Biological Journal of the Linnean Society, 2010, 100, 368–383. With 5 figures
Population and species boundaries in the South
American subterranean rodent Ctenomys
in a dynamic environment
PATRICIA MIROL1*, MABEL D. GIMÉNEZ2,3, JEREMY B. SEARLE3,
CLAUDIO J. BIDAU4 and CHRIS G. FAULKES1
1
School of Biological and Chemical Sciences, Queen Mary, University of London, Mile End Road,
London E1 4NS, UK
2
Facultad de Ciencias Exactas, Químicas y Naturales, Universidad Nacional de Misiones, Félix de
Azara 1552, N3300LQH Posadas, Misiones, Argentina
3
Department of Biology, University of York, PO Box 373, York YO10 5YW, UK
4
Instituto Oswaldo Fiocruz, FIOCRUZ, Río de Janeiro, 21045-900, Brazil
Received 1 September 2009; accepted for publication 18 November 2009
bij_1409
368..383
Subterranean rodents of the genus Ctenomys are an interesting system to assess the effects of habitat instability
on the genetic structure of populations. The perrensi group is a complex of three species (C. roigi, C. perrensi and
C. dorbignyi) and several forms of uncertain taxonomic status, distributed in the vicinity of the Iberá wetland in
Argentina. Because of limited availability of suitable dry habitat, Ctenomys populations are distributed patchily
around a vast mosaic of marshes, swamps and lagoons and become connected or isolated over time, depending
particularly on the precipitation regime. Genetic variation at 16 microsatellite loci in 169 individuals collected in
the area revealed eight clusters of populations which are thought to be evolutionary units, but which do not fit
previous species limits. We interpret this lack of congruence between taxonomy and genetic structure as the result
of a dynamic population structure. Where populations become connected, hybridization is possible. Where
populations become isolated, rapid genetic divergence may occur. In the perrensi group, it appears that both
of these factors disrupt the association between different genetic and morphological characters. The study of
multiple characters is crucial to the understanding of the recent evolutionary history for dynamic systems such as
this. © 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383.
ADDITIONAL KEYWORDS: genetic structure – habitat fragmentation – Iberá wetland – metapopulation –
microsatellites.
INTRODUCTION
Many natural populations are structured, and the
study of their dynamics over space and time has led
to the development of metapopulation approaches
(Levins, 1969; Hanski, 1994, 1998, 1999). Spatially,
natural or human-mediated habitat fragmentation
may restrict species and populations to small patches
of habitat. Over time, entire populations may become
*Corresponding author. E-mail: [email protected]. Current
address: Museo Argentino de Ciencias Naturales, Angel
Gallardo 470, Buenos Aires, Argentina.
368
isolated or connected and may be subject to local
extinction and recolonization.
A very good example of a naturally fragmented
landscape is found in the Iberá wetland in Corrientes,
Argentina. This is one of the largest wetlands in
South America, located between three large rivers,
the Rio Paraná alto, the Rio Paraná medio and the
Rio Uruguay, covering more than 14 000 km2 and
consisting of a vast mosaic of marshes, swamps and
lagoons, of which nearly 60% are permanently inundated (Fig. 1). Altogether, 90% of the Iberá marsh is
dominated by permanent or temporary water bodies,
whereas, in the dry areas, there is a predominance of
sandy soils, sometimes forming extensive hillocks
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
SPECIES AND POPULATION BOUNDARIES IN CTENOMYS
Figure 1. Satellite map showing the sampling localities
of Ctenomys in the Iberá wetland. Localities are circled as
the population clusters obtained using STRUCTURE
(I–VIII), and the lines connecting the clusters show the
grouping from principal component analysis. White
squares, C. dorbignyi; black squares, C. roigi; white
circles, C. perrensi; black circles, Ctenomys sp. Numbers
correspond to the localities as in Table 1.
(Ferrati, Canziani & Ruiz Moreno, 2005). Humanmediated effects, such as the construction of the
Yacyretá dam on the Paraná River (Ferrati & Canziani, 2005), and natural effects, such as El Niño,
have increased water levels. The complex of permanently and seasonally flooded habitats supports a
diverse community of wildlife typical of subtropical
seasonal savannahs.
