Multilocus phylogeny and species delimitation within the Natterer’s bat
species complex in the Western Palearctic
I. Salicini ⇑, C. Ibáñez, J. Juste
Department of Evolutionary Ecology, Estación Biológica de Doñana (EBD-CSIC), Seville, Spain
a b s t r a c t
Keywords:
Cryptic species
Species-tree
Introns
Integrative taxonomy
Chiroptera
Myotis
Delimiting species is a crucial issue for many biological disciplines and is of primary importance for
designing effective conservation plans. Traditional taxonomy based on morphological characters can
be misled by the presence of phenotypic plesiomorphism or adaptative convergence. The use of multiple
locus genetic data appears thus as a powerful tool for recognizing species boundaries.
In this study, we used six nuclear introns and two mitochondrial markers to conduct a phylogenetic
study of the Myotis nattereri species complex in the Western Palearctic. We combined tree-based and
non-tree-based analyses, and also used concatenated phylogenetic methods of the separated nuclear
and mitochondrial dataset as well as a recent coalescence-based multilocus approach. The strong concordance between the results of the analyses conducted confirms that M. nattereri is a paraphyletic group
that is composed of four well-differentiated lineages in the study area. In the framework of the unified
species concept, these four clades can be confidently considered as four valid species. This recognition
of new cryptic species in the Western Mediterranean region shows that the biodiversity of this wellstudied area is still not fully understood.
.
1. Introduction
Despite being key concepts in the fields of systematic and evolutionary biology, recognizing and delimiting species are also two
highly controversial issues (e.g. Agapow et al., 2004; Mayden,
1997). Recognizing species is not only a taxonomic challenge, but
is also essential for other biological disciplines such as biogeography, ecology and evolutionary biology (Sites and Marshall, 2003),
and has serious consequences for conservation biology and the design of effective conservation plans (Agapow, 2005; Sattler et al.,
2007).
Although the development of the ‘unified species concept’ (de
Queiroz, 2007) and the recent conceptual advances in integrative
taxonomy (Padial et al., 2010) seem to have reconciled somewhat
the variety of alternative species concepts, there is still strong disagreement regarding the reliability of methods used in delimiting
species, that is, in recognizing evolutionary independent metapopulation lineages (Wiens, 2007).
Undoubtedly, methodological advances in molecular biology
and the great amount of available genetic information, together
with tremendous analytic improvements, have enabled DNA-based
approaches to play a leading role in detecting species boundaries,
⇑ Corresponding author. Address: Estación Biológica de Doñana (EBD-CSIC),
C/Americo Vespucio, 41092 Sevilla, Spain. Fax: +34 954621125.
E-mail address: irene@ebd.csic.es (I. Salicini).
supporting or replacing traditional methods based on morphological, ecological and behavioral analyses.
On the other hand, to avoid the risk of possible taxonomic inflation (Isaac et al., 2004) and the possibility of other misleading pitfalls such as convergent molecular evolution (e.g. Jones, 2010), it is
vital that great emphasis be placed on the reliability and improvement of methods used in genetic delimitation analyses.
The ‘traditional’ molecular approach, using a few genes tree
(usually a mitochondrial one) as an approximation to the species
trees, has been widely criticized due to an increasing awareness
that there is a strong likelihood of discordance between gene trees
and species tree (e.g. Degnan and Rosenberg, 2009; Edwards, 2008;
Maddison, 1997). Information from different genetic markers
(mitochondrial and nuclear) is thus necessary for delimiting evolutionary lineages, as well as for establishing phylogenetic relationships. On the other hand, the ‘democratic approach’ and the
consensus and concatenation methods, widely used in multilocus
analyses, imply a risk of obtaining overconfident support for incorrect species trees (see review in Degnan and Rosenberg (2009) and
in Kubatko and Degnan (2007)).
Several new methods have been developed in recent years for
inferring species tree from multiple loci in a coalescent framework
(see the extensive reviews in Blair and Murphy (2011) and in Liu
et al. (2009)). Nevertheless, these coalescence-based methods are
still poorly applied in non-model species phylogenies, especially
at the interface between populations and species, where the threat
of incomplete lineage sorting is greatest (Degnan and Rosenberg,
2009). Despite a large body of theoretical work that clearly urges
researchers to use such coalescent-based methods (e.g. Eckert
and Carstens, 2008; Edwards, 2008; Maddison and Knowles,
2006), the best way of applying these methods is still far from
obvious. Moreover, the high computational demand that such
new methodologies suppose adds further practical difficulties to
their application. Different strategies are being used (e.g. Belfiore
et al., 2008; Carstens and Dewey, 2010; Hird et al., 2010; Leaché,
2009; Yang and Rannala, 2010) in what seems to be a heterogeneous effort to apply ‘good theory’ to a more complicated reality.
Genetic methods are certainly a precious tool for delimiting
divergent clades that would otherwise be impossible to recognize
using classical phenotypic characters due to their morphological
convergence or parallelism (Burland and Worthington Wilmer,
2001; Goodman et al., 2009; Weisrock et al., 2010). Morphological
crypticism seems to be particularly common in the order Chiroptera (Burland and Worthington Wilmer, 2001; Mayer and von
Helversen, 2001) and it is probably connected to strong constraints
on phenotypic evolution resulting from their very specialized and
successful morphologies, and to the low dependence that these
nocturnal animals have on visual cues (Burland and Worthington
Wilmer, 2001). Even in the well-studied European fauna, several
new species of bats have been described in the last two decades
based on genetic differentiation, including Pipistrellus pygmaeus
(Barrat et al., 1997), Plecotus macrobullaris and Plecotus kolombatovici (Kiefer and Veith, 2002; Spitzenberger et al., 2006), and Myotis
alcathoe (Helversen et al., 2001).
