69
Mitochondrial DNA control region diversity in Common Terns 
Sterna hirundo from Slovenia and Croatia
Raznolikost kontrolne regije mitohondrijske DNK pri navadni čigri Sterna hirundo 
iz Slovenije in Hrvaške
Ida Svetličić1, Jelena Kralj2, Miloš Martinović2, Davorin Tome3, Tilen Basle4, Luka Božić4, 
Iztok Škornik5, Luka Jurinović6, Ana Galov1*
1  University of Zagreb, Faculty of Science, Department of Biology, Rooseveltov trg 6, Zagreb, Croatia,  
e-mail: ida.svetlicic@biol.pmf.hr, anagalov@biol.pmf.hr
2  Institute of Ornithology, Croatian Academy of Sciences and Arts, Gundulićeva 24, Zagreb, Croatia,  
e-mail: jkralj@hazu.hr, martinovic@hazu.hr
3  National Institute of Biology, Večna pot 111, Ljubljana, Slovenia, e-mail: davorin.tome@nib.si
4  DOPPS – BirdLife Slovenia, Tržaška cesta 2, Ljubljana, Slovenia, e-mail: tilen.basle@dopps.si,  
luka.bozic@dopps.si
5  Sečovlje Salina Nature Park, SOLINE Pridelava Soli d.o.o., Seča 115, Portorož, Slovenia, e-mail:  
iztok.skornik@soline.si
6  Croatian Veterinary Institute, Poultry Centre, Heinzelova 55, Zagreb, Croatia, e-mail: luka.jurinovic@gmail.com
* corresponding author 
63 Common Tern Sterna hirundo samples from Croatia and Slovenia were 
analysed with respect to their genetic diversity and differentiation. Samples 
originated from two freshwater populations (areas of the rivers Sava and 
Drava) and one coastal population (Sečovlje Salina). The molecular marker 
of choice was 709 bp long fragment of the mitochondrial control region, 
the fastest-evolving part of the mitochondrial genome. 21 haplotypes 
with 12 polymorphic sites were identified. Overall haplotype diversity was 
substantial and estimated at 0.8599, while the overall nucleotide diversity 
was low and estimated at 0.0025. Diversity indices were highest for the 
Drava population, followed by the Sava and the lowest for the Sečovlje 
population. Overall genetic structure was significantly low (Fst=0.0377) 
and attributed to the differences in haplotype frequencies between the 
populations. The high level of genetic diversity found in continental 
populations illustrates the importance of their habitats as reservoirs of 
genetic diversity and calls for their further protection and management.
Key words: mtDNA, freshwater colonies, population genetics, Laridae, 
seabird
Ključne besede: mtDNA, celinske kolonije, populacijska genetika, 
Laridae, morske ptice
Acrocephalus 40 (180/181): 69–78, 2019
10.1515/acro-2019-0004
1. Introduction
Common Tern Sterna hirundo is a coloni-
al seabird of the family Laridae that breeds both 
on freshwater and marine habitats of temperate 
and subarctic regions of Europe, Asia and North 
America. It is a long-distance migrator, winter-
ing in tropical and subtropical coastal regions. 
Despite being listed as a species of least concern 
by the IUCN, the global population trend is 
70
mostly unknown on account of variation in 
trends between different populations (Birdlife 
International  2018). Freshwater colonies are 
located mostly on river and lake islands. They are 
under a strong negative influence of anthropo-
genic activities, mainly river regulation, as well as 
under natural threats (e.g. competition with gulls 
and mammalian predation), which has led to a sig-
nificant global reduction of the natural freshwa-
ter breeding sites. Freshwater populations of the 
Common Tern are now almost entirely dependent 
on artificial sites, specifically rafts and gravel islands 
(Becker & Ludwigs 2004). Freshwater breeding 
sites in Slovenia and Croatia can be found along 
the river Sava where the most important breeding 
site is Rakitje (Martinović et al. 2019) and in 
the river Drava area where the main colony inhab-
its the Ptuj reservoir. Many small coastal colonies 
are located along the Adriatic Sea, while one of the 
largest is located in Sečovlje Salina Nature Park in 
the Northern Adriatic (Denac et al. 2019).
Despite ongoing research, dynamics of both 
freshwater and coastal populations in Croatia and 
Slovenia are still relatively unclear. High breed-
ing site fidelity, a well-known characteristic of the 
Common Tern (Austin 1949) and many other 
seabird species (Palestis 2014, Coulson 2016), 
can favour population differentiation by restricting 
gene flow (Friesen et al. 2007). Therefore, popu-
lation substructuring in long-distance migratory 
seabirds is seemingly driven by behaviour rather 
than by physical land barriers (Friesen et al. 2007, 
Szczys et al. 2017A).