Species that form wetland communities are
adapted in varying degrees to life in a flooded environment. Surprisingly, subterranean rodents of the
genus Ctenomys are one of the most conspicuous
representatives of the small mammal community of
the Iberá, despite the fact that they are highly dependent on sandy soils, where they can excavate their
burrows (Antinuchi & Busch, 1992; Busch et al.,
2000). Although sandy soils located at the margins of
surface water are suitable, they are, however, fre-
369
quently threatened by flooding. This produces particular dynamics, in which changing rainfall patterns
lead to fluctuating water levels which, in turn,
produce changes in the suitability of habitat for these
rodents. Local populations may then become either
connected or isolated.
The impact of this environmental setting on the
genetic structure of Ctenomys is of particular interest
in the context of the extraordinary genetic variability
shown by the genus. Ctenomys is distributed throughout the southern cone of South America (Reig et al.,
1990) and constitutes the most speciose group of all
subterranean rodents (Reig et al., 1990; Cook & Lessa,
1998; Lessa & Cook, 1998; Lessa, 2000; Slamovits
et al., 2001; Castillo, Cortinas & Lessa, 2005; Bidau,
2006). The genus arose during the late Miocene or
early Pliocene (Reig et al., 1990; Verzi, 2002) and
diversified to 62 living species, that is 45% of all species
of subterranean rodents (Lacey, Patton & Cameron,
2000). This extraordinary rate of divergence has been
attributed to the combined effect of many factors,
including patchy distributions and spatial isolation,
restricted mobility, territoriality, small effective population sizes, socially structured mating systems and,
perhaps crucially, a labile karyotype including a variation in the diploid chromosome number from 10 to 70
(Reig & Kiblisky, 1969; Reig et al., 1990; Ortells, 1995).
A number of species groups within Ctenomys have
been described, based on biogeography, morphology
and karyotype (Reig et al., 1990). Efforts have been
made to resolve the phylogenetic relationships among
these species groups using mitochondrial cytochrome
b sequences and nuclear intron sequences, but uncertainties still remain (Lessa & Cook, 1998; Mascheretti
et al., 2000; Castillo et al., 2005). Here, we consider
the perrensi species group, which occurs in the vicinity of the Iberá and is a complex of three species (C.
roigi, C. perrensi and C. dorbignyi) and several forms
of uncertain taxonomic status (Ctenomys sp.). The
three named species have diploid numbers of 2n = 48,
50 and 70, respectively, whereas Ctenomys sp. exhibits 2n ranging from 40 to 66 (Ortells, 1995; García
et al., 2000; Giménez et al., 2002). The high karyotypic variability of the perrensi group, together with
its habitat-driven population subdivision, strongly
suggests that chromosomal evolution is an ongoing
and recurrent process in these genomes. Although the
monophyly of the group is well established, the
species boundaries within it are not clear (Giménez
et al., 2002). Spatial and temporal variation in environmental conditions across the Iberá makes population boundaries diffuse, even within a metapopulation
context, reflecting a general problem in population
biology (Waples & Gaggiotti, 2006).
In this study, we used microsatellite markers to
examine the genetic structure of the perrensi group of
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
370
P. MIROL ET AL.
populations and species of Ctenomys. Combining our
new data with those previously published on chromosomes and mitochondrial DNA haplotypes, we aimed
to use genetics to define boundaries for species and
populations within the dynamic framework of the
environment they inhabit. The variability in the perrensi group in the Iberá, and the circumstances that
promote it, appear to epitomize what is found in
Ctenomys as a whole. Therefore, this study is an aid
to the understanding of the basis of differentiation in
the genus and highlights Ctenomys as a model system
for the study of speciation in small mammals.