The bat genus Myotis has undergone one of the most successful
radiations of all mammal groups and more than 100 described extant species are found throught almost all of the world (Simmons,
2005). The lack of clear differentiated morphology, the presence of
plesiomorphic characters and the existence of sibling species are
all factors that have hindered the taxonomic work on this group
(Menu, 1987; Ruedi and Mayer, 2001). Based on morphological
and ecological characters, three different subgenera have traditionally been recognized within Myotis (Findley, 1972), along with the
controversial Cistugo group. Nevertheless, this phenotypic differentiation has been shown to be largely incongruent with phylogenetic evolution (Hoofer and Van Den Bussche, 2003; Ruedi and
Mayer, 2001; Stadelmann et al., 2007) and is instead the consequence of recurrent adaptative convergence. Morphological similarity is thus a poor predictor of the phylogenetic relationship
between species in this group of bats and is a misleading descriptor
of taxonomic arrangements at multiple levels, from species to genera (Carstens and Dewey, 2010; Ruedi and Mayer, 2001; Stadelmann et al., 2007).
According to traditional taxonomy, Natterer’s bat (Myotis nattereri) is non-migratory and widespread throughout most of Europe, from Portugal and the northern part of the Maghreb as far
as western Russia, the Caucasus and north-western Asia Minor.
This species appears to be part of the so called ‘nattereri clade’, a
group of closely related taxa with confusing taxonomy that have
been considered variously as species or subspecies or synonyms,
depending on the studied characters (Horáček and Hanák, 1984;
Jones et al., 2006). Other lineages recognized in this group are distributed in the Far East (Myotis pequinius and Myotis bombinus) and
in the Caucasus (Myotis schaubi). Several studies have recently
showed the presence in the Western Palearctic of deep differentiated mitochondrial lineages of M. nattereri in the Iberian and Italian Peninsulas and in the north-west Maghreb (Galimberti et al.,
2010; García-Mudarra et al., 2009; Ibáñez et al., 2006; Mayer
et al., 2007), suggesting an as-yet unresolved phylogenetic
situation.
Our main goal in this study is to clarify the phylogeny of M. nattereri in the Western Mediterranean Basin through a wide
sampling area. The use of both mitochondrial and nuclear markers
in a multilocus context will permit to test whether the previously
found lineages reflect only the mitochondrial evolution history
within a unique species, or if they are the result of long-term population isolation that ended with speciation events. If this second
hypothesis is true, we expect to find the same differentiation reflected also in the nuclear genome.
The strategy we employed consisted of performing several concatenated phylogenetic reconstructions and clustering analyses
with different sets of genetic data obtained from mitochondrial
and nuclear DNA as a means of grouping individuals into lineages,
thereby allowing us to compare results and test the robustness of
these clusters. The highlighted clades were then used as ‘species’ in
a multi-locus coalescent-based analysis to achieve a better resolution for their interrelationships and to assess the reliability of the
node in a coalescence-based framework (e.g. Belfiore et al., 2008;
Leaché, 2009; Fujita et al., 2010).
From a conservation standpoint it is also vital to assess the real
taxonomy and biogeography of this complex of lineages. The
IUCN’s assessment for M. nattereri is ‘Least Concern’, since it is considered to be a ‘widespread and abundant’ taxon with ‘no evidence
of current significant population decline’. However, the suggested
presence of a complex of differentiated species could completely
modify this picture.
2. Materials and methods
2.1. Sampling
In the present study we analyzed a total of 32 samples of the
M. nattereri complex (sensu Simmons, 2005) from different regions
of the Western Palearctic: Iberian Peninsula (15 samples), Italian
Peninsula (7 samples), Morocco (2 samples) and Central Europe
and Balkans (8 samples) (Fig. 1). Two samples of M. schaubi (the
only species of the nattereri complex that is geographically close
to the studied lineages) from Iran were also included to provide
a clearer picture of the phylogenetic relationships of the group
(Appendix A).
Samples from Myotis daubentonii and Myotis mystacinus were
used as out-groups in the analyses.
Most of the samples were ethanol-stored wing punches (Worthington Wilmer and Barratt, 1996). Tissues were digested with
Proteinase K and total genomic DNA was extracted using phenol/
chloroform protocol and ethanol precipitation (Sambrook et al.,
1989).
2.2. DNA amplification and sequencing
Partial mtDNA Cytochrome b (Cytb) and NADH dehydrogenase
1 (ND1) fragments were amplified using the pairs of primers Molcit-F (Ibáñez et al., 2006) – Molcit-R (50 -CCTTTGCCGGTTTACAAGACC-30 ) and ND1F2 – ND1R (Kawai et al., 2002) respectively.
Due to the high level of phylogenetic relatedness among the lineages included in our analyses (Ibáñez et al., 2006), we decided to
use nuclear introns since these markers show higher levels of
nucleotide variability than other nuclear markers such as exons;
in addition, the amplification of these markers is relatively easy
since the primers can be placed in the adjacent conserved exons
(Creer et al., 2005; Igea et al., 2010).
For this study we amplified for the first time the following five
introns: SLC38A7-8; ABHD11-5; ACOX2-3; COPS7A-4; and ROGDI7, selected from the 224 markers identified from the analysis of the
genomes of five mammal species (Igea et al., 2010). In addition, we
also used the unpublished intron ACPT-4 provided by J. Igea and J.
Castresana. Our selection was based on the suitable genetic
Fig. 1. Map of sampling locations. Symbols represent genetic clades at species level:
indicates the distribution of M. nattereri complex in the region.
Myotis nattereri s.s.; 4 Myotis sp.A; s Myotis escalerai; h Myotis sp.B. The gray area
variability of these introns and the ease with which they can be
amplified and sequenced in our studied group. In order to facilitate
amplification, some of the primers were also appropriately modified when the first sequences from our specimens were obtained.
The primers’ sequences, as well as information on fragment localization and the amplification parameters of the nuclear markers,
are given in Table 1.
All PCR products were purified and sequenced in both directions using an ABI 3100 automated sequencer (PE Biosystems,
Warrington, UK). The sequences were at first aligned and edited
visually using Sequencher 4.5 (Gene Code Corp). For the more complex alignment of the nuclear sequences, we also used the software
ClustalX implemented in MEGA 4 (Tamura et al., 2007).
The discrimination of the alleles of the heterozygote nuclear sequences were solved by direct editing of single base mutation or
indels, comparing the forward and the reverse sequences for the
same locus. For the heterozygous with multiple differences, for
which a direct editing approach is not useful, alleles were solved
using the coalescent-based Bayesian algorithm of the PHASE software (Stephens et al., 2001) implemented in DNAsp version 5.0
(Librado and Rozas, 2009).
