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Population and subspecies differentiation in a high latitude
breeding wader, the Common Ringed Plover
Charadrius hiaticula
Leon Thies1, Pavel Tomkovich2, Natalie dos Remedios3, Terje Lislevand4,
Pavel Pinchuk5, Johan Wallander6, Juliana Dänhardt7, Böðvar Þórisson8,
Donald Blomqvist9,* & Clemens Küpper1,10,*
Thies L., Tomkovich P., dos Remedios N., Lislevand T., Pinchuk P., Wallander J.,
Dänhardt J., Þórisson B., Blomqvist D. & Küpper C. 2018. Population and sub -
species differentiation in a high latitude breeding wader, the Common Ringed
Plover Charadrius hiaticula. Ardea 106: 163–176. doi:10.5253/arde.v106i2.a8
Exploring the patterns of genetic structure in the context of geographical and
phenotypic variation is important to understand the evolutionary processes
involved in speciation. We investigated population and subspecies differentiation
in the Common Ringed Plover Charadrius hiaticula, a high latitude wader that
breeds in arctic and temperate zones from northeast Canada across Eurasia to
the Russian Far East. Three subspecies, hiaticula, tundrae and psammo -
dromus, are currently widely recognised, whereas a fourth subspecies, koly-
mensis, has been proposed based on geographic isolation and phenotypic
differences. We genotyped 173 samples from eleven Common Ringed Plover
breeding sites, representing all four putative subspecies, at eight polymorphic
microsatellite loci to examine the patterns of population and subspecies differen-
tiation. Bayesian clustering identified three genetic clusters among samples,
corresponding to the breeding sites of the three currently recognised sub -
species. The existence of the subspecies kolymensis was not supported. We
also detected the presence of a previously unknown hybridisation zone
extending from Northern Scandinavia to Belarus. Differentiation of the sub -
species tundrae and hiaticula most likely occurred in allopatry on the Eurasian
continent during past glaciation events, followed by population expansion
leading to colonisation of Iceland and Greenland. The lack of genetic differentia-
tion within the tundrae subspecies is consistent with ongoing range expansion
and high gene flow maintained through migratory behaviour. We discuss the
importance of historic climate changes, migratory behaviour and mating system
on shaping the observed pattern of genetic differentiation.
Key words: subspecies delineation, population differentiation, microsatellites,
Charadrius
1Institute of Zoology, University of Graz, Universitätsplatz 2, 8010 Graz, Austria;
2Zoological Museum, M.V. Lomonosov Moscow State University, Bolshaya
Nikitskaya Str. 6, 125009 Moscow, Russia; 3Department of Animal and Plant
Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, UK;
4University Museum of Bergen, Department of Natural History, University of
Bergen, P.O. Box 7800, N-5020 Bergen, Norway; 5Belarus Bird Ringing Centre,
National Academy of Sciences, Academichnaya Str. 27, 220072 Minsk, Belarus;
6Swedish Board of Agriculture, 551 82 Jönköping, Sweden; 7Centre for Environ -
mental and Climate Research, Lund University, Sölvegatan 37, 223 62 Lund,
Sweden; 8South Iceland Research Centre, University of Iceland, Lindarbraut 4,
840 Laugarvatn, Iceland; 9Department of Biological and Environ mental
Sciences, University of Gothenburg, Box 463, 405 30 Gothenburg, Sweden;
10Max Planck Institute for Ornithology, Eberhard Gwinner Str., 82319
Seewiesen, Germany;
*corresponding authors (ckuepper@orn.mpg.de,
donald.blomqvist@bioenv.gu.se)
Reproductive isolation and genetic differentiation are
important precursors of speciation (Mayr 1942, Coyne
& Orr 2004). Most terrestrial species with large geo -
graphic distributions show moderate to strong genetic
differentiation (Avise 2000). Genetic differentiation
measured by neutral genetic markers usually arises
either because of limited dispersal resulting in isola-
tion-by-distance, or due to spatial barriers interrupting
the geographic distribution of a species (vicariance).
However, a number of widespread terrestrial species
with large continental distributions appear to com -
pletely lack genetic differentiation (Estoup et al. 1996,
Reudink et al. 2011, Küpper et al. 2012).
Across animal taxa, dispersal ability is positively
correlated with gene flow and hence negatively corre-
lated with genetic differentiation (Bohonak 1999). Two
dispersal processes, natal and breeding dispersal
(Greenwood & Harvey 1982), have the largest influence
on gene flow. Of these, natal dispersal is considered
more important since natal dispersal distances are
usually greater than breeding dispersal distances.
Interestingly, in birds both natal and breeding dispersal
are negatively correlated with geographic range but
positively related to migration (Paradis et al. 1998).
Dispersal behaviour is shaped by a suite of life history
characteristics, including social traits such as mating
behaviour (Greenwood 1980, D'Urban Jackson et al.
2017, Kempenaers & Valcu 2017).
Phenotypic differentiation does not necessarily
imply genetic differentiation (Marthinsen et al. 2007,
Rheindt et al. 2011, Woltmann et al. 2014, Hossein et
al. 2017). Examining the coherence of genetic and
phenotypic variation across populations is important to
understand the evolutionary processes involved with
speciation. Generally, incongruence between genotypes
and phenotypes can have two explanations. First,
phenotypically monomorphic taxa may exhibit deep
genetic divergences, for example when a recognised
species harbours several cryptic species (Tolley et al.