Genetic studies on wild animal populations are 
mostly performed using neutral genetic markers such 
as mitochondrial DNA control region and microsat-
ellites, which are suitable for studies of genetic diver-
sity, genetic structure and demographic processes of 
populations. Genetic diversity estimation constitutes 
the basis of many contemporary conservation efforts 
because it is crucial for the evolutionary potential 
of species and therefore one of the keys for its long-
term survival (Van Dyke 2008). It can thus serve as a 
measure of the species capacity for future adaptation 
to environmental challenges.
The control region is the fastest evolving part 
of the mitochondrial genome, traditionally used 
as a marker in population genetic and phylogeo-
graphic studies. One of the reasons for the ubiqui-
ty of the mitochondrial DNA (mtDNA) control 
region genetic analysis is its exceptionally high 
polymorphism coupled with conserved arrange-
ment of neighbour genes, which allows for con-
sistent amplification by polymerase chain reaction 
with many primers. Extreme polymorphism aids 
in identification of interpopulation as well as in-
trapopulation lineages. Furthermore, an important 
property of mtDNA is the uniparental inheritance 
which results in a lack of recombination. Individ-
ual lineages can thus be traced through time in a 
more straightforward manner than with nuclear 
markers. Moreover, mtDNA is more sensitive to de-
mographic changes such as bottlenecks and popula-
tion expansion due to its haploid nature (Freeland 
et al. 2011). Despite downsides to mtDNA analyses 
(Hurst & Jiggins 2005), mitochondrial DNA has 
remained the marker of choice in many population 
and phylogeographic studies.
One of the first population genetic studies of 
Common Terns was undertaken on populations 
from North America, using isoelectric focusing of 
blood proteins. High levels of gene flow were sug-
gested among four colonies as no genetic differen-
tiation was found (Burson III 1990). It was fol-
lowed by an investigation of Lithuanian colonies 
using allozymes where some genetic differentiation 
was reported (Sruoga et al. 2002). Further in-
vestigations were performed using nuclear genetic 
markers, specifically microsatellite loci, where sig-
nificant differentiation among sampled locations 
was found in Lithuania (Sruoga et al. 2006) and 
in the North Atlantic region (Szczys et al. 2012). 
A  later study by Szczys et al. (2017B) on cy-
tochrome B revealed hierarchical metapopulation 
structure with the presence of small- and large-scale 
connectivity between metapopulations.
To our knowledge, there are no published pop-
ulation genetic studies of Common Tern inferred 
from an analysis of the mitochondrial DNA control 
region marker. Thus, in this paper we provide the 
first assessment of mtDNA control region diversi-
ty of Common Terns in general. The goal of this 
study was to analyse a 709 bp long fragment of the 
mitochondrial control region in two continental, 
freshwater populations of Common Terns from 
Slovenia and Croatia and in a coastal population 
from Slovenia (Sečovlje Salina). Considering the 
local and global threats affecting freshwater tern 
I. Svetličić et al.: Mitochondrial DNA control region diversity in Common Terns Sterna hirundo from Slovenia and Croatia
71
habitats, this study aimed to quantify the levels of 
genetic diversity in populations of Common Terns 
from the three geographic areas as well as to detect 
a possible phylogeographic structure. Furthermore, 
the intention was to gain insight into possible ex-
pansions or bottlenecks in their population history.
2. Methods
We caught 63 Common Terns during their 
breeding period in the spring and summer of 2017 
(6 individuals), 2018 (54 individuals) and 2019 
(3 individuals). Sampling took place in gravel pits 
near the river Sava (30 individuals), the river Drava 
(22 individuals from Ptuj accumulation and 2 
individuals further downstream) and in Sečovlje 
Salina (9 individuals) (Figure 1). Around 50 µL of 
blood was taken from the brachial vein and stored 
either in EDTA coated tubes on ice or on bloodstain 
storage cards (Nucleocard, Machery Nagel), while 
feathers were taken from some individuals and kept 
in paper envelopes at room temperature. 
DNA was extracted using DNeasy Blood and 
Tissue kit (Quiagen, Hilden Germany) follow-
ing the manufacturer's protocol. To amplify ~800 
bp long fragment of the control region, we de-
signed primers SH-mtCR4F (AACACCCATC-
CAACTCGGAA) and SH-mtCR4R (AAT-
TTCACTGTCGTTGACGTGT) using the 
mitogenome sequence from GeneBank (Yang et al. 
2017). PCR conditions were as follows: 3 min de-
naturation at 95°C, followed by 40 cycles of 30s at 
95°C, 45s of annealing at 52°C, and 45s of elonga-
tion at 72°C. Final elongation period was 10 min at 
72°C. Upon PCR amplification, the samples were 
sent for purification and sequencing to Macrogen 
Europe, Amsterdam, the Netherlands. Sequences 
were aligned, inspected and adjusted to the length 
of 709 bp using Applied Biosystems SeqScape soft-
ware. Three samples from the river Sava area were 
excluded from further analysis as their electro-
pherograms showed unresolved peak duplications.