The same software was used to evaluate genetic variability calculating the number of haplotypes (h), mutations (g) and segregating sites (S), as well as the haplotype diversity (Hd) and nucleotide
diversity (p) for each marker (Table 1).
2.3. Concatenated phylogenetic analyses
We analyzed mitochondrial markers and nuclear introns via
two independent Bayesian phylogenetic analyses implemented in
MrBayes v.3.1.2 (Ronquist and Huelsenbeck, 2003). For each marker, the best fitting substitution model was selected according to
the Akaike Information Criterion using JModeltest (Posada, 2008)
(Table 1).
The combined sequences of Cytb and ND1 were collapsed to
haplotypes with the software Collapse1.2 (available from http://
darwin.uvigo.es). Bayesian analysis of the mitochondrial haplotypes was performed with three simultaneous runs, each with four
chains, for 107 generations; runs were sampled every 1000 generations and generated 10,000 trees. Stationarity, evaluated during
the analysis with the average standard deviation of split frequencies and then by exploring the likelihood plots of the runs using
Table 1
Information for nuclear introns used in the study. The Ensembl code is given for Myotis lucifugus (ENSMLUG) when available or for human genome (ENSG). For each nuclear
marker the annealing temperature (Ta), the maximum length of the obtained sequence (length) and the substitution model (Mod) are given. Number of variable sites (S),
nucleotide diversity (p), number of haplotypes (h) and haplotype diversity (Hd) are given for the nattereri complex + M. schaubi dataset.
Marker
Ensembl code
Primer code
Sequence (50 –30 )
Ta (°C)
Length (bp)
Mod
S
p
h
Hd
SLC38A7 intron 8
ENSMLUG00000003740
359
TVM + I + G
36
0.016
36
0.978
ENSG00000106077
63
223
K80 + G
11
0.015
10
0.831
ACOX2 intron 3
ENSMLUG00000004929
61
387
TPM1 + I + G
32
0.014
28
0.912
ACPT intron 4
ENSMLUG00000013373
61
261
TrN + G
21
0.014
24
0.923
COPS intron 4
ENSMLUG00000013776
63
628
TrN + G
23
0.008
19
0.856
ROGDI intron 7
ENSMLUG00000014706
RGGCCTRGCYGSCTGCTTCATCTT
TCVGASAGYTTGGCTTGRATGAGGCA
CTGCTCACCAACCTGGTGGAGGT
TTVGGCACRGTCTGCATCTGGGC
CCTSGGCTCDGAGGAGCAGAT
GGGCTGTGHAYCACAAACTCCT
GAYTTTGACCGSACVCTGGAGAG
AGYAGYTCVYGGTATCGRGGACA
TACAGCATYGGRCGRGACATCCA
TCACYTGCTCCTCRATGCCKGACA
CTGATGGAYGCYGTGATGCTGCA
CACGGTGAGGCASAGCTTGTTGA
63
ABHD11 intron 5
AAT-F1
AAT-R1
ABHD11-F1
ABHD11-R1
ACOX2-F1
ACOX2-R1
ACPT-F1
ACPT-R1
COPS-F1
COPS-R1
ROGDI-F1
ROGDI-R1
63
352
K80
14
0.005
11
0.702
TRACER v 1.5 (Rambaut and Drummond, 2007), was reached very
early in the analyses, and likelihood values remained stable
throughout the whole run. The initial 2000 trees were discarded
as burn-in. We also used the ‘‘Are We There Yet?’’ (AWTY, Nylander et al., 2008) tools to compare the split frequencies obtained
across the independent runs of the analysis and to verify the stabilization of posterior probabilities of nodes.
To perform the nuclear phylogenetic reconstruction, we concatenated the six introns in a Bayesian analyses carried out with three
independent runs of five Markov chains each, starting from randomly generated trees; they were run for 3 x 107 generations
and sampled every 1500 generations. Since all the fragments are
non-coding, the whole dataset was partitioned only by genes,
according to the different substitution models suggested by Jmodeltest for each marker. In order to facilitate the convergence of the
runs, the temperature value was set to 0.23 so as to increase the
probability of switching chains. The convergence between runs
was evaluated with the average standard deviation and by exploring the likelihood plots of the runs using TRACER v 1.5 (Rambaut
and Drummond, 2007). The initial 4000 trees generated before
the convergence were discarded as burn-in. The stabilization of
posterior probabilities of nodes was verified by the compare function in AWTY (Nylander et al., 2008).
We decided to use phased introns instead of the genotype data
in the concatenated partitioned analysis because the proportion of
heterozygous nuclear sequences was quite high and due to the fact
that we were unable to determine in advance whether or not the
differences between the two alleles from the same individual were
less important than the differences between different individuals.
The nuclear Bayesian analysis was thus run with two sequences
of the concatenated alleles for each sample. As almost all the samples were heterozygous in at least two loci, the concatenated dataset we used was in fact just one of the possible combinations of
alleles for each individual, and it was impossible to know what
the original genomic arrangement of the alleles was. To test
whether the arbitrariness of the concatenation could influence
the resulting phylogeny we repeated a shorter Bayesian analysis
with ten different concatenation rearrangements (3 runs, 107 generations, sampling every 1000, burn-in determined according to
the number of trees generated before convergence was reached).
We used the same parameters for reconstructing the Bayesian
gene-tree for each individual intron to test whether any of the nuclear genes alone could recover the phylogeny resulting from the
concatenated analyses, or whether any of them supported alternative topologies.
2.4. Testing alternative topologies
In order to check the conflict observed between topologies of
the mitochondrial and nuclear phylogenies, and to check whether
different topologies could be rejected, we evaluated alternative
topologies for both datasets. Maximum likelihood trees with
unconstrained and two alternative constrained topologies were
generated for mitochondrial and partitioned nuclear datasets with
RAxML version 7.0.4 (Stamatakis, 2006) under the GTR + G + I model. The same software also calculated the site-wise log-likelihoods
that were then used to perform the approximately unbiased treeselection test (AU; Shimodaira, 2002) and to conduct the Shimodaira–Hasegawa test (SH; Shimodaira and Hasegawa, 1999) in the
software package CONSEL (Shimodaira and Hasegawa, 2001).