2008). Second, morphologically distinct taxa can
appear to lack genetic differences (Woltmann et al.
2014), for example, when the diagnostic differences are
encoded by only a small number of genes.
Genetic data are routinely used to assess the validity
of species and subspecies delineation. Tradition
ally,
subspecies have been used to distinguish populations
with morphological differences (Phillimore & Owens
2006, Winker 2010, Haig et al. 2011). In a number of
species, population genetic data and subspecies delin-
eation are in agreement, although the majority of avian
subspecies have not been subject to genetic evaluation.
Correct taxonomy and delineation of species and popu-
lations is important for conservation policies and
management. Species, subspecies or distinct popula-
tions are often used as the basis of practical manage-
ment and legislation (Höglund 2009, Haig & D’Elia
2010). Examination of genetic diversity can help to
assess the viability of small populations (Blomqvist et
al. 2010) and if necessary inform translocations of
animals to supplement endangered populations.
Waders, or shorebirds, are characterised by high
dispersal abilities as well as large variation between
species and populations in mating systems, migration
and breeding behaviour (Piersma 1987, Piersma &
Lindström 2004, Székely et al. 2007, Thomas et al.
2007). Waders are a challenging group of birds for
taxonomists, with poor resolution especially at the tips
of the wader phylogeny (dos Remedios et al. 2015). In
a number of wader species, genetic variation does not
match phenotypic variation across geographic clines
(Marthinsen et al. 2007, Küpper et al. 2009, Rheindt et
al. 2011). Variation in mating and migratory behaviour
has also been invoked to explain genetic differentiation
and subspecies number in waders (Kraaijeveld 2008,
D'Urban Jackson et al. 2017) although genetic evalua-
tion of subspecies delineation is yet to be conducted for
most wader species.
We studied genetic differentiation and subspecies
delineation in the Common Ringed Plover Charadrius
hiaticula, (Ringed Plover from here on) a high latitu-
dinal monogamous wader (Wallander et al. 2001,
Wiersma et al. 2018). As the ancestral Charadrius
plover (dos Remedios et al. 2015), Ringed Plovers have
an Arctic breeding distribution, stretching from eastern
Canada across the Palearctic to the Russian Far East.
The breeding distribution also extends into temperate
climate zones in Europe (Wiersma et al. 2018) and
Siberia (Lappo et al. 2012). The global population is
estimated at 360,000–1.3 million individuals (Delany
et al. 2006), and Iceland holds the largest European
population (up to 50,000 breeding pairs; Wiersma et al.
2018). Return rates of juveniles to their hatching site is
relatively low, implying high natal dispersal, whereas
adult return rates to breeding sites seem to be variable
(Laven 1940, Wallander & Andersson 2003, Lislevand
et al. 2017, Tomkovich et al. 2017). Western Palearctic
Ringed Plovers show leap-frog migration, with the
northern breeding populations generally wintering
further south than southern populations (Taylor 1980).
It is likely, however, that individuals from different
breeding populations mix during winter, at least in
Iberia and north-western Africa (Thorisson et al. 2012).
Despite its large breeding distribution, the Ringed
Plover shows relatively little morphological variation
ARDEA 106(2), 2018
164
Thies et al.: POPULATION AND SUBSPECIES DIFFERENTIATION IN COMMON RINGED PLOVERS
(Salomonsen 1930, Engelmoer & Roselaar 1998,
Meissner 2007, Meissner et al. 2010, Wiersma et al.
2018). Consequently, the number of distinct subspecies
has been debated, ranging from two to seven
(Engelmoer & Roselaar 1998, Meissner 2007, Lappo et
al. 2012, Wiersma et al. 2018) with most authors
suggesting three subspecies: C. h. hiaticula breeding
from southern Scandinavia and the Baltic south to the
British Isles and north-western France, C. h. tundrae
from northern Scandinavia across northern Russia, and
C. h. psammodromus in northeast Canada, Greenland,
Iceland and Faeroe Islands. The subspecies differ in
morphometrics (particularly wing, secondary and tail
length), moult cycle and plumage colouration of the
upper parts (Engelmoer & Roselaar 1998). The exact
subspecies delineation is contentious, for example
Scandinavian populations have been proposed to be
transition forms between the nominate and tundrae
subspecies (Haftorn 1971). Moreover, the Siberian
distri bution of Ringed Plovers is interrupted between
145°E and 160°E, with only a few scattered breeding
sites found in between (Figure 1). Interestingly, Ringed
Plovers from Chukotka, in the Far East, differ in
morpho metrics and chick plumage from C. h. tundrae
(Engelmoer & Roselaar 1998, Lappo et al. 2012). Chick
plumage is often a valid indicator of species differences
(Jehl 1968, Küpper et al. 2009) and the geographically
isolated populations in Chukotka are tentatively
referred to as subspecies kolymensis (Lappo et al. 2012).
A thorough genetic analysis is required to verify this
taxonomic hypothesis.
Here, we examine population genetic structure in
Ringed Plovers, sampling a substantially larger part of
the breeding distribution than any previous study (cf.
D'Urban Jackson et al. 2017). We analyse genetic varia-
tion within and among eleven breeding populations,
from Iceland in the west to Chukotka in the Russian Far
East. Using data from eight polymorphic microsatellite
markers, we evaluate genetic subspecies delineation
and examine patterns of genetic differentiation in the
Ringed Plover. We discuss factors influencing popula-
tion subdivision and gene flow.