Mega X (Kumar et al. 2018) was used to 
determine the best nucleotide substitution model 
according to the Bayesian Information Criterion 
(BIC). The selected model with the lowest BIC 
score was Kimura 2-parameter with Gamma 
distributed rates (Nei  & Kimura 2000).Arlequin 
(Excoffier  & Lischer 2010) was used to 
determine the number of haplotypes (H), assign 
haplotypes to the individuals, determine the number 
of variable positions, nucleotide composition, and 
for calculation of haplotype diversity (Hd)  – the 
probability that two randomly chosen sequences are 
different. The mean number of pairwise differences 
and nucleotide diversity (π)  – the probability that 
the two homologous nucleotides differentiate – were 
calculated with Arlequin as well. AMOVA (Analysis 
of Molecular Variance) was conducted in order to 
investigate spatial genetic structuring by analyzing 
covariance components utilizing information on 
haplotype frequencies and nucleotide distance 
between haplotypes (Excoffier et al. 1992), as 
implemented in Arlequin. Population groups were 
defined as ‘’Sava’’, ‘’Drava’’ and ‘’Sečovlje’’. Pairwise 
FST and ΦST values for each group were calculated 
in Arlequin as well. Significance was tested using 
10,000 non-parametric permutation procedures. 
DnaSp (Rozas et al. 2017) was used to calcu-
late Fu’s Fs (Fu 1997) and Tajima’s D (Tajima 1989) 
neutrality tests in order to detect past demographic 
changes. Fu’s Fs is based on the deviation of hap-
lotype frequencies in comparison with expectation 
under population stability, while Tajima’s D calcu-
lates the difference between Theta estimation from 
the number of polymorphic sites and the Theta esti-
mation from the average number of pairwise differ-
ences. Tajima’s D value departs from neutrality to 
negative values in cases of demographic expansion 
or purifying selection. The significance of both Fu’s 
Fs and Tajima’s D was tested using a coalescent tool 
with 10,000 replicates. 
PopART (Population Analysis with Reticu-
late Trees) (Leigh  & Bryant 2015) was used for 
calculating and visualizating the median-joining 
haplotype network. Network methods are gener-
ally more fitting than tree algorithms in studies 
of intraspecific genetic diversity as they allow for 
the presence of ancestral haplotypes in a sample 
(Bandelt et al. 1999, Zachos et al. 2010).
3. Results 
Analysis of the 709 bp fragment of mtDNA control 
region in 60 Common Tern individuals revealed 21 
haplotypes (GenBank accession no. MN337406  - 
MN337426) with 12 segregating sites (Table 1). 
All of the observed substitutions were transitions, 
Acrocephalus 40 (180/181): 69–78, 2019
72
while no deletions or insertions were observed. 
The most common haplotypes were Stehi01 and 
Stehi03 found in all groups of samples. Stehi03 was 
prevalent, being found in 35% of samples (Table 2). 
Low-frequency haplotypes were numerous as 12 
haplotypes were found in only one individual 
each (six unique haplotypes from the Drava, five 
from the Sava and one from Sečovlje Salina). Seven 
haplotypes were shared between samples from 
the Sava and Drava (Stehi01, Stehi02, Stehi03, 
Stehi04, Stehi05, Stehi07 and Stehi12), while only 
two haplotypes (Stehi01 and Stehi03) were shared 
among all three populations. Haplotypes differed 
by one to four base substitutions (Figure 2), while 
mean number of pairwise differences in haplotype 
sequences of different groups ranged from 0.7044 
to 2.1085 (Table 3). 
Overall haplotype diversity was estimated at 
0.8599, and nucleotide diversity at 0.0025. All di-
versity indices were highest for the river Drava pop-
ulation. Haplotype diversity for the river Drava was 
estimated at 0.9239, for the river Sava at 0.8946, 
while it was the lowest for Sečovlje Salina (0.4167). 
Nucleotide diversity estimates were found to be in 
the same order, as well as the mean number of pair-
wise distances (the highest values for the Drava, fol-
lowed by the Sava, and the lowest values found in 
Sečovlje Salina) (Table 3). 