2.5. Clustering analyses
We used the information from the nuclear and mitochondrial
haplotypes present in each sample to further investigate the clustering within the dataset and the assignment of the individuals to
populations. This analysis was performed with the software
STRUCTURAMA (Huelsenbeck and Andolfatto, 2007). Apart from
clustering individuals in K populations, this software uses the Markov chain Monte Carlo (MCMC) method to approximate the posterior probability that an individual is assigned to a specific
population. We performed two types of analyses. First, we ran
the analyses leaving the number of populations and the prior of
the expected number of populations as random variables, following a Dirichlet process, with the shape and scale parameters as default. We then repeated the analysis with the number of
populations fixed a priori to values from 1 to 10. Both analyses
were then repeated with the nuclear markers only, in order to
highlight possible differences in the nuclear clustering signal.
2.6. Species-tree phylogeny
Finally, the results obtained from previous phylogenetic and
clustering analyses were used to define the groups of individuals
to be used as ‘species’ in a multilocus coalescence-based phylogenetic analysis. This analysis in fact needs a priori information
regarding the species delimitation and the species assignation of
the individuals in order to reconstruct the topology of the species
tree. We used the phased nuclear alleles and the mitochondrial sequences in the hierarchical Bayesian analysis *BEAST (Heled and
Drummond, 2010) implemented in the software BEAST v.1.5.4
(Drummond and Rambaut, 2007). We ran three independent
MCMC analyses for 3 x 107 generations and sampled every 1000;
the first 10% were discarded as burn-in. We checked the convergence of the runs and that the effective sample sizes (ESS) were
all above 200 by exploring the likelihood plots using TRACER v
1.5 (Rambaut and Drummond, 2007). Species tree reconstruction
seems to be more sensitive to missing data than concatenation
method (Edwards, 2008). To avoid the potential problems that
the lack of a marker for a sample could cause in the reconstruction
of the species tree, we then used in this case only the samples for
which all the markers were sequenced. Nevertheless, in this analysis and unlike in the concatenated analysis, the arrangement of
the phased alleles for each sample is not relevant because the gene
datasets are analyzed separately.
3. Results
3.1. Sequencing
The mitochondrial ingroup dataset consisted of 34 sequences
with a total length of 1325 base pairs (700 bp for Cytb and
650 bp for ND1). There were 392 parsimony informative sites
and 41 singletons, collapsing to 25 different haplotypes.
Nuclear markers had lengths ranging between 223 bp (ABHD)
and 628 bp (COPS) for a total nuclear dataset of 2210 bp. Complete
information on the length of the fragments, variability and number
of haplotypes for the introns can be found in Table 1.
3.2. Mitochondrial phylogeny
The mitochondrial topology is very well supported (Bayesian
posterior probability P0.99) from the basal split as far as several
terminal branches (Fig. 2A) with evident genetic structure within
the main clades. Mitochondrial phylogeny recovers a deep basal
split and five main clades (Fig. 2A) that, for their geographic distribution and relative relationship, seem to recover the mitochondrial
lineages found by previous studies. One clade with very low variability that consists of three different haplotypes from eight samples from Central and Northern Europe corresponds to the
traditional M. nattereri sensu stricto, as it includes the closest
(A)
(B)
Fig. 2. Concatenated phylogenetic trees, based on partitioned Bayesian inference of two mitochondrial markers (A) and six nuclear introns (B). Numbers on branches indicate
posterior probability in Bayesian analyses (P95%). The first two letters of the samples name refer to the countries of origin: Croatia (HR), France (FR), Germany (GE), Italy (IT),
Montenegro (MN), Morocco (MO), Serbia (SR), Spain (SP), United Kingdom (UK). For more information on the geographical origins of haplotypes, see the Appendix A.
samples to the type locality of this species. A second clade (Clade
A) is closely related to the previous one and only contains haplotypes from the Italian Peninsula and the north of the Iberian Peninsula. The majority of the haplotypes from Iberian samples form
a third differentiated clade that corresponds to Myotis escalerai,
according to Ibáñez et al. (2006). Finally, a forth clade (Clade B) includes only haplotypes from Morocco. The fifth clade corresponds
to M. schaubi. Both M. escalerai and Clade A show also deeper differentiation with secondary well-supported clades.
The monophyly of the Western Paleartic M. nattereri is not supported; in fact two of the ‘nattereri lineages’ (M. escalerai and Clade
B) are more closely related to M. schaubi than to the other nattereri
lineages (M. nattereri s. str. and Clade A).
3.3. Nuclear phylogeny
The phylogeny based on the concatenated nuclear markers supports the monophyly of the same main clades obtained from the
mitochondrial data. The five clades – M. schaubi, M. nattereri s.
str., M. escalerai, Clade A and Clade B – are well supported and all
the haplotypes belong to the same clade as in the mitochondrial
identification. Some branches within these main clades also receive strong support, although no evident structure is recognizable
and they do not correspond to mitochondrial secondary clades
(Fig. 2B). The only discrepancy between the nuclear and mitochondrial topologies is in the position of M. schaubi. While in the mitochondrial reconstruction M. schaubi is monophyletic with the M.
escalerai + Clade B group, in the nuclear phylogeny it is closer related to the other main lineage composed of M. nattereri + Clade
A. Both topologies are very well supported in their respective
analyses.
The alternative concatenation arrangements reveal closely coincident topologies and all recover the same monophyletic lineages,
varying only in the posterior probability of the clades, which indicates that the resulting topology in this case is not affected by the
arbitrariness of the allele concatenation process. The Bayesian posterior probabilities for the monophyly of the five main lineages and
the main nodes in the different phylogenetic analyses are shown in
Table 2.
None of the single nuclear locus provides good-enough resolution of the relationships within the studied group, although all recover the monophyly for some of the lineages (Table 2). The COPS
Table 2
Monophyly support values. The posterior probability values of the mains nodes are given for the concatenated mitochondrial and nuclear topologies, for the first five alternative
concatenation rearrangements of the nuclear alleles, for the gene trees of the singular introns and for the multi-locus species-tree analysis.