165
Female Common Ringed Plover of the tundrae subspecies (Chukotka, 6 June 2015).
METHODS
We collected blood, tissue or feather samples from
Ringed Plover adults or chicks in eleven sampling areas
across Eurasia (Table 1, Figure 1). Our sampling includ -
ed areas from the three currently recognised sub species,
hiaticula (breeding areas HAL, TUR; area abbreviations
in Table 1), psammodromus (WES) and tundrae (LAP,
VAR, VOR, TAY), and the suggested fourth subspecies
kolymensis (NWC, SEC, ECC, NEC). The smallest
distance between sampling areas was around 230 km
(ECC and SEC) and the maximum distance between
samples within an area was 132 km.
Blood sampling was the main source of DNA, with
the exception of WES, where we obtained feather
samples instead. Adult plovers were caught on their
ARDEA 106(2), 2018
166
Site Country Abbreviation Latitude (°N) Longitude (°E) nH
OHE
Westfjords Iceland WES 66.06 –23.35 42 0.70 0.68
Halland Sweden HAL 57.33 12.06 25 0.64 0.68
Lapland Sweden LAP 68.35 18.49 10 0.47 0.70
Turov Belarus TUR 52.07 27.73 12 0.73 0.71
Varanger Norway VAR 70.36 30.59 12 0.60 0.68
Vorkuta Russia VOR 67.72 63.64 10 0.58 0.70
Taymyr Russia TAY 72.87 105.96 15 0.70 0.66
NW Chukotka Russia NWC 69.72 170.33 2 NA NA
SE Chukotka Russia SEC 62.54 177.00 24 0.69 0.71
E Central Chukotka Russia ECC 64.50 177.92 11 0.65 0.66
NE Chukotka Russia NEC 67.07 –174.50 10 0.73 0.72
Table 1. Characteristics of sampled Common Ringed Plover breeding areas. Latitude and longitude are means of sampling area; n:
number of individuals successfully genotyped; HE: expected heterozygosity across final six microsatellite markers; HO: observed
heterozygosity across final six microsatellite markers.
SEC
NWC
TAY
VOR
VAR
LAP
HAL
TUR
WES
NEC
ECC
1000 km
Figure 1. Polar projection of the breeding range and the sampled Common Ringed Plover breeding sites. Breeding range of the
species is shown based on distributions given in Delany et al. (2009), Valcu et al. (2012) and Lappo et al. (2012).
Thies et al.: POPULATION AND SUBSPECIES DIFFERENTIATION IN COMMON RINGED PLOVERS
nests using funnel traps and automatic clap-net traps.
Chicks were caught at hatching or during opportunistic
encounters in the field. We obtained a small blood
sample (25–50 ml for adults from the brachial vein, 25
ml for chicks from the tarsal vein). At NWC and SEC, we
obtained tissue samples from one embryo from one
nest at each site. Blood and tissue were stored in
absolute ethanol or Queen’s Lysis Buffer (Seutin et al.
1991) whilst feather samples were kept refrigerated in
sealed plastic bags until DNA extraction.
DNA extraction and microsatellite genotyping
followed established protocols (Küpper et al. 2009,
2012). In brief, we extracted DNA from blood or
feather using an ammonium acetate precipitation
protocol following proteinase K digest (Nicholls et al.
2000). After two ethanol washes, the DNA was dried
and dissolved in TE buffer. We used a 0.7% agarose gel
stained with SYBR safe (Invitrogen) to check the
quality and establish quantity of the DNA. We amplified
fragments of two to four microsatellite loci in multiplex
Polymerase Chain Reactions (PCRs) with 1 ml (0.1–100
ng) of DNA solution, 1 ml of primer mix including one
fluorescently labelled primer per pair and 4 ml (samples
extracted from blood) or 8 ml (samples extracted from
feathers) solution of the Qiagen Multiplex PCR Kit
(Qiagen). We analysed the fragment length of PCR
products using an ABI3730 capillary DNA Analyzer,
visualising the fragments with GENEMAPPER software
v. 4.1. We avoided the inclusion of first order relatives
by genotyping only presumably unrelated parents, or a
single chick from broods where the parent had not been
caught.
To obtain polymorphic markers we tested available
microsatellite primers at the NERC Biomolecular Analy -
sis Facility at University of Sheffield that had shown
amplification in other Charadrius plovers previously
(Funk et al. 2007, Küpper et al. 2007, 2008, Dawson et
al. 2010). The final microsatellite data consisted of
eight autosomal polymorphic microsatellite markers
(Table 2). We only included samples from which we
obtained at least 75% of genotypes from all markers.
All excluded samples were feather samples from Ice -
land (n= 18), likely reflecting the higher degradation
or lower DNA yield from these samples (Harvey et al.
2006). All subsequent calculations were made using
the remaining 173 individuals from eleven sites (Table
1).
