ISSRs which are dominant markers, in order to reveal the genetic
differences between stationary and migratory population of honey bees of Muğla ecotype
in Turkey. Also, the other stationary populations (Kırklareli, Yığılca, Hatay, and Artvin)
were examined with ISSRs. This is the first study that determines the genetic differences
between honey bee populations of Turkey based on ISSR marker. This technique has high
reproducibility in ISSRs; requires low DNA amount; is simple, quick, efficient, cheap, and
there is no need to use radioactivity. Heterozygotes from homozygotes cannot be
distinguished as a drawback, but it is very useful in large scale phylogenetic studies
(Wolfe, 2005). In this study, 4 ISSR primers were used which were 3’ anchored. The
benefit of 3’ anchored ISSR primers that was claimed by previous studies (Reddy, 2002)
was also seen in the process of scoring since scored bands were very clear. The number of
used ISSR primers in this study was also enough to distinguish the subspecies. When the
number of loci increases, it becomes possible to distinguish samples more efficiently,
especially in using STRUCTURE software in order to analyze population structure. Thus,
in further studies, large number of ISSR primers may be used.
Corresponding to previous studies (reviewed in Kence, 2006; Bodur, 2007), the distribution
of the subspecies of Apis mellifera in the regions of this study was found as follows; A. m.
anatoliaca is in Muğla and Yığılca; A. m. carnica is in Kırklareli; A. m. caucasica is in
Artvin and A. m. syriaca is in Hatay. The main purpose of the study is to compare Muğla
stationary and Muğla migratory bees, to see if Muğla stationary bee population represents
Muğla ecotype better than Muğla migratory bees. Since migratory bees visit many different
habitats belonging to other subspecies, one would expect that they are differentiated from
Muğla stationary, considerably.
In the comparison of Muğla populations, it was seen that the expected heterezygosity (He)
and Shannon’s information index (I) values of Muğla migratory were greater than the ones
of Muğla stationary which may confirm that Muğla migratory population, which also has
41
the highest percentage of polymorphic loci among all populations, is a more diverse
population than Muğla stationary is.
Among the stationary populations, the highest He and I values belonged to Kırklareli and
the lowest He and I values belonged to Hatay population. These values showed the same
pattern as previous studies with increasing from northern to southern and eastern to western
of Anatolia and having the highest I value in Kırklareli (Kandemir et al. 2000; Kence,
2006; Tunca, 2009; Kükrer, 2013). Tunca (2009) also found that Hatay population had the
lowest genetic diversity among the other populations. This may be because Hatay
stationary population is a terminal population which represents Apis mellifera syriaca
(Kandemir, 2006; Tunca, 2009).
The genetic differentiation (Gst) between Muğla stationary and migratory population was
found very low but indicates that there is still little genetic differentiation between
populations. The Nm value of them was found very high (more than thirteen migrants per
generation into a population) indicates that they belong to one panmictic (randomly
mating) population (Wright, 1931).
Gst and Nm values among all stationary population met the expectations and confirmed the
previous studies about the distribution of honey bees in Turkey. Muğla stationary
population had the lowest Gst and Nm values with Yığılca population. That is reasonable
while both of the populations belong to A. m. anatoliaca. As similar to previous studies
(Kandemir et al., 2005; Kence, 2006; Tunca, 2009), Muğla stationary had the highest Gst
and lowest Nm values with Kırklareli population. The Gst and Nm values among Muğla
migratory and the other stationary populations showed the similar pattern. That may need
attention owing to disordered routes and timing of migratory beekeeping resulting in mixed
gene flows among populations.
Among the stationary populations without Muğla stationary, the highest pairwise Gst and
the lowest Nm values were calculated between Hatay and Artvin. However, in the
microsatellite studies of honey bees of Turkey, it was showed that Artvin population is
closer to Hatay than Kırklareli population (Bodur et al., 2007; Tunca, 2009; Kükrer, 2013).