Nodes monophyly
M. schaubi
M. escalerai (ES)
Clade B (MB)
M. nattereri (NA)
Clade A (MA)
ES + MB
NA + MA
ES + MB + Mschaubi
NA + MA + Mschaubi
Concatenated
Alternative alleles concatenations
Mt
Nuc
alt1
alt2
alt3
alt4
alt5
Single introns phylogeny
AAT
ABHD
ACOX
ACPT
COPS
ROGDI
Species-tree
1
1
1
1
1
1
1
1
–
1
1
1
1
1
0.95
0.99
–
0.98
1
1
1
1
1
1
1
–
0.98
1
1
1
0.98
1
1
1
–
0.98
1
1
1
1
0.6
0.99
1
–
0.98
1
1
1
1
1
1
1
–
0.98
1
1
1
1
0.65
0.99
1
–
0.98
–
–
–
–
–
–
–
–
–
–
0.99
0.99
–
–
–
–
–
0.51
1
–
0.5
–
–
–
–
–
0.81
–
0.94
–
–
0.98
–
–
–
–
0.93
1
1
–
–
1
1
–
–
1
1
1
–
–
–
–
–
–
0.99
0.99
–
0.91
Table 3
Estimates of population structure. based on two mitochondrial and six nuclear loci (i = number of populations).
i
All markers
Nuclear markers only
Posterior probability
Pr[K = i|X]
1
2
3
4
5
6
7
8
9
Log likelihood
P[X|K = i]
0.0000
0.0000
0.0000
0.0294
0.5896
0.3036
0.0656
0.0100
0.0019
Posterior probability
Pr[K = i|X]
1367.58
1222.84
1185.09
1160.25
1158.98
1162.30
1171.86
1168.69
1167.20
Log likelihood
P[X|K = i]
0.0000
0.0000
0.0000
0.0255
0. 0.5253
0.3298
0.0966
0.0217
0.0011
1158.13
1029.12
998.26
980.02
978.84
982.15
987.83
986.23
984.99
Table 4
Individual alleles for mitochondrial and nuclear markers and population assignment resulted by Structurama analyses.
Haplotypes
SPAV1
SPBA
SPCC
SPCA
SPGU
SPHU
SPLR1
SPMA
SPOU
SPSE
SPTA
SPTE
SPZA
MOAG
MOMK
ITAO
ITRC
ITFG
ITME
ITRA
ITRN
ITVB
SPAV2
SPLR2
HRDU
FRBR
GEHD
MNPD
SRKO
SRBO
GBES
GBCU
Mschaubi A
Mschaubi B
Population clustering
CytB
ND1
AAT
ABHD
ACOX
ACPT
COPS
ROGDI
1
2
1
3
4
5
6
3
6
2
5
5
6
7
8
9
10
11
12
13
9
14
15
16
17
18
18
18
18
18
18
18
19
19
1
2
3
4
5
6
5
7
5
2
5
5
5
8
8
9
10
11
12
13
13
14
15
16
17
18
19
18
18
18
18
18
20
21
1; 2
3; 3
4; 4
5; 6
7; 3
8; 8
4; 9
5; 4
10; 11
4; 9
7; 12
8; 9
7; 7
21; 21
21; 12
13; 14
15; 15
16; 16
17; 18
16; 14
19; 19
13; 13
20; 20
?; ?
22; 23
24; 25
22; 26
27; 28
29; 30
31; 32
33; 34
35; 26
36; 36
37; 37
1; 1
2; 2
2; 2
2; 2
2; 2
3; 3
2; 3
1; 3
2; 2
2; 2
2; 2
2; 2
2; 2
8; 8
8; 8
4; 4
5; 6
6; 6
6; 7
6; 4
6; 4
6; 4
6; 6
6; 6
9; 9
4; 4
4; 4
9; 4
4; 4
9; 9
4; 4
9; 4
10; 10
10; 6
1; 1
2; 1
1; 3
1; 1
1; 4
1; 1
4; 4
1; 1
1; 3
1; 1
1; 1
1; 4
5; 5
14; 14
14; 15
6; 7
8; 9
10; 11
12; 10
11; 11
13; 11
11; 11
11; 11
11; 11
9; 16
17; 18
19; 20
19; 21
16; 16
20; 22
17; 23
24; 25
26; 26
26; 27
1; 2
3; 4
5; 6
3; 6
3; 6
3; 6
3; 6
3; 6
6; 6
3; 6
3; 7
3; 6
3; 6
17; 18
19; 20
8; 9
10; 11
10; 12
8; 8
11; 12
10; 12
13; 14
8; 8
15; 16
21; 22
22; 23
24; 24
22; 25
26; 26
27; 28
22; 25
29; 30
31; 31
32; 32
1; 2
2; 2
2; 2
2; 2
2; 4
2; 2
2; 2
2; 2
1; 2
2; 2
2; 2
2; 2
2; 2
11; 11
11; 11
5; 5
6; 6
7; 8
9; 9
9; 9
7; 7
5; 7
6; 6
10; 10
3; 12
13; 14
5; 15
3; 16
7; 7
3; 3
17; 18
3; 14
19; 19
20; 21
1; 1
2; 2
2; 3
2; 2
2; 2
2; 2
2; 2
2; 2
2; 2
2; 2
2; 2
2; 2
2; 2
9; 9
10; 11
4; 5
6; 6
?; ?
6; 7
6; 8
6; 6
6; 6
8; 8
6; 8
6; 6
6; 6
?; ?