Linkage disequilibrium, null alleles and deviation
from neutrality may have an impact on estimates of
population differentiation (Luikart et al. 2003, Chapuis
& Estoup 2007). We therefore tested the microsatellite
loci for these three characteristics for each sampling
area where at least ten individuals were successfully
genotyped, before conducting the population genetic
analyses. We used GENEPOP v. 4.3 (Raymond & Rousset
1995) to test for linkage disequilibrium, adjusting the
significance levels with the Bonferroni correction to
account for multiple testing in pairwise comparisons
between loci. We assessed the proportion of null alleles
in MICROCHECKER v. 2.2.3 (Van Oosterhout et al. 2004)
with 0.95 confidence interval and 1000 bootstrap itera-
tions. We then assessed the selective neutrality of
unlinked markers that showed no significant null
alleles using the software LOSITAN (Antao et al. 2008),
which employs an FST-outlier approach to test for loci
under selection. The confidence interval was set to 0.99
and we tested for conformance to neutrality under the
stepwise mutation model with the options ‘neutral
mean FST’ and ‘force mean FST’.
We used ARLEQUIN v. 3.5.2.2 (Excoffier et al. 2005)
to compute indices of genetic variation within and
among populations, including observed heterozygosity
(HO) and expected heterozygosity (HE). We then evalu-
ated population differentiation among breeding areas
in several steps. To account for (1) expected variance in
gene flow in pairwise comparisons that feature sites of
the same or different subspecies, (2) variation in genetic
diversity and (3) type of genetic markers employed, we
calculated three pairwise indices of genetic variation:
FST, RST and Jost’s DST (henceforth D) between breeding
areas. FST, a summary statistic introduced by Wright
(1943), is the method
most frequently used for assess
ing
167
Locus ALength Genotyping Reference
name (bp) success
Calex7 15 137–167 99% Küpper et al. (2007)
Calex10 3 210–214 100% Küpper et al. (2007)
Calex17 4 190–196 100% Küpper et al. (2007)
Calex20*8 176–183 86% Küpper et al. (2007)
Calex23 7 227–239 88% Küpper et al. (2007)
Calex40*21 124–237 90% Küpper et al. (2007)
C201 18 154–192 98% Funk et al. (2007)
C204 9 185–201 99% Funk et al. (2007)
Table 2. Characteristics of the eight microsatellite markers used
to genotype Common Ringed Plovers. Genotyping success refers
to samples included in the final data set. Loci marked with an
asterisk were excluded from population genetic analyses due to
the presence of null alleles. Arefers to the number of alleles,
and Length to allele length in base pairs.
population differentiation (Meirmans & Hedrick 2011)
and we provide values for the widely used FST-esti-
mator that is appropriate for multiallelic loci (Weir &
Cockerham 1984). RSTis an FST-analogue reflecting the
mutational processes of microsatellite markers. For its
calculation the assumption is made that marker varia-
tion is generated through a stepwise mutation model
and hence RSTvalues may provide more accurate differ-
entiation estimates when markers adhere to the step-
wise mutation process (Balloux & Lugon-Moulin 2002).
However, FST-based estimates may perform better than
RST when there are few loci and small sample sizes
(Gaggiotti et al. 1999). Dmeasures the actual differen-
tiation in allele frequencies among populations (Jost
2008). Unlike FST , it does not decline with increasing
marker polymorphism, as the maximum possible FST-
value is determined by the amount of within-popula-
tion diversity (Meirmans & Hedrick 2011). Pairwise
FST- and RST-values were calculated using ARLEQUIN
with 1000 random permutations. D-values were calcu-
lated using the DEMEtics package v. 0.8-7 (Gerlach et
al. 2010) for R v. 3.2.2. (R Core Team 2015) with 1000
bootstrap resamplings to estimate P-values. All FST -,
RST- and D-values were tested at a Bonferroni adjusted
a
threshold of 0.05 to account for multiple testing.
Differentiation among all breeding areas was also
assessed through an analysis of molecular variance
(AMOVA) implemented in ARLEQUIN. We assessed to
what extent molecular variance would be explained by
variance among individuals, breeding areas or sub -
species in two models: (1) across all breeding areas and
(2) while grouping the areas into the three traditional
subspecies based on their geographic location.
We tested for isolation-by-distance by performing
Mantel-tests. Pairwise geographic distances between
breeding areas were calculated based on geographic
coordinates. As a measure of genetic distance, we
calculated pairwise FST, RSTand D. Mantel-tests were
calculated using the ecodist package v. 1.2.9 (Goslee &
Urban 2007) in R and statistical significance was
assessed using 10,000 random permutations.
We used the Bayesian clustering software STRUC-
TURE v. 2.3.4 (Pritchard et al. 2000) to determine
population structure and the number of genetic clusters
Kin our sample set. We ran two sets of models: (1)
without location prior and (2) with location prior
grouping samples within a distance of 500 km, merging
VAR and LAP as well as SEC, ECC and NEC, resulting in
eight regions with specific location priors. Using the
location prior has been shown to identify meaningful
structure when the amount of available genetic data
(samples or markers) is low (Hubisz et al. 2009). We
used the admixture model with correlated allele
frequencies (Falush et al. 2003). We tested models from
K= 1 to K= 11 (the number of breeding areas). For
each model, we ran ten replicates with a burn-in period
of 100,000 followed by 1 million generations. We
combined the ten replicates to assess summary statis-
tics for each model, including assignment probabilities,
log-likelihoods and delta K(Evanno et al. 2005), using
STRUCTURE HARVESTER (Earl & vonHoldt 2012).
Results of the ten runs for each Kwere summarised
using CLUMPP (Jakobsson & Rosenberg 2007) and
visualised with DISTRUCT (Rosenberg 2004).We then
identified the most plausible K from inspection of the
plots and summary statistics of each model.