Table 1: Segregating sites (S) on the mtDNA control region fragment (709 bp) of S. hirundo
Tabela 1: Ločitvena mesta (S) na mtDNA fragmentu kontrolne regije (709 bp) pri navadni čigri
 
S1  
(5)
S2 
(18)
S3 
(42)
S4 
(75)
S5 
(92)
S6 
(95)
S7 
(98)
S8 
(129)
S9 
(269)
S10 
(282)
S11 
(438)
S12 
(473)
Stehi01 A T C A C C A G G A A C
Stehi02 G T C A C T A G G A A C
Stehi03 A T C A C T A G G A A C
Stehi04 A T C A C C A A G A A C
Stehi05 G T C A C C A G G A A C
Stehi06 A T C A T T A G G A A C
Stehi07 A T C A C T A G G A G C
Stehi08 A C C A C T A A G A A C
Stehi09 A T C A C C A G A A A C
Stehi10 G T C A C T A G G A A T
Stehi11 A T C A C T A G G A A T
Stehi12 A T C A C T A A G A A C
Stehi13 A T C G T T A G G A A C
Stehi14 A T C A C C A G G A A C
Stehi15 A T T A C T A A G A A C
Stehi16 G T C A C T A A G A A C
Stehi17 A T C A T C A A G A A C
Stehi18 A T C A C T G A G A A C
Stehi19 A T C A C T A A G G A C
Stehi20 G T C A T T A A G A A C
Stehi21 A T T A T T A G G A A C
I. Svetličić et al.: Mitochondrial DNA control region diversity in Common Terns Sterna hirundo from Slovenia and Croatia
73
Table 2: Distribution and frequency (%) of 21 S. hirundo mtDNA control region haplotypes
Tabela 2: Razporeditev in frekvenca (%) 21 haplotipov mtDNA kontrolne regije pri navadni čigri
Haplotype haplotip Sava (N=27) Drava (N=24) Sečovlje Salina (N=9) Total (N=60)
Stehi01 3 (5.00) 2 (3.33) 1 (1.67) 6 (10.00)
Stehi02 3 (5.00) 2 (3.33) 0 4 (6.67)
Stehi03 8 (13.33) 7 (10.00) 6 (11.67) 21 (35.00)
Stehi04 2 (3.33) 1 (1.67) 0 3 (5.00)
Stehi05 1 (1.67) 3 (5.00) 0 4 (6.67)
Stehi06 2 (3.33) 0 0 2 (3.33)
Stehi07 2 (3.33) 1 (1.67) 0 3 (5.00)
Stehi08 1 (1.67) 0 0 1 (1.67)
Stehi09 1 (1.67) 0 0 1 (1.67)
Stehi10 1 (1.67) 0 0 1 (1.67)
Stehi11 1 (1.67) 0 0 1 (1.67)
Stehi12 1 (1.67) 2 (3.33) 0 3 (5.00)
Stehi13 0 1 (1.67) 0 1 (1.67)
Stehi14 0 1 (1.67) 0 1 (1.67)
Stehi15 0 1 (1.67) 0 1 (1.67)
Stehi16 0 1 (1.67) 0 1 (1.67)
Stehi17 0 1 (1.67) 0 1 (1.67)
Stehi18 0 0 1 (1.67) 1 (1.67)
Stehi19 1 (1.67) 0 0 1 (1.67)
Stehi20 0 2 (3.33) 0 1 (1.67)
Stehi21 0 2 (3.33) 0 1 (1.67)
Table 3: Genetic diversity indices and neutrality test results for 709 bp fragment of mtDNA control region in 
S. hirundo. Statistically significant values (p < 0.02 for Fs, p < 0.05 for D) are marked in bold. N – number of 
individuals, H – number of haplotypes, Hd – haplotype diversity, π – nucleotide diversity, k – mean number of 
pairwise differences, S – segregating sites, D – Tajima's D, Fs – Fu's Fs.
Table 3: Indeks genetske raznovrstnosti in test nevtralnosti za 709 bp dolge fragmente mtDNA kontrolne regije 
pri navadni čigri. Statistično značilne vrednosti (p < 0.02 za Fs, p < 0.05 za D) so v krepkem tisku. N – število 
osebkov, H – število haplotipov, Hd –haplotipska raznovrstnost, π – nukleotidska ranovrstnost, k – povprečno število 
parnih razlik, S – predeli ločevanja, D – Tajima's D, Fs – Fu's Fs.
Population
populacija N H Hd Π k S  D Fs 
Sava 27 13 0.8946 0.0025 1.7920 9 -0.8998 -8.8476
Drava 24 14 0.9239 0.0030 2.1085 6 0.1367 -10.2569
Sečovlje Salina 9 3 0.4167 0.0010 0.7044 3 -1.5130 -0.3802
Total 60 21 0.8599 0.0025 1.7721 12 -1.0263 -17.8652
Acrocephalus 40 (180/181): 69–78, 2019
74
At least one neutrality test for each group of 
samples suggested historical expansion. Large neg-
ative and significant values of Fu’s Fs were detected 
in sample sets from the Drava and the Sava and in 
the overall data set. Tajima’s D was significant ex-
clusively in the samples from Sečovlje Salina, where 
Fu’s Fs test was not significant (Table 3).
Haplotypes were dispersed without evident 
spatial association (Figure 2). Within population 
measure inferred by AMOVA accounted for 98.81% 
of variance. Global ΦST based on molecular dis-
tance was low (0.0041) and insignificant, as well 
as pairwise ΦST values. However, global fixation 
index FST based on F-statistics (haplotype frequen-
cies) was low (0.0377) but significant (p = 0.0389), 
as well as the pairwise FST values that were estimat-
ed at 0.09631 and 0.11969 when Sečovlje Salina was 
compared against the Sava and the Drava respective-
ly (Table 4).