6; 6
6; 12
6; 6
6; 6
6; 6
13; 13
13; 13
I
I
I
I
I
I
I
I
I
I
I
I
I
II
II
III
III
III
III
III
III
III
III
III
IV
IV
IV
IV
IV
IV
IV
IV
V
V
phylogeny is the best resolved of the nuclear markers and yields
strong support for most of the basal nodes. The phylogeny of one
of the introns (ABHD) has a unique clade in which Clade B is more
closely related to M. nattereri + Clade A than to M. escalerai,
although with a very low posterior probability (0.55). No other intron provides support for clades that are incompatible with those
recovered by the mitochondrial and nuclear concatenated topologies. The resulted phylogenetic trees for the single introns are given in the Supplementary Fig. 4.
populations K = 5 (Table 3). Similarly, the marginal likelihood for
the assignments with a fixed number of populations (from 1 to
9) reaches its highest value for K = 5 (Table 3). The same results
were obtained considering the six introns only (Table 3). The
assignment of each individual to one of the five populations is exactly the same in both analyses and recovers the same clustering
generated by the mitochondrial and nuclear phylogeny (Table 4).
With both dataset, however, the clustering with 6 and 7 groups
could not be rejected (posterior probability >0.05%).
3.4. Test of alternative topologies
3.6. Species tree
We performed the SH and AU tree selection tests to test
whether the difference between mitochondrial and nuclear phylogenies is statistically different and whether one or both of the
datasets rejected alternative topologies. The maximum likelihood
trees generated for the partitioned mitochondrial and nuclear concatenated dataset are completely consistent with the Bayesian
ones. First, we investigated the different position of M. schaubi with
respect to the nattereri lineages. The mitochondrial topology ((M.
escalerai, Clade B, M. schaubi), M. nattereri, Clade A) was thus forced
onto the nuclear dataset, while the mitochondrial dataset was constrained with the alternative topology ((M. nattereri, Clade A, M.
schaubi), M. escalerai, Clade B) supported by the nuclear markers.
Both indexes reject the alternative topology for the mitochondrial
dataset (AU p = 0.005, SH p = 0.02), whereas the mitochondrial
topology is not statistically rejected by the nuclear dataset (AU
p = 0.39, SH p = 0.54). Moreover, we forced the monophyly of the
nattereri group with respect to M. schaubi ((M. nattereri, Clade A,
M. escalerai, Clade B), M. schaubi) for both genome datasets to test
whether this hypothesis could be statistically rejected. Again, the
alternative topology is strongly rejected by the mitochondrial dataset (AU p = 0.006, SH p = 0.02), whereas the monophyly of the nattereri lineages is not rejected by the nuclear dataset (AU p = 0.34,
SH p = 0.39).
We used the five groups and the individual assignment indicated by mitochondrial and nuclear phylogenetic trees and by
the population structure analysis as species in the *BEAST analyses.
The resulting tree recovers the relationships between the clades
that is supported by the nuclear tree (Fig. 3), with M. schaubi more
closely related to the M. nattereri + Clade A node, although this result is not that well supported (posterior probability = 0.91). M.
escalerai and the Clade B branch, as well as M. nattereri and the
Clade A node, are, on the other hand, very well supported (posterior probability = 0.99).
3.5. Clustering and individual assignment
The first Structurama analysis for the complete sequences data
set with a random number of populations and no prior information
shows the maximum posterior probability for the number of
4. Discussion
4.1. Overall methods and results
Previous studies have highlighted the existence of well-differentiated mitochondrial lineages within the western Palaearctic region for what had hitherto been considered as a single species
(Galimberti et al., 2010; García-Mudarra et al., 2009; Ibáñez
et al., 2006; Mayer et al., 2007). The main goal of this study was
to use genetic information from different markers and with different approaches to test whether those lineages correspond to existing
independent evolutionary lineages (species) within the
Western Palearctic M. nattereri.
Delimiting closely related species is a particularly difficult issue.
It is very unlikely that closely related species have had time to
reach the evident and congruent separation of several taxonomic
characters (Padial et al., 2010; Shaffer and Thomson, 2007). Moreover, the identification of morphological and ecological differences
M.schaubi
0.91
M.nattereri
0.99
1
Clade A
M.escalerai
0.99
Clade B
M.daubentonii
0.006
Fig. 3.
*
BEAST species tree inferred using both the nuclear and mitochondrial data.
could be misleading in the case of adaptative convergence and
morphological crypticism (as discussed for the genus Myotis in part
1) or impossible in groups in which direct studies are particularly
difficult (Appleton et al., 2004; Burland and Worthington Wilmer,
2001; Weisrock et al., 2010). In this context, a taxonomic approach
of ‘integration by congruence’ (Padial et al., 2010) based on concordant patterns of divergence between several taxonomic properties
(e.g. morphological or ecological differentiation, reproductive isolation, monophyly of the clades) would certainly risk underestimating the real number of species.
On the other hand, within the perspective of the unified species
concept (de Queiroz, 2007), speciation starts at the beginning of
the separation of the lineages and lasts throughout the entire
process of acquiring differentiating characteristics. Therefore, each
these properties could serve as an operational criterion for assessing
the independent evolution of the lineages (e.g. Leaché and Fujita,
2010; Weisrock et al., 2010), while each additional criterion could
serve to confirm the independence of the lineages (de Queiroz,
2007; Ross et al., 2010). This approach is consistent with the
framework of integrative taxonomy ‘by accumulation’ (Padial
et al., 2010).
Reciprocal monophyly is certainly one of these criteria and is
strongly supported for the four clades found in this study, both
for the mitochondrial DNA and for the nuclear DNA. Although concatenating is commonly used to sum the information from multiple markers, support for the nodes could be overestimated in
concatenated methods, especially in the study of closely related
species (Degnan and Rosenberg, 2009). Nevertheless, the strong
support for the obtained topology in the coalescent-based analysis
confirms the robustness of these independent lineages. The results
of the concatenated phylogenetic analyses and the clustering
methods, along with the strong support for the resulting species
tree, all emphasize the same four lineages within the nattereri
dataset and the assignment of the individuals to these clades.
The larger effective population size of the nuclear DNA compared to the mitochondrial DNA and the consequent stronger effect
of the incomplete lineage sorting make it difficult for any single
nuclear marker to recover the species trees (Funk and Omland,
2003; Moore, 1995). Moreover, the slower evolution rate of the nuclear genome hinders each marker from accumulating phylogenetic informative changes since the separation of the lineages.
These two factors probably explain the absence of concordance
and monophyly in the single nuclear gene trees. However, it is
interesting to note that traces of the overall clustering can be retrieved in all the intron phylogenies.