ARDEA 106(2), 2018
168
WES HAL LAP TUR VAR VOR TAY NWC SEC ECC NEC
WES 0.41 –0.01 –0.03 0.10 0.13 –0.01 0.04 –0.02 0.01 –0.03
HAL 0.02 0.28 0.40 0.15 0.29 0.39 0.24 0.35 0.40 0.34
LAP –0.02 0.01 –0.02 –0.01 0.03 –0.03 –0.08 0.00 –0.02 –0.04
TUR –0.01 0.03 0.00 0.07 0.09 –0.03 –0.02 –0.02 –0.03 –0.04
VAR 0.01 0.03 –0.02 0.01 0.06 0.07 –0.04 0.08 0.08 0.03
VOR 0.02 0.05 0.02 0.01 0.05 0.10 –0.07 0.06 0.07 0.04
TAY –0.01 0.04 –0.01 0.00 0.01 0.03 –0.01 –0.01 –0.03 –0.04
NWC 0.06 0.08 0.04 0.08 0.09 0.13 0.05 –0.07 –0.06 –0.09
SEC 0.00 0.02 –0.01 0.01 0.02 0.02 0.01 0.06 –0.02 –0.02
ECC 0.02 0.02 0.00 0.00 0.01 0.04 0.00 0.07 0.00 –0.04
NEC 0.00 0.03 0.01 0.01 0.04 0.00 0.01 0.06 –0.01 0.03
Table 3. Pairwise FST- (below diagonal) and RST -values (above diagonal) among Common Ringed Plover sampling areas.
Abbreviations are given in Table 1. Bold values indicate significant (P< 0.05) differentiation after Bonferroni correction.
Thies et al.: POPULATION AND SUBSPECIES DIFFERENTIATION IN COMMON RINGED PLOVERS
RESULTS
All pairwise tests for linkage disequilibrium were non-
significant after Bonferroni correction. Loci Calex20
and Calex40 showed a significant proportion of null
alleles in at least three breeding areas and consequently
both loci were excluded from further analyses. The
remaining six loci appeared to be selectively neutral as
selection tests were not significant when accounting for
multiple testing.
The final six loci had a mean of 9.3 alleles per locus
ranging from three at Calex10 to 18 alleles at locus
C201 (Table 2). HEshowed little variation between
sites, ranging from 0.66 in ECC to 0.72 in NEC, whereas
we observed more variation at HO, ranging from 0.47 in
LAP to 0.73 in NEC, TAY and TUR (Table 1).
Pairwise comparisons of FST- and D-values among
breeding areas revealed no significant genetic differen-
tiation after Bonferroni correction (FST: mean = 0.02,
range: –0.02–0.13; D: mean = 0.06, range: –0.12–
0.16; Table 3, Table S1). RST -values were generally
higher (mean = 0.06, range: –0.09–0.40) and we
detected eight significant comparisons, all between
HAL in southwestern Sweden and other breeding areas
(Table 3). In fact, for RST the ten highest pairwise com -
parisons all involved HAL whereas there was no clear
pattern in FST or D.
The AMOVAs showed differences in attributing
genetic variation to the different hierarchical levels. For
the first comparison, with breeding areas as the highest
hierarchical level, the RST (P< 0.001) but not FST
model (P= 0.42) attributed significant genetic varia-
tion to differences among breeding areas. The second
set of models, with a priori grouping of the sites accord -
ing to the three putative subspecies and geographic
information, explained no significant amount of varia-
tion in FST (P= 0.124) or RST (P= 0.238; Table 4). We
found no isolation by distance effect using FST (r=
0.03, P= 0.40), RST (r= 0.13, P= 0.13) or D(r=
0.18, P= 0.07).
The results of the Bayesian clustering differed
according to whether a location prior was specified or
not. Without location prior the best model was K= 1,
suggesting no population differentiation across sites.
With the help of the location prior we detected further
plausible genetic structure. Comparison of log-likeli-
hoods, the Evanno method and the graphical output
suggested that the best model was K= 3 (Figure 2).
Ringed Plovers breeding in Iceland (WES), that repre-
sented the subspecies psammodromus, formed one
cluster, plovers from HAL, representing subspecies
hiaticula, formed the second cluster and plovers from
Russia (VOR, TAY, NWC, SEC, ECC, NEC), that belong
to the subspecies tundrae, formed the third. There was,
however, no support for the kolymensis subspecies.
Interestingly, tundrae individuals from LAP, VAR and
hiaticula plovers from TUR were not clearly assigned to
any cluster. Instead, consistently across all ten indi-
vidual runs, proportions of their genotypes were
assigned to all three clusters, but the sites did not form
an additional fourth cluster (see bottom panel of Figure
2). Interestingly, the genetic profiles of putative tundrae
plovers from Lapland and Varanger appeared more
similar to the hiaticula cluster, whereas the putative
hiaticula individuals from TUR were more related to the
tundrae cluster (Figure 2).