4. Discussion
In this paper we report results of the first assessment 
of Common Tern mtDNA control region diversity. 
The sample set consisted of individuals breeding in 
Slovenia and Croatia, with the majority of samples 
originating from continental, freshwater colonies.
During the conduction of this study, double 
electropherogram peaks were noticed in three 
samples (data not shown), indicating possible 
Table 4: Pairwise φST (above the diagonal) and FST 
values (below the diagonal) for the continental groups of 
S. hirundo populations. Statistically significant values (p 
< 0.05) are marked in bold.
Tabela 4: Parne φST (nad diagonalo) in FST vrednosti 
(pod diagonalo) za populacije navadne čigre. Statistično 
značilne vrednosti so v krepkem tekstu.
  Sava Drava
Sečovlje 
Salina
Sava - -0.00561 -0.01439
Drava -0.01230 - 0.05137
Sečovlje 
Salina 0.09631 0.11969 -
Figure 1: Sampling locations for Common Tern colonies in Croatia and Slovenia
Slika 1: Mesta vzorčenja kolonij navadne čigre na Hrvaškem in v Sloveniji
I. Svetličić et al.: Mitochondrial DNA control region diversity in Common Terns Sterna hirundo from Slovenia and Croatia
75
control region duplication, and therefore those 
samples were excluded from further analysis. Am-
biguities in the control region are regularly report-
ed in seabird species (Friesen et al. 2007, Skujina 
et al. 2016) and for that reason, cytochrome B 
is usually the preferred choice of mitochondri-
al marker for population genetic studies in Terns 
(Faria et al. 2010, Miller et al. 2013, Boutillier 
et al. 2014; Szczys et al. 2017A, Szczys et al. 
2017B). However, the control region is much more 
variable and is therefore more suitable for studies 
of genetic diversity at the population level. In our 
study, possible duplication of the control region 
occurred in low frequency (4.8%) and thus would 
have had a minor impact on the results. Further 
research will enable a more precise estimation of 
mtDNA control region duplication prevalence 
in Common Tern populations and should aim to 
resolve them.
Our analyses of samples of Common Terns 
from two freshwater habitats – rivers Sava and 
Drava, and from the coastal colony of Sečovl-
je Salina, revealed a substantial level of haplotype 
diversity as a large number of haplotypes was de-
tected, specifically 21. This is particularly notable 
as only 60 samples were analysed in this study. It 
Figure 2: Median-joining haplotype network generated by the program Pop Art based on the mtDNA control region 
haplotypes of S. hirundo. Size of the circles is proportional to the frequency of the haplotype. Different shades 
represent sample groups Sava, Drava and Sečovlje Salina. The number of hatch marks indicates the number of 
mutational differences between haplotypes.
Slika 2: Haplotipska mreža mtDNA kontrolnih regij navadne čigre, narejena s programom Pop Art. Velikost kroga je 
proporcionalna frekvenci haplotipa. Različne sivine ponazarjajo skupine s Save, Drave in iz Sečoveljskih solin. Število 
oznak ponazarja število mutacijskih razlik med haplotipi. 
Acrocephalus 40 (180/181): 69–78, 2019
76
is somewhat higher than 18 haplotypes found in 
89 individuals of Sooty Tern (Sterna fuscata) from 
Eastern Australia and the Coral Sea (Peck  & 
Congdon 2004), whereas it is comparable to 67 
mtDNA control region haplotypes found in 188 
individuals of Least Tern Sternula antillarum from 
North America (Draheim et al. 2010).
Haplotype diversity value of 0.8599 found in 
this study is substantially high and comparable to 
the Hd values found in Least Terns with Hd values 
ranging from 0.76 to 0.96 (Draheim et al. 2010). 
It is also similar to Hd values of cytochrome B 
mitochondrial marker found in Gull-billed Terns 
Gelochelidon nilotica (Hd values ranged from 0.843 
to 0.908) (Miller et al. 2013). Moreover, haplotype 
diversity found in this study is higher than Hd 
values of cytochrome-b sequence found for Common 
Tern populations from northeastern United States 
and southern Canada, which ranged from 0.21 to 
0.77 (Szczys et al. 2017B) and those reported for 
Eurasian Black Tern Chlidonias niger populations, 
which ranged from 0.33 to 0.82 (Szczys et al. 
2017a). Higher Hd values found in this study come 
as no surprise as they are expected for more variable 
markers, such as the control region.
In contrast with high haplotype diversity 
values, nucleotide diversity values are very low, both 
in the overall sample (0.0025) as well as in the indi-
vidual groups (0.0010–0.0030) (Table 3). They are 
lower than Π values previously reported for Least 
Terns (ranging from 0.0010 to 0.0069) (Draheim 
et al. 2010), but still higher than those reported 
for Sooty Terns (Peck & Congdon 2004), where 
the Π values ranged from 0.0007 to 0.0016. Mean 
number of pairwise differences between sequences 
was also low, reflecting small number of mutations 
between different haplotypes. 