The conflict here may be caused by using dominant marker. The lowest pairwise Gst value
and the highest Nm were detected between Kırklareli and Yığılca population and confirmed
previous studies. It may be caused of geographic proximity of Yığılca and Kırklareli. The
average Gst value among all populations was very high and indicated that there is very
high genetic differentiation. The average Nm value showed that there is limited genetic
exchange among all populations. Gst values of the study was calculated higher and Nm
values were lower than Tunca (2009) obtained with RAPD markers, which might be caused
by using different dominant markers.
All AMOVA analyses of the bee populations of the present study turned out to be
significant which may explain the high genetic structure observed in the study. However, in
the comparison of Muğla populations, the obtained PhiPT was very low but significant and
indicated little genetic difference between populations. Variation among all stationary
populations was greater than variation within populations which confirms that the
stationary populations are isolated. But when Muğla migratory instead of Muğla stationary
42
was used in the analysis, then, the variation among population decreased as expected. With
a different approach, when Muğla stationary and migratory populations were united as a
single population of Muğla and compared with the other stationaries, the variation among
populations was lower than the variation within populations. This result also confirms that
migratory beekeeping disturbs population structure that subspecies have and is also in
agreement with the results of Kükrer (2013).
All pairwise PhiPT values of all populations were compatible with the recent study of
Kükrer (2013) and the closest populations were Muğla stationary and Muğla migratory
population (0,042) and the most distant populations were Kırklareli and Artvin population
(0,612). According to pairwise PhiPT results, Muğla migratory bees were closer to the
other stationary populations in comparison of the distance between Muğla stationary and
the other populations. Thus, Muğla migratory bees become more homogenized compared
to Muğla stationary bees. Pairwise centroid based distances of the bee populations from
principal coordinates obtained in this study were also in agreement with the pairwise PhiPT
values.
PCO analysis confirmed the four different subspecies. Muğla populations and Yığılca
population were in the middle of the graph indicated Anatolian subspecies and they
surrounded by other stationaries from three different subspecies. The distinction of
Kırklareli population can be seen from illustration. This result was compatible with
principal component analysis (PCA) result of Kükrer (2013) and also PCO result of Tunca
(2011).
In UPGMA tree, separately clustering of Kırklareli was seen, which was in agreement with
previous studies (reviewed in Kence, 2006). As expected, Yığılca and Muğla population
clustered together, owing to belonging to the same subspecies, A. m. anatoliaca. In NJ tree,
Kırklareli separated from the others and clustered alone, again. UPGMA and NJ trees
obtained from this study also confirmed previous studies (Kandemir et al., 2005; Tunca,
2009, 2011; Kükrer, 2013).
Evanno et al., (2005) had found that the results of STRUCTURE program are sensitive to
the type of genetic marker used (dominant vs. co-dominant), the number of loci scored, the
number of populations sampled, and the number of individuals typed in each sample. They
also claimed that longer burn-in or MCMC did not change significantly the results, that
also confirmed by us. When they compared dominant (AFLP) and co-dominant
(microsatellites) data sets for the same sampling intensity, they found that results of codominant markers are slightly more significant than dominant markers, but results of
dominant markers are also very repeatable. It was also found that, when the loci number of
dominant markers is higher, the results are more significant. And, if the numbers of
individuals each sample are not equal, the significance of the results may be affected.
In this study, we have arranged the number of individuals in our samples to be equal. Then,
the procedure of STRUCTURE analysis was performed and the most probable value of
clusters was obtained as four. Our result of population structure in stationary populations is
compatible with the recent study of Kükrer (2013). He also found the cluster number as
four in honey bees of Turkey which represents the four subspecies. The mixing between
43
Yığılca and Kırklareli and the mixing between Muğla stationary and Hatay that was seen in
this study may need more attention. Further studies with higher sample sizes would help to
understand this issue. In the illustration, gene flow of Artvin into other populations was
seen much lower than expected. In Turkey, trade of Caucasican honey bees is very
common, so the gene flow is expected to be higher. This may be caused by the low loci
number used in this study. When we analyzed all of the populations, the illustration was
not very explanatory because of Muğla migratory population. In overall data, cluster
assignments and individual gene flows between populations could not be observed, which
also supports that migratory beekeeping disturbs the structure of populations.