DISCUSSION
Our genetic evaluation of subspecies delineation in the
Ringed Plover based on microsatellite markers
provided
some support for the presence of subspecies hiaticula,
tundrae and psammodromus, but no support for repro-
169
FST RST
Model Source of variation % var P% var P
(1) breeding areas only Among breeding areas 1.84 0.420 13.00 <0.001
Among individuals 3.81 0.016 6.99 0.060
Within individuals 94.35 <0.001 80.01 <0.001
(2) subspecies Among subspecies 0.62 0.124 5.83 0.238
Among breeding areas 1.41 0.009 8.78 <0.001
Among individuals 3.81 0.014 6.86 0.056
Within individuals 94.16 <0.001 78.52 <0.001
Table 4. Results of AMOVA-models for FST and RST in the Common Ringed Plover. The top hierarchical grouping for each model is
given in the Model column.
ductive isolation of kolymensis Ringed Plovers. The
Bayesian clustering analyses showed a reasonable
match between geographic and genetic data, corrobo-
rating the three established subspecies, although the
AMOVA results for subspecies delineation were not
significant. As kolymensis Ringed Plovers are geograph-
ically separated and show distinct morphological
features from C. h. tundrae, the observed lack of genetic
differentiation among breeding sites in Siberia implies
a mismatch between genetic and phenotypic data across
the distribution range of this species. Such mismatches
are commonly reported in other waders that breed at
high latitudes. In Dunlins Calidris alpina and Purple
Sandpipers Calidris maritima, two sandpipers that
breed at similar latitudes as Ringed Plovers, subspecies
delineation based on phenotypic characters is poorly
supported by genetic markers (Marthinsen et al. 2007,
Barisas et al. 2015, LeBlanc et al. 2017). In contrast, in
several temperate or tropic waders, sub species delin-
eation is in agreement with patterns of genetic differen-
tiation (Ottvall et al. 2005, Funk et al. 2007, Höglund et
al. 2009, Küpper et al. 2009, Miller et al. 2010, but see
Rheindt et al. 2011, dos Remedios et al. 2017).
Why do mismatches between phenotypic and gene -
tic characters occur more frequently in high latitude
species than in low latitude species? Historic climate
oscillations had particularly strong impacts on habitats
at higher latitudes, including those the Ringed Plover
occupies today. These changes are thought to have
resulted in repeated expansions and contractions of
species ranges. Current within-species patterns of
genetic differentiation in boreal and arctic species are
thought to have been shaped by Pleistocene climate
cycles in particular (Avise & Walker 1998, Lovette
2005). Due to contrasting habitat demands, historic
population changes are likely to exhibit opposing
patterns for boreal/temperate and arctic species
(Kraaijeveld & Nieboer 2000). Advancing ice sheets
during glaciation periods forced populations of boreal/
temperate species into reproductively isolated refugia
where they started to diverge in isolation. In contrast,
glaciation periods were likely a time of population
range shifts and often expansions for subarctic/ arctic
breeders such as the Ringed Plover. Although northern
breeding areas were lost to the expanding ice, suitable
habitat became available in southern areas leading to
population expansions southwards. In warm periods,
vegetation growth created environmental barriers and
shifted available habitat further north meaning that
populations became restricted to higher latitudes or
isolated mountain areas where they diverged through
isolation by environment (Kraaijeveld & Nieboer 2000).
Based on our results, we tentatively suggest that in
Ringed Plovers, the isolation period may have first led
to differentiation between tundrae and hiaticula
subspecies in allopatry during the last glaciation (the
ARDEA 106(2), 2018
170
WES
HAL
LAP
TUR
VAR
VOR
TAY
NWC
SEC
ECC
NEC
Figure 2. Results of the Bayesian clustering analysis conducted in STRUCTURE for Common Ringed Plovers grouped by breeding
areas. Results are displayed as averages of ten runs for models K= 2 (top), K= 3 (middle) and K= 4 (bottom). Model K= 3 has
highest biological plausibility, clustering the samples according to subspecies and indicating an admixture zone spanning from
Northern Scandinavia (LAP and VAR) to Belarus (TUR). There was no support for a divergence of the subspecies kolymensis (NWC,
SEC, EEC and NEC).
Thies et al.: POPULATION AND SUBSPECIES DIFFERENTIATION IN COMMON RINGED PLOVERS
Weichselian glacial). During this period, ice sheet
coverage was very heterogeneous and varied regionally
(Kraaijeveld & Nieboer 2000, Svendsen et al. 2004).
Coverage was greatest in central Siberia 80,000 to
90,000 years ago (Svendsen et al. 2004) whereas
northern Europe was fully ice-covered only during the
last glacial maximum (18,000 to 20,000 years ago).
Remarkably, during this late glacial period nearly all of
Siberia was ice free (Tarasov et al. 2000, Svendsen et al.
2004), presumably providing ample breeding habitat
for Ringed Plovers. While hiaticula may have found
refuge in western Europe on ice free parts of the British
Isles or in the North Sea area, tundrae was likely
restricted to the ice free parts of Siberia. In eastern
Europe, boreal forests almost reached the ice sheets
(Kraaijeveld & Nieboer 2000). Ice sheets and forests
hence would have provided a geographic barrier that
separated hiaticula and tundrae plovers.
The current breeding range of psammodromus was
fully covered by a large ice sheet during the last glacial
maximum (Patton et al. 2016), meaning that the Ice -
land colonisation by Ringed Plovers must have
occurred after the ice retreated. Pairwise RST -values
and hierarchical Bayesian analysis indicate that the
Icelandic Ringed Plovers (ssp. psammodromus) are
more closely related to tundrae than to hiaticula,
suggesting that tundrae ancestors colonised Iceland
despite the larger distance (c. 800 km vs. c. 1100 km to
the closest breeding areas of hiaticula and tundrae,
respectively). Alternatively, the subspecies psammod-
romus could have first diverged during an interglacial
period from hiaticula and then colonised Siberia
through leap-frog dispersal. Given that Greenland and
Eastern Canada were covered by ice sheets, both
refugia would have been located nearby in Northwest
Europe. We consider this scenario unlikely since popu-
lations would likely have had secondary contact before
differentiation took place.