Samples from the colonies on the rivers Sava 
and Drava share more haplotypes (seven) than 
either of those sets shares with samples from Sečov-
lje Salina (two out of three haplotypes found in 
Sečovlje Salina are shared among all three groups 
and the third haplotype is not shared with any 
other population) (Table 2). One explanation can 
be insufficient sampling in Sečovlje Salina, as only 
nine individuals were sampled there. On the other 
hand, the finding could be explained by potential-
ly higher movements of individuals between fresh-
water habitats of the Sava and Drava which have 
more similar ecological conditions than between 
any freshwater habitat and Sečovlje Salina, as it 
represents a dissimilar coastal habitat. Geographi-
cal distance could also be a reason for the observed 
pattern; the Sava and Drava are much closer to each 
other than the coastal colony of Sečovlje Salina and 
dispersal rates are expected to be higher in adjacent 
colonies. Similar results were obtained with tests of 
genetic differentiation, as pairwise FST values were 
statistically significant only for coastal-freshwater 
population pairs. Admittedly, genetic differentia-
tion was weak (0.11969 for Sečovlje Salina – Drava 
and 0.09631 for Sečovlje Salina – Sava) and ob-
served only in measures that utilize solely F–statis-
tic based on haplotype frequencies, namely global 
and pairwise FST (Table 4). Again, possible dif-
ferentiation between the coastal colony of Sečov-
lje Salina and both freshwater colonies could be 
due to contrasting ecological factors of freshwater 
and coastal habitats or to small sample size of the 
Sečovlje Salina population. However, measures de-
pendent on nucleotide distance (global and pair-
wise ΦST) did not show differentiation between 
either any pair of population groups, or in the 
overall data set (Table 4). Very weak population 
differentiation, or its absence, observed using mi-
tochondrial cytochrome B markers, was previous-
ly noted for Common Terns (Szczys et al. 2017b) 
and Black Terns (Szczys et al. 2017a), while those 
studies demonstrated some level of differentiation 
using nuclear markers. The opposite pattern was 
also documented, as Miller et al. (2013) detect-
ed significant genetic structure by mtDNA analysis 
of Gull-billed Terns, but did not demonstrate dif-
ferentiation using microsatellites. Since the results 
might be highly dependent on the choice of marker, 
further research on nuclear markers such as micro-
satellites could offer additional insights into popu-
lation structure. In general, mitochondrial markers 
reveal female ancestry while nuclear markers reflect 
biparentally inherited diversity. Moreover, mito-
chondrial DNA reflects historical processes, while 
microsatellites are more informative of contempo-
rary population genetics processes. Therefore, the 
research on Common Terns that breed in Croatia 
and Slovenia should be expanded to microsatellite 
analysis which could offer broader insight into the 
demographic structure and the genetic variability 
of the populations. 
I. Svetličić et al.: Mitochondrial DNA control region diversity in Common Terns Sterna hirundo from Slovenia and Croatia
77
The finding of numerous low frequency and 
private haplotypes (Table 2) indicates the popula-
tions are not homogenous. High haplotype diversi-
ty, low nucleotide diversity, and numerous low fre-
quency haplotypes that differ by a small number of 
mutations can be explained as a signature of pop-
ulation expansion or indication of recent origin 
(Peck & Congdon 2004; Faria et al. 2010). Neu-
trality tests of Fu’s Fs indicate possible population 
expansion in the overall sample set and individually 
in the samples from the Sava and Drava areas (Table 
3). Fu’s Fs test has a great statistical power to detect 
excess of low frequency alleles expected in the ex-
panding population (Ramos-Onsins  & Rozas, 
2002). Tajima’s D implied possible expansion for 
the Sečovlje Salina group as well. These findings 
might reflect expansion from glacial refugia during 
the last ice age (Oomen et al 2011, Szczys 2017 b) 
and they are consistent with other published data 
on terns (Peck  & Congdon 2004, Faria et al. 
2010, Szczys et al. 2017 a, Szczys et al. 2017 b). 
Our findings on mtDNA control region di-
versity indicate that genetic diversity of Common 
Tern populations from Slovenia and Croatia is sub-
stantially high. Samples from the river Drava ex-
hibited the highest values of haplotype and nucleo-
tide diversity, closely followed by samples from the 
river Sava area, which illustrates the importance of 
these habitats as reservoirs of genetic diversity and 
specifically underscores the value of maintaining 
artificial sites for the long-term conservation of 
Common Tern populations in freshwater habitats.
Acknowledgements: Funding was provided by 
Cooperation Programme Interreg V-A Slovenia  – 
Croatia (grant SLO-HR347).