Results of analyses of among all stationary populations conducted here confirmed previous
studies on distribution and the presence of subspecies of honey bees in Turkey. The results
are important since the genetic diversity of honey bees in Turkey is highly conserved which
becomes harder owing to initiatives of queen breeding and exporting, and also nongovernmental companies which are promoted by investors. These anthropogenic effects are
still mixing populations and posing a threat for natural populations of honey bees. In order
to maintain current structure and genetic diversity, strict regulations should be established.
Results of stationary populations are also important regarding establishing the importance
of isolated regions. This study also contributes for understanding to phylogenetic
relationships among the populations in Turkey. If regions neighboring Anatolia and Thrace
are participated in future studies, a better understanding can be developed.
The genetic differentiation between Muğla stationary and Muğla migratory population was
found much lower than expected. And also, the gene flow between the populations was
very high which indicates that they are local populations. Muğla stationary and Muğla
migratory honey bees look quite close to each other, although there is a significant
difference between them. Why Muğla migratory bees did not differentiate from Muğla
stationary bees, despite very intensive migratory beekeeping practice, is an interesting
question. This may be because of reproductive isolation of Muğla migratory bees from the
other races. Mechanism of reproductive isolation between Muğla migratory populations
and the other bee populations remains to be investigated.
In Turkey, there is not a regulation or restriction related with the routes that the beekeepers
use. This is a crucial issue for conservation of the current biodiversity of honey bees. Also,
in the future studies on the genetic effect of migratory beekeeping, knowing the
information about the routes will be much useful in terms of evaluating the differentiation
among populations.
44
CHAPTER 5
CONCLUSION
Consistent with previous studies, it was revealed that honey bees of Turkey have a high
level of genetic diversity. That may be a result of the various climatic and phytogeographic
features of Anatolia. Subspecies of local honey bees have been adapted to the extremely
divergent climate and habitat conditions for thousands of years.
Honey bee population of Kırklareli (Thrace) was distinctly different from the Anatolian
honey bees. This result supports the previous findings which claim that they belong to C
lineage.
It was found that the differentiation between Muğla migratory bees and Muğla stationary
bees was very low, which may be attributed to reproductive isolation of Muğla migratory
bees from the other races. Mechanism of reproductive isolation between Muğla migratory
and the other bee races remains to be investigated.
Migratory beekeeping poses an important threat to genetic diversity and population
structure of honey bees in Turkey. Urgent regulation for migration routes of migratory
populations should be done in order to prevent the gene flow between migratory
populations and local populations. To preserve current diversity and genetic structure as
much as possible, wide isolated areas should be established for reducing the effect of
migratory beekeeping. It has been showed by earlier studies that migratory colonies are
more vulnerable to diseases, pathogens, viruses or pests. In order to maintain the health of
the local colonies, controlling of the migratory routes and keeping them away from isolated
areas are inevitable.
In stationary populations, genetic structure was observed and the expected four clusters
belong to four subspecies was illustrated. However, when migratory colonies were
participated in the analysis, no genetic structure could be observed. Further studies on this
issue about loss of population structure are needed with higher sample sizes from different
isolated and non-isolated regions of Turkey including neighbouring regions such as Iran
and Bulgaria.


Precautions such as wider isolated areas and establishing selection programs
should be formed in order to conserve Kırklareli, Anatolian and Hatay populations,
immediately.
Sales of commercial queens or colonies which are non-native races should be
strictly forbidden to prevent the loss of genetic diversity and homogenization.
45

Instead of this, developing desired features on local honey bees by breeding
practices should be encouraged.
Researches in order to reveal the loss of genetic structure and diversity should be
more supported. If it is possible, different methods to recover the loss should be
developed.
Results of the study are important as being a base for comparison between stationary
and migratory colonies from future honey bee generations in Turkey.
46
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APPENDIX
A. SOLUTIONS
TE (Tris – EDTA Buffer)
Tris
10 mM
EDTA
1 mM
H2 O
100 ml
(pH 8.0)
10X TBE (Tris – Borate – EDTA) Electrophoresis Buffer
per liter
1X Concentratiom
Tris
108 gr
89 mM
Boric Acid
55 gr
89 mM
0.5 m EDTA
40 ml
2 mM
H2O
to 1 liter
(pH 8.0)
56