After the retreat of the ice sheet, population expan-
sions led to secondary contact between the subspecies.
The hierarchical Bayesian analysis conducted in STRUC-
TURE suggests that the secondary contact zone runs
through Northern Scandinavia and Eastern Europe;
Ringed Plovers at three locations (LAP, VAR and TUR)
show an admixture profile, indicating that these sites
are part of an intergradation zone between the three
subspecies (Figure 2). Currently, the three populations
are either classified as tundrae (LAP and VAR) or hiati -
cula (TUR) based on phenotypic characters (Pinchuk et
al. 2016). For the Scandinavian sites LAP and VAR this
supports observations of intermediate phenotypic char-
acters in these populations (Haftorn 1971, Engelmoer
& Roselaar 1998). We note that the Belarusian breeders
(TUR) show a higher genetic similarity to the Siberian
Ringed Plovers sampled at VOR and TAY than to the
other hiaticula population sampled at HAL. Indeed, the
pairwise comparisons HAL vs. TUR were moderate
(FST, D) to high (RST; Table 4, Table S1) suggesting
substantial differentiation. Based on the clustering
results we hypothesize that the admixture zone runs
further to the west, e.g. through Poland or the Eastern
Baltic countries Lithuania, Estonia or Latvia.
Given the lack of genetic differentiation among the
Far East Siberian plovers, we suggest that the disjunct
distribution of Ringed plovers in Siberia is the result of
recent colonisation and expansion (Lappo et al. 2012).
The gap in their distribution between 145°E and 160°E
could be either the result of leap migration over unsuit-
able breeding habitat, or due to the past extinction of
small population segments in this area. Interestingly,
plovers recently started to close this gap in the breeding
distribution using artificial gravel or sand mounds
created by humans near villages, suggesting that the
colonisation process is still ongoing (Lappo et al. 2012).
The current migration routes of Ringed Plovers
breeding in Chukotka support the idea of their post-
glacial eastward expansion: Chukotka Ringed Plovers
have been found to winter in Africa and the Middle
East, but during both spring and autumn migration,
they migrate through Siberia following the West Asian
– East African Flyway (Tomkovich et al. 2017).
The pattern of genetic differentiation is similar to
the one observed in other Palearctic temperate- and
arctic-breeding wader species (e.g. Ottvall et al. 2005,
Marthinsen et al. 2007, Höglund et al. 2009, Rönkä et
al. 2012, Verkuil et al. 2012, Conklin et al. 2016).
Interestingly, the geographic ranges of genetic lineages
or subspecies of waders tend to be highly variable,
suggesting that different species used different refugia
or displayed different recolonisation dynamics. As in
other plover species (D'Urban Jackson et al. 2017), we
observed low genetic differentiation over large geo -
graphic distances in the Ringed Plover. Previous work
demonstrated that breeding dispersal, which is closely
linked to mating system in many waders, is related to
the degree of genetic differentiation. Polygamous
wader species often show remarkable long-distance
breeding dispersal within a season (Kempenaers &
Valcu 2017) and have lower genetic differentiation
than monogamous species (D'Urban Jackson et al.
2017). Based on its monogamous mating system
(Wallander et al. 2001), we expected the Ringed Plover
to exhibit moderate to high genetic structure and a
pattern of isolation by distance. In contrast to these
171
predictions, we observed a complete lack of genetic
differentiation within the tundrae subspecies, for which
many samples from a large geographic distribution
were analysed. Ringed Plovers have low natal philo -
patry, associated with high juvenile dispersal (Laven
1940, Pienkowski 1984, Foppen et al. 2006). Such high
juvenile dispersal will result in high gene flow and
prevent population divergence since as little as one
migrant per generation is commonly assumed to
prevent populations from diverging (Mills & Allendorf
1996). Recent studies have also pointed to individual
variation in migratory behaviour within subspecies
with variation in routes, periods and selection of stop
over sites, leading to shorter ‘hops’ and longer ‘jumps’
even for individuals from the same populations (Hedh
& Hedenström 2016, Lislevand et al. 2017, Tomkovich
et al. 2017). It is not known how individual migration
schedules develop but it is plausible that juveniles from
different natal sites mix in the wintering areas and then
simply follow adults from their winter group to their
breeding grounds.
The genetic differentiation of Icelandic Ringed
Plovers from continental populations is in line with
observed patterns among island populations of many
other bird species (Gill 2014). The ocean seems partic-
ularly effective in preventing or inhibiting gene flow
even for proficient dispersers such as waders that
migrate annually over large water bodies. In plovers,
populations across island archipelagos are often geneti-
cally differentiated and this may eventually lead to
allopatric speciation through isolation by environment
(Küpper & dos Remedios in press). Isolation in allo -
patry, on continents or islands separated by water, has
led to the emergence of closely related sister species
such as the Kentish Plover Charadrius alexandrinus
superspecies complex (Rheindt et al. 2011, Almalki et
al. 2017).