Other than the position of M. schaubi, the genetic signal resulting from the concatenated nuclear analyses is completely congruent with the mitochondrial-based hypothesis. Additional analyses
will be required to clarify whether the incongruence in M. schaubís
sister relationships is the result of real different evolutionary histories of the mitochondrial and nuclear genomes (e.g. ancient mitochondrial introgression) or whether it is just the effect of the
incomplete lineage sorting and an artefact caused by the lack of
resolution in the used markers.
The greater difficulties in the lineage sorting of nuclear markers
and the nature of slower evolution could also explain why the nuclear topology signal is not strong enough to reject alternative
topologies, even if the resulting topology is strongly supported.
The four revealed lineages have been isolated for long enough
for both the mitochondrial DNA and the slower-evolving nuclear
genome to differentiate. Moreover, the nuclear markers we used
permitted us to dismiss the possibility that the mitochondrial
structure could be a result of sex-biased behavior (i.e. strict phylopatry of the females), confirming instead that this structure is
the outcome of the segregation of complete genomes that have
reached evolutionary independence due to long-term population
isolation. Nevertheless, with our genetic data we cannot completely rule out the possibility of gene flow between sister clades,
although, given the clear differentiation, we believe that if gene
flow is occurring between sister clades it will only be recent or very
scarce (or both). The presence of gene flow and occasional hybridization is not a rare feature in sister species and could continue
long after speciation (Eckert and Carstens, 2008). Other analyses,
focusing especially in the contact zone between the lineages and
the use of other markers (such as microsatellites), are necessary
if we are to evaluate the possibility of gene flow being present.
4.2. Conclusions and taxonomic consequences
All the results of our analyses confirm that in the Palearctic region M. nattereri is a paraphyletic clade composed of four well-differentiated lineages at species level. They match the clades
partially found by previous studies: M. nattereri s. str. (indicated
simply as M. nattereri hereafter), M. escalerai (Ibáñez et al., 2006),
Clade A corresponding to Myotis sp. A (Ibáñez et al., 2006; Mayer
et al., 2007) and Clade B corresponding to Myotis sp. B (GarcíaMudarra et al., 2009).
For two of these four species (M. nattereri and M. escalerai) taxonomic descriptions already exist. A proper description according
to the International Code of Zoological Nomenclature, with detailed morphological and ecological data, is still needed for the
two undescribed clades (Myotis sp. A and Myotis sp. B) to settle
the taxonomic situation of the group.
M. nattereri, described in 1817 by Kuhl, with its type locality in
central Germany, is widely distributed in Central and Eastern Europe, but according to our results is absent from the Italian and Iberian Peninsulas (Fig. 1).
M. escalerai was first described in 1904 by A. Cabrera on the basis of morphological characters (especially the insertion of the
patagium in the ankle) that this Spanish zoologist recognized in
specimens from the Mediterranean coast of the Iberian Peninsula
(Cabrera, 1904). Nevertheless, just 10 years later the author himself acknowledged that these findings were not valid for the diagnosis of a new species and rejected the presence of differentiating
morphological characters, thereby indicating that M. escalerai
should only be considered as a junior synonym of M. nattereri (Cabrera, 1914). The genetic results we present here confirm Cabrera’s
initial claim and validate the description of this taxon. M. escalerai
is widely distributed across the Iberian Peninsula and has recently
been found on the northern slopes of the eastern Pyrenees in
France (Evin et al., 2009). Few small morphological characters
seem to distinguish M. escalerai from the other European clades,
M. nattereri and Myotis sp. B, especially in the hair fringing the uropatagium (Ibáñez et al., 2006). Moreover, its strict cave-dwelling
habits and large breeding colonies contrast with the small reproductive colonies found in tree holes that are typical of the other
European nattereri bats (Ibáñez et al., 2006; Mitchel-Jones et al.,
1999).
Myotis sp. B is the only nattereri taxon found in the Maghreb. It
seems to share the same ecological and morphological characters
as M. escalerai. Due to the rarity of this species in the area (Benda
et al., 2004), little information is available either on its ecological
characteristics or its true conservation status.
According to our study, Myotis sp. A, closely related to M. nattereri, is present in the Italian Peninsula and in the north of the Iberian
Peninsula.
The recognition of these new species in the Western Mediterranean region shows that the biodiversity of this well-studied area is
still not completely understood. The importance of the Mediterranean Basin as a biodiversity hotspot and as a refuge of endemisms
calls for further efforts aimed at fully evaluating the cryptic diversity that is still hidden within Mediterranean ecosystems (Myers
896
Table A1
List of the species, localities, GenBank accession numbers for each marker and references of the samples used for the study (EBD: Estación Biológica de Doñana, Seville, Spain; NMP: National Museum (Natural History) of Prague, Czech
Republic).
GenBank accesion numbers
Species
Locality
CytB
ND1
SLC38A; ABHD; ACOX; ACPT; COPS; ROGDI
Source
HRDU
FRBR
GEHD
MNPD
SRKO
SRBO
GBES
GBCU
ITAO
ITRC
ITFG
ITME
ITRA
ITRN
ITVB
SPAV2
SPLR2
SPAV1
SPBA
SPCC
SPCA
SPGU
SPHU
SPLR1
SPMA
SPOU
SPSE
SPTA
SPTE
SPZA
MOAG
MOMK
M.schaubi A
M.schaubi B
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
M.