MATERIAL AND METHODS
STUDY AREA, SAMPLE COLLECTION AND
MICROSATELLITE ANALYSIS
A total of 169 specimens of Ctenomys was collected
during 1994–2003 using live traps at 24 sampling
sites in the vicinity of the Iberá (Corrientes, Argentina; Fig. 1). Most localities were sampled during two
consecutive years. In those cases in which the second
collection was conducted more than 2 years later than
the first, an analysis of molecular variance (AMOVA)
between collection years was performed in order to
determine whether the samples could be combined.
Individuals were classified as C. dorbignyi (four sampling sites), C. perrensi (three sampling sites) or C.
roigi (two sampling sites) on the basis of morphology,
karyotype and site of capture in relation to previous
studies (Ortells, Contreras & Reig, 1990; Ortells
& Barrantes, 1994; Ortells, 1995 and references
therein). Other individuals from Corrientes (15 sampling sites) were classified as Ctenomys sp. because
they could not readily be assigned to any of the
named species. Their morphology was similar to C.
perrensi, but their karyotypes varied greatly between
2n = 42 and 65. From the year 2000 onwards, after
the collection of a small tissue biopsy, animals were
released within the same burrow system in which
they had originally been captured.
DNA was extracted from ethanol-preserved tissue
samples (usually tail tip) following a standard digestion with proteinase K, extraction with chloroform–
isoamyl alcohol and precipitation with ethanol
(Sambrook, Fritsch & Maniatis, 1989). All 21 microsatellite loci described for two species of Ctenomys (C.
haigi, Lacey et al. 1999; C. sociabilis, Lacey, 2001)
were amplified. Four loci failed to produce polymerase
chain reaction (PCR) products (Hai3, Hai7, Hai8 and
Hai13) and one, Hai1, was monomorphic in all
samples. Genotypes were obtained for the remaining
16 loci. PCR amplifications were carried out in a
volume of 15 mL containing 30 ng DNA, 0.2 mM of each
primer, 0.2 mM deoxynucleoside triphosphate (dNTP),
1 ¥ Taq buffer, 1.5 mM MgCl2 and 0.75 units of Taq
polymerase (Invitrogen). Cycling consisted of 94 °C for
5 min, followed by 25 cycles of 94 °C for 30 s, annealing temperature for 30 s and 72 °C for 45 s, with a
final extension of 72 °C for 5 min. Annealing temperatures were as follows: Soc1, 54 °C; Soc2, Soc4, Soc5,
Soc6, Soc7 Soc8, Hai4, Hai10, Hai12, 60 °C; Soc3,
62 °C; Hai2, Hai5, Hai9, Hai11, 58 °C; Hai5, 53 °C.
Genotypes were produced by amplifying DNA with
one primer per pair end-labelled with [g32P]-ATP.
Radioactively labelled PCR products were electrophoresed in 6% polyacrylamide, 40% urea denaturing
sequencing gels. Radioactive gels were then exposed
to photographic film for 24–72 h at room temperature.
To determine the allele length in base pairs (bp),
radioactively labelled M13mp18 sequence was used
as a size standard, and amplicons of previously genotyped individuals were run in all gels to facilitate
consistent scoring.
GENETIC
VARIATION
Allele frequencies, observed and expected heterozygosities, deviations from Hardy–Weinberg equilibrium (HWE) and linkage disequilibria between pairs
of loci were calculated using ARLEQUIN 2.0
(Schneider et al., 1997) and GENEPOP 3.1 (Raymond
& Rousset, 1995). These programs were used to calculate standard genetic diversity indices and their
variances and to compute pairwise FST values (Weir &
Cockerham, 1984). Deviations from HWE were tested
for all locus–locality combinations and globally using
the Markov Chain method of Guo & Thompson (1992)
as implemented in GENEPOP. Significance levels
were adjusted using the sequential Bonferroni
method to take into account multiple tests (Rice,
1989). Allelic richness was determined through 1000
resamplings of the data using FSTAT (Goudet, 1995).
Differentiation between localities and groups of localities was also assessed by AMOVA (Excoffier, Smouse
& Quattro, 1992) as implemented in ARLEQUIN. The
statistical significance of FST values was tested by
1000 permutations of genotypes among localities.
This conservative procedure does not assume HWE
and allows for linkage among loci. We tested for
correlation between FST and geographical distance
using Mantel tests implemented in the option
ISOLDE of GENEPOP.