In this study, we used eight polymorphic microsatel-
lites originally developed for other plover species (Funk
et al. 2007, Küpper et al. 2007). Two of these markers
showed a high frequency of potential null alleles. There
is always a trade-off between minimizing the errors
that arise from non-conforming markers by keeping
loci, and reducing statistical power by reducing the
number of loci (Selkoe & Toonen 2006). However, a
clustering analysis including the presumed null allele
markers lead to the same qualitative results in regards
of number of genetic clusters and admixture zone
(Figure S1). We did not find any support for isolation-
by-distance patterns either across the species range or
within subspecies (own unpubl. data). This seems at
first remarkable, given that we sampled across large
geographic distances. We note that estimates of genetic
differentiation based on RST were generally higher than
those based on FST. This may be explained by a better fit
of the stepwise mutation model for the employed
microsatellite markers (Slatkin 1995, Balloux & Lugon-
Moulin 2002). Alternatively, the low pairwise FST-values
may reflect the high amount of within-population diver-
sity found in Ringed Plover populations (Meirmans
&
Hedrick 2011). However, this may also be a conse-
quence of the generally low to moderate genetic differ-
entiation observed, and could change if more markers
were employed and more sites added in future studies.
Additional markers would also be needed to robustly
test our outlined phylogeographic hypothesis on the
impact of glaciation on subspecies differentiation.
In conclusion, we found genetic support for the
three currently recognised subspecies of the Ringed
Plover based on microsatellite markers. An admixture
zone of all three subspecies runs through Northern
Scandinavia and Eastern Europe. The existence of a
fourth suggested subspecies, with different phenotypic
traits, breeding in Chukotka was not supported by the
genetic data. We suggest that the current three sub -
species arose only during and after the latest glaciation
event. The apparent mismatch between genetic and
phenotypic characters in the Ringed Plover may be
explained by its evolutionary history, which most likely
included repeated population expansions and contrac-
tions. Future studies with DNA sequence markers and
further sampling sites are needed to shed light on the
timing of population differentiation, the exact location
and dynamics of the admixture zone and may also
clarify the colonisation history of this species.
ARDEA 106(2), 2018
172
ACKNOWLEDGEMENTS
Alexei Dondua, Egor Loktionov, Yulia Karagicheva, Vladimir
Morozov, Viktor Golovnyuk, Angela Pauliny, Mikael Larsson,
Uno Unger, Natalia Karlionova, Eugenia Luchik and Ivan
Bogdanovich helped with collection of samples. Mihai Valcu
kindly provided an updated map of the breeding distribution
and the R-code to draw Figure 2. We thank Roos Kentie and two
anonymous reviewers for constructive comments on an earlier
version of this manuscript. Fieldwork was funded through Elis
Wides foundation (to JD) and BirdsRussia (to PT). PT was
supported by the Russian Science Foundation project no. 14-50-
00029. DB was supported by the Swedish research council
Formas (grants no. 215-2009-463 and 215-2013-752). Micro -
satellite genotyping was funded and conducted at the NERC
Biomolecular Analysis Facility, University of Sheffield. We thank
Terry Burke and Tamás Székely for initial discussion of the
study. CK was supported by the Max Planck Society.
Thies et al.: POPULATION AND SUBSPECIES DIFFERENTIATION IN COMMON RINGED PLOVERS 173
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ARDEA 106(2), 2018
176
SAMENVATTING
Het onderzoeken van patronen in genetische structuren in de
context van geografische en fenotypische variatie is belangrijk
om de evolutionaire processen te begrijpen die bij soortvorming
zijn betrokken. We hebben de differentiatie van populaties en
ondersoorten onderzocht in Bontbekplevieren Charadrius hiati-
cula, een steltloper die broedt in de arctische en gematigde
streken van het noordoosten van Canada en van Eurazië (tot het
Russische Verre Oosten toe). Er worden momenteel drie onder-
soorten algemeen erkend (hiaticula, tundrae en psammodro -
mus), terwijl een vierde ondersoort (kolymensis) is voorgesteld
op basis van geografische isolatie en fenotypische verschillen.
We hebben 173 monsters van elf broedlocaties van Bontbek -
plevieren (die alle de vier vermeende ondersoorten vertegen-
woordigen) gegenotypeerd op acht polymorfe microsatelliet-loci
om patronen in populaties en ondersoorten te analyseren.
Bayesiaanse clustering onderscheidde drie genetische clusters,
overeenkomend met de broedplaatsen van de drie momenteel
erkende ondersoorten. Het bestaan van de ondersoort koly-
mensis werd niet ondersteund. We ontdekten ook de aanwezig-
heid van een voorheen onbekende hybridisatiezone die zich
uitstrekt van Noord-Scandinavië tot Wit-Rusland. Differentiatie
van de ondersoorten tundrae en hiaticula deden zich hoogst-
waarschijnlijk voor in allopatrische populaties op het Eura -
ziatische continent, gevolgd door populatiegroei die leidde tot
kolonisatie van IJsland en Groenland. Het ontbreken van geneti-
sche differentiatie binnen de ondersoort tundrae komt overeen
met de voortdurende uitbreiding van het leefgebied en de sterke
uitwisseling van genen die door het trekgedrag in stand wordt
gehouden. We bespreken het belang van historische klimaatver-
anderingen, trekgedrag en paringssysteem bij het vormgeven
van het waargenomen patroon van genetische differentiatie.
Corresponding editor: Roos Kentie
Received 20 April 2018; accepted 24 July 2018
Supplimentary Material is available online
www.ardeajournal.nl/supplement/s106–163–176