5. Povzetek
Avtorji prispevka so analizirali genetsko raznolikost 
in diferenciacijo 63 primerkov navadne čigre Sterna 
hirundo iz Slovenije in Hrvaške. Vzorce so pridobili 
iz dveh sladkovodnih (območji rek Save in Drave) 
in ene obalne populacije (Sečoveljske soline). Izbrali 
so 709 baznih parov dolg molekularni marker, 
fragment mitohondrijske kontrolne regije, ki je del 
mitohondrijskega genoma, izpostavljen najhitrejšim 
evolucijskim spremembam. Odkrili so 21 haplotipov 
z 12 polimorfičnimi mesti. Skupna haplotipska 
raznolikost je bila visoka (0,8599), medtem ko je bila 
nukleotidska raznolikost nizka (0,0025). Indeksi 
diverzitete so bili najvišji pri dravski, nato pri 
savski in najnižji pri sečoveljski populaciji. Skupna 
genetska strukturiranost, pripisana razlikam v 
pogostosti haplotipov med populacijami, je bila 
nizka (Fst=0,0377) in statistično značilna. Visoka 
genetska pestrost v celinskih populacijah navadne 
čigre prikazuje vrednost njihovih habitatov kot 
rezervoarjev genetske pestrosti in opozarja na 
pomembnost nadaljnjega ohranjanja in upravljanja 
takih habitatov.
6. References 
Austin O. L. (1949): Site tenacity, a behavior trait of the 
common tern (Sterna hirundo Linn.). – Bird banding 
20: 1–39 
Bandelt H. J., Forster P.  & Rohl A. (1994): Median-
Joining Networks for Inferring Intraspecific Phylogenies. 
- Molecular Biology & Evolution 16 (1): 37–48.
Becker P. H.  & Ludwigs J.D. (2017): Common Tern 
(Sterna hirundo). - The Birds of North America. 
BWP Update 6: 9–137.
Birdlife International  2018.  Sterna hirundo.  The 
IUCN Red List of Threatened Species 2018. http://
dx.doi.org/10.2305/IUCN.UK.2018–2.R LTS.
T22694623A132562687.en, 27/8/2019 
Boutilier S. T., Taylor S. A., Morris-Pocock J. 
A., Lavoie R. A., Friesen V. L. (2014): Evidence 
for genetic differentiation among Caspian tern 
(Hydroprogne caspia) populations in North America. 
- Conservation Genetics 15 (2): 275–281.
Burson Iii S. L. (1990): Population Genetics and 
Gene Flow of the Common Tern. The Condor 92: 
182–192.
Coulson, J. C. (2016): A Review of Philopatry in 
Seabirds and Comparisons with Other Waterbird 
Species. - Waterbirds 39 (3): 229–240.
Dayton J., Ledwoń M., Paillisson J.-M., Atamas 
N., Szczys P. (2017): Genetic Diversity and Popu-
lation Structure of the Eurasian Whiskered Tern 
(Chlidonias hybrida ), a Species Exhibiting Range 
Expansion . - Waterbirds 40 (2): 105–117.
Denac, D., Škornik, I., Božič, L., Mozetič, B. (2019): 
Navadna čigra Sterna hirudno. pp. 196–197 in: 
Mihelič, T., Kmecl, P., Denac, K., Koce, U., Vrezec, 
A., Denac, D.: Atlas ptic Slovenije. Popis gnezdilk 
2002–2017. DOPPS, Ljubljana. 
Draheim H. M., Miller M. P., Baird P., Haig S. M. 
(2010): Subspecific Status and Population Genetic 
Structure of Least Terns (Sternula antillarum) Inferred 
by Mitochondrial DNA Control-Region Sequences 
and Microsatellite DNA . - The Auk 127 (4): 807–819.
Acrocephalus 40 (180/181): 69–78, 2019
78
Excoffier L., Lischer H. E. (2010): Arlequin suite ver 
3.5: a new series of programs to perform popula-
tion genetics analyses under Linux and Windows. – 
Molecular Ecology Resources 10 (3): 564–567.
Excoffier L., Smouse P. E., Quattro J. M. (1992): 
Analysis of Molecular Variance Inferred From 
Metric Distances Among DNA Haplotypes. - Appli-
cation 491: 479–491.
Faria P. J., Campos F. P., Branco J. O., Musso C. M., 
Morgante J. S., Bruford M. W. (2010): Popula-
tion structure in the South American tern Sterna 
hirundinacea in the South Atlantic: Two popula-
tions with distinct breeding phenologies. - Journal of 
Avian Biology 41 (4):378–387.
Freeland J.R., Kirk H., Petersen S.D. (2011): Molecular 
Ecology. -2nd ed. –Wiley-Blackwell, Oxford.
Friesen V. L., Burg T. M., Mccoy K. D. (2007): Mech-
anisms of population differentiation in seabirds. - 
Molecular Ecology 16: 1765–1785.
Fu Y. X. (1997): Statistical tests of neutrality of mutations 
against population growth, hitchhiking and back-
ground selection. – Genetics 147 (2) 915–925.
Hurst, G., Jiggins, F. (2005): Problems with mito-
chondrial DNA as a marker in population, phylo-
geographic and phylogenetic studies: The effects of 
inherited symbionts. -Biological sciences / The Royal 
Society 272: 1525–34. 