Pairwise FST values were used in a distance matrix
to construct a neighbor-joining (NJ) tree with
NEIGHBOR in PHYLIP 3.5 (Felsenstein, 1991). An
NJ tree was also constructed on the basis of Nei’s D
(Nei, 1972) using the program DISPAN (Ota, 1993).
In addition, individual genotypes were ordinated in
multidimensional space by principal component
analysis (PCA) using PCAGEN (Goudet, 1995). To
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
371
SPECIES AND POPULATION BOUNDARIES IN CTENOMYS
determine the number of nontrivial dimensions, we
used the broken-stick method (Jackson, 1993), as
calculated in PCAGEN.
POPULATION
STRUCTURE
The program STRUCTURE 2.2.3 (Pritchard,
Stephens & Donnelly, 2000, http://pritc.bsd.uchicago.
edu/structure.html) was used to determine genetic
subdivision within a geographical area. For this
study, we were interested in the genetic subdivision of
Ctenomys in the vicinity of the Iberá wetland and, in
particular, how many clusters of populations we could
identify, each defining a separate evolutionary unit.
STRUCTURE was used to provide an estimate of the
number of population clusters (K) present. Before
applying the model to the data, we assumed that all
samples belonged to a hypothetical single population
(option USEPOPINFO = 0). Ten independent runs of
K = 1–25 were performed in STRUCTURE with one
million Markov chain Monte Carlo (MCMC) iterations
and a 0.5 million burn-in period using no prior information and assuming independent allele frequencies
and admixture. From these runs, the optimal K was
determined by the approach of Evanno, Regnaut &
Goudet (2005). In assigning individuals to each of the
K population clusters, we considered that a membership probability q ⱖ 0.9 indicates membership of that
cluster. This value of q is within the range of values
suggested in the literature (Manel, Gaggiotti &
Waples, 2005).
GENE
FLOW AND POPULATION HISTORY
We calculated the relative likelihoods of two models of
population structure with 2MOD (Ciofi et al., 1999).
The software uses an MCMC approach to estimate
the probabilities of obtaining the dataset under a
pure drift model and an immigration drift equilibrium
model. In the first case, allele frequencies are the
result of random changes, whereas, in the second
case, they are the result of a balance between gene
flow and genetic drift. The program also provides an
estimation of F – the probability that the first event
in the genealogy is a coalescence rather than an
immigration or founder event. The MCMC simulation
was run for 100 000 iterations with a 10 000-iteration
burn-in period.
Finally, to detect the genetic signatures of bottlenecks, we used BOTTLENECK (Cornuet & Luikart,
1996) in those populations in which the sample size
was 10 individuals or higher.
cases), and therefore the samples were combined.
Among the 16 loci analysed, there were 5–19 alleles
per locus over all individuals. The mean number of
alleles per locus per locality varied in the range
1–4.31 and the allele richness within each locality
varied in the range 1–1.69 (Table 1). Two sampling
sites were exceptional, Contreras Cué and Tacuarita
(localities 23 and 24 in Fig. 1). In locality 23, all
individuals were homozygous for the 16 loci, whereas,
in locality 24, there was one individual heterozygous
for Hai4 and three heterozygotes for Hai11, all the
rest being homozygotes. Both sampling sites showed
the same allele at all loci except two: Soc1 and Hai11.
Locus Hai6 showed evidence for departure from
HWE in seven sampling sites, and was therefore
excluded in all analyses in which HWE was assumed.
Overall FST was 0.395 (95% CI, 0.365–0.429),
indicating highly significant differentiation among
localities. Pairwise FST comparisons (Table 2) showed
significant differences between all pairs of localities,
except the two sampling sites of C. roigi. Genetic
differentiation was also tested using AMOVA: 39.9%
of the variation was a result of among-locality differences (FST = 0.399, P < 0.0001). Mantel tests for correlation between genetic and geographical distance
were not significant (P > 0.05).