Dubrovnik, Croatia
Tremblay, France
Heidelberg, Germany
Podgorica, Montenegro
Valjevo, Serbia
Bor district, Serbia
Essex, UK
Cumbria, UK
Aosta, Italy
Reggio Calabria, Italy
Foggia, Italy
Messina, Italy
Ravenna, Italy
Rimini, Italy
Verbania, Italy
Avila, Spain
La Rioja, Spain
Avila, Spain
Badajoz, Spain
Caceres, Spain
Cadiz, Spain
Guadalajara, Spain
Huesca, Spain
La Rioja, Spain
Malaga, Spain
Ourense, Spain
Sevilla, Spain
Tarragona, Spain
Teruel, Spain
Zaragoza, Spain
Agadir, Morocco
Tazouguerte, Morocco
Iran
Iran
JN591504
JN591505
DQ120892
JN591506
JN591507
JN591508
JN591509
JN591510
JN591493
JN591494
JN591495
JN591496
JN591497
JN591498
JN591499
JN591500
JN591501
JN591480
JN591481
JN591482
JN591483
JN591484
JN591485
JN591486
JN591487
JN591488
JN591489
JN591490
JN591491
JN591492
JN591502
JN591503
JN591511
JN591512
JN591537
JN591538
JN591539
JN591540
JN591541
JN591542
JN591543
JN591544
JN591526
JN591527
JN591528
JN591529
JN591530
JN591531
JN591532
JN591533
JN591534
JN591513
JN591514
JN591515
JN591516
JN591517
JN591518
JN591519
JN591520
JN591521
JN591522
JN591523
JN591524
JN591525
JN591535
JN591536
JN591545
JN591546
JN591413;JN591446;JN601529; JN601563;JN601597;JN601631
JN591414;JN591447;JN601530; JN601564;JN601598;JN601632
JN591415;JN591448;JN601531; JN601565;JN601599;JN601633
JN591416;JN591449;JN601532; JN601566;JN601600;JN601634
JN591417;JN591450;JN601533; JN601567;JN601601;JN601635
JN591418;JN591451;JN601534; JN601568;JN601602;JN601636
JN591419;JN591452;JN601535; JN601569;JN601603;JN601637
JN591420;JN591453;JN601536; JN601570;JN601604;JN601638
JN591421;JN591454;JN601537; JN601571;JN601605;JN601639
JN591422;JN591455;JN601538; JN601572;JN601606;JN601640
JN591423;JN591456;JN601539; JN601573;JN601607;JN601641
JN591424;JN591457;JN601540; JN601574;JN601608;JN601642
JN591425;JN591458;JN601541; JN601575;JN601609;JN601643
JN591426;JN591459;JN601542; JN601576;JN601610;JN601644
JN591427;JN591460;JN601543; JN601577;JN601611;JN601645
JN591428;JN591461;JN601544; JN601578;JN601612; –
JN591429;JN591462;JN601545; JN601579;JN601613;JN601647
JN591430;JN591463;JN601546; JN601580;JN601614;JN601648
JN591431;JN591464;JN601547; JN601581;JN601615;JN601646
JN591432;JN591465;JN601548; JN601582;JN601616;JN601649
JN591433;JN591466;JN601549; JN601583;JN601617;JN601650
– ;JN591467;JN601550; JN601584;JN601618;JN601651
JN591434;JN591468;JN601559; JN601593;JN601627;JN601659
JN591435;JN591469;JN601560; JN601594;JN601628;JN601660
JN591436;JN591470;JN601551; JN601587;JN601619;JN601652
JN591437;JN591471;JN601552; JN601588;JN601620;JN601653
JN591438;JN591472;JN601553; JN601589;JN601621; –
JN591439;JN591473;JN601554; JN601590;JN601622;JN601654
JN591440;JN591474;JN601555; JN601591;JN601623;JN601655
JN591441;JN591475;JN601556; JN601592;JN601624;JN601656
JN591442;JN591476;JN601557; JN601585;JN601625;JN601657
JN591443;JN591477;JN601558; JN601586;JN601626;JN601658
JN591444;JN591478;JN601561; JN601595;JN601629;JN601661
JN591445;JN591479;JN601562; JN601596;JN601630;JN601662
I. Pavlinic
C. Jan
U. Häussler
M. Paunovic
M. Paunovic
M. Paunovic
S. Rossiter
S. Rossiter
P. Debernardi;E. Patriarca
EBD
EBD
EBD
J. Altringham
M. Bertozzi
P. Debernardi;E. Patriarca
I. Blazquez
EBD
I. Blazquez
EBD
G. Schreur
EBD
J. de Lucas; O. de Paz
J. T. Alcalde
EBD
EBD
J. T. Alcalde
EBD
C. Flaquer
J. T. Alcalde
D. Trujillo
EBD
EBD
NMP
NMP
nattereri
nattereri
nattereri
nattereri
nattereri
nattereri
nattereri
nattereri
spA
spA
spA
spA
spA
spA
spA
spA
spA
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
escalerai
spB
spB
schaubi
schaubi
I. Salicini et al. / Molecular Phylogenetics and Evolution 61 (2011) 888–898
ID
I. Salicini et al. / Molecular Phylogenetics and Evolution 61 (2011) 888–898
et al., 2000). Furthermore, we must still strive to understand the
ecological and conservation needs of this biodiversity under a scenario of growing losses of many natural habitats mainly due to the
intense anthropic pressure in this fragile area.
Acknowledgments
We are particularly grateful to M. Bertozzi for the help in the
field and to J.L. García-Mudarra in field and lab, as well as to all
the people that helped with the samples collection (see Appendix
A). J. Castresana and J. Igea provided primers before their publication and helped in their optimization and the first steps of the nuclear genome analyses. To S.V. Edwards and the people at the OEBMCZ (University of Harvard, USA) for the insights into multilocus
approach; M. Fujita was particularly helpful with the *BEAST and
STRUCTURAMA analyses. S. Carranza, M. Ruedi, C. Vilá provided
guidance on the project and precious feedbacks on the manuscript.
E. Randi, A. Gonzalez Voyer, J. Castresana and J. gea and two anonymous referees improved the manuscript. To F. Grazioli for the picture of M. nattereri s.l. used in the graphical abstract. To the LEM
staff of the EBD (CSIC). Field work was carried out with the authorization of the regional autonomic governments in Spain and the
‘‘Ministero dell’Ambiente e delle Tutela del Territorio’’ (Number
2008-0012012) in Italy. I. Salicini benefited from a JAE pre-doc fellowship from the Consejo Superior de Investigaciones Cientificas.
The study was funded by the Projects SAF2006-12784-C02-02,
SAF2009-09172 of the Spanish Ministry of Science and Education
and 200430E330Intramural of the CSIC.
Appendix A
See Table A1.
Appendix B. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at doi:10.1016/j.ympev.2011.08.010.
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