Kumar S., Stecher G., Li M., Knyaz, C., Tamura K. 
(2018): MEGA X : Molecular Evolutionary Genetics 
Analysis across Computing Platforms.  – Molecular 
Biology & Evolution 35 (6): 1547–1549.
Leigh J. W., Bryant D. (2015): POPART : full-fea-
ture software for haplotype network construction. 
- Methods in Ecology and Evolution 6: 1110–1116.
Martinović M., Galov, A., Svetličić I., Tome D., 
Jurinović L., Ječmenica B., Basle T., Božič L., 
Kralj J. (2019): Prospecting of breeding adult 
Common terns in an unstable environment. - 
Ethology Ecology & Evolution 31 (5): 457–468.
Miller M. P., Mullins T. D., Haig S. M. (2013): Genetic 
Structure, Diversity and Subspecies Status of Gull-
billed Terns (Gelochelidon nilotica) from the United 
States. - Waterbirds 36 (3): 310–318.
Nei M., Kumar S. (2000): Molecular Evolution and 
Phylogenetics. -1st ed. - Oxford University Press, 
New York.Oomen R. A. O., Eudink M. W. R., 
Ocera J. J. N., Omers C. M. S., Reen M. C. G., Yle 
C. J. K. (2011): Mitochondrial Evidence for Panmixia 
despite Perceived Barriers to Gene Flow in a Widely 
Distributed Waterbird. - Journal of Heredity 102 (5): 
584–592.
Palestis B. G. (2014): The role of behavior in tern 
conservation. - Current Zoology 60:500–514.
Peck D. R., Congdon B. C. (2004): Reconciling historical 
processes and population structure in the sooty tern 
Sterna fuscata. - Journal of Avian Biology 35 (4): 327–335.
Ramos-Onsins S. E., Rozas J. (2002): Statistical 
Properties of New Neutrality Tests Against Population 
Growth. - Molecular Biology  & Evolution 19 (12): 
2092–2100.
Rozas J., Ferrer-Mata A., Sanchez J. C., 
Guirao-Rico S., Librado P., Ramos-Onsins S. E., 
Sanchez-Gracia (2017): DnaSP 6 : DNA Sequence 
Polymorphism Analysis of Large Data Sets. - Molecular 
Biology & Evolution 34 (12): 3299–3302.
Skujina I., Mcmahon R., Lenis V. P. E., Gkoutos G. V., 
Hegarty M. (2016): Duplication of the mitochondrial 
control region is associated with increased longevity in 
birds. - Aging 8. (8): 1781–1788.
Sruoga A., Volkovaitė V., Butkauskas D., 
Raudonikis L., Sorokaitė J., Tubelytė V. (2002): 
Genetic differentiation of Common Tern (Sterna 
hirundo) colonies. - Biologija 4: 21–24.
Szczys P., Nisbet I. C. T., Wingate D. B. (2012): 
Conservation genetics of the Common Tern (Sterna 
hirundo) in the North Atlantic region; implications 
for the critically endangered population at Bermuda. - 
Conservation Genetics 13 (4): 1039–1043.
Szczys P., Lamothe K. A., Druzyaka A., Poot M. J. M., 
Siokhin V., Van Der Winden J. (2017a): Range-wide 
patterns of population differentiation of Eurasian Black 
Terns (Chlidonias niger niger) related to use of discrete 
post-nuptial staging sites. - Journal of Ornithology 158 
(2): 365–378.
Szczys P., Oswald S. A., Arnold ,J. M. (2017b): 
Conservation implications of long-distance 
migration routes: Regional metapopulation structure, 
asymmetrical dispersal, and population declines. - 
Biological Conservation 209: 263–272.
Tajima F. (1989): Statistical Method for Testing the 
Neutral Mutation Hypothesis by DNA Polymorphism. 
- Genetics 123: 585–595.
Van Dyke F. (2008): Genetic Diversity - Understanding 
Conservation at Genetic Levels. pp. 153–184. In: 
Conservation Biology. - Springer, Dordrecht.
Yang C., Wang Q. X., Li, X. J., Yuan H., Xiao H., Huang 
Y. (2017): The mitogenomes of Gelochelidon nilotica 
and Sterna hirundo (Charadriiformes, Sternidae) and 
their phylogenetic implications. - Mitochondrial DNA 
Part B: Resources 2 (2): 601–603.
Zachos F. E., Slimen H. Ben, Giacometti M., 
Suchentrunk F. (2010): Regional genetic in situ 
differentiation despite phylogenetic heterogeneity in 
Alpine mountain hares. – Journal of Zoology 282: 47–53.
Prispelo / Arrived: 3.10.2019
Sprejeto / Accepted: 22.12.2019
I. Svetličić et al.: Mitochondrial DNA control region diversity in Common Terns Sterna hirundo from Slovenia and Croatia