POPULATION
For the STRUCTURE analysis, we ran the program
ten times, both excluding locus Hai6 and including
this locus but accounting for possible null alleles. The
results of both runs were similar, with an optimal
value of K, according to the DK approach of Evanno
et al. (2005), at K = 8 (Fig. 2). There were two smaller
peaks of DK at K = 2 and K = 14. The signal at K = 2
indicates the deep differentiation between Contreras
Cué and Tacuarita and the rest of the sampling sites.
The differences between these two sampling sites and
the rest were substantial and are reflected in all
analyses.
12
10
8
DK 6
4
2
0
1
RESULTS
GENETIC
VARIATION
There were no significant differences between the
years of collection within localities (P > 0.05 in all
STRUCTURE
3
5
7
9
11
13
15
17
19
21
23
25
K
Figure 2. DK values obtained following Evanno et al.
(2005) as a function of K, the number of putative population clusters according to STRUCTURE.
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
372
P. MIROL ET AL.
Table 1. Genetic variability at the 16 microsatellite loci in the localities of Ctenomys sampled
PCA group
Structure
cluster
A
Locality
N
A
AR
VIII
23 Csp Contreras Cué
24 Csp Tacuarita
11
10
1.00
1.12
1.06
1
1.03
1.02
–
–
–
42/72
42/72
B
V
6
7
10
11
12
4
10
10
6
8
2.94
3.62
3.94
2.37
3.31
3.24
1.51
1.56
1.60
1.43
1.56
1.53
1
–
–
–
3
4
50/80
50/80
?
54/80
54/80
C
II
3 Cd Paraje Angostura
4 Cd Mbarigüí
8
4
8 Cr Arroyo Peguajó
9 Cr Costa Mansión
8
4
1.38
1.27
1.32
1.51
1.58
1.54
1.50
1.45
1.49
1.53
2
–
2
–
2
2
–
–
–
–
70/80
70/80
III
2.37
1.94
2.15
2.75
2.81
2.78
2.87
4.06
2.94
3.24
3.44
4.19
3.82
4.31
3.94
3.13
3.31
3.67
3.44
3.37
3.31
3.56
3.42
1.58
1.69
1.64
1.69
1.62
1.65
1.61
1.64
1.56
1.53
1.56
1.61
1.57
10
4
14
2
2
2
1
7
2
3
2
1
8
VI
D
I
Cp 3 de Abril
Cp Rincón de Ambrosio
Csp Saladas 1
Csp Saladas 2
Csp Saladas 3
13 Csp Pago Alegre
14 Csp Mburucuyá
15 Csp Manantiales
6
11
8
1 Cd San Joaquín de Miraflores
2 Cd Sarandicito
7
7
IV
5
16
17
18
Cp Goya
Csp Chavarría
Csp San Roque
Csp Santa Rosa
7
7
3
5
VII
19
20
21
22
Csp
Csp
Csp
Csp
7
7
5
7
Paraje Caimán
San Miguel
Curuzú Laurel
Loreto
np
2n/FNa
48/76
48/76
56/?
51/76
?
70/84
70/80
50/80
58/80
62/80
65/80
46/74
44/72
42/72
42/72
A, mean number of alleles per locus; AR, allelic richness; N, sample size; np, total number of private alleles per locality
with a frequency greater than 0.01; 2n/FNa, diploid number/fundamental autosomal number, according to Giménez et al.
2002; PCA, principal component analysis. Cd, C. dorbignyi; Cp, C. perrensi; Cr, C. roigi; Csp, Ctenomys sp. Locality
numbers follow Figure 1.
All runs at K = 8 produced similar solutions to that
shown in Figure 3, with similar values of cluster
membership for all individuals. The eight population
clusters identified tend to group together localities in
geographical proximity, but do not fit closely to the
species limits (Figs 1 and 3 and Table 1). The localities with C. dorbignyi are separated into two different
clusters (I and II). Likewise, for C. perrensi, sampling
site 5 (Fig. 1) makes up cluster IV, together with three
localities of Ctenomys sp. (16, 17 and 18, Fig. 1),
whereas sites 6 and 7 form cluster V, together with
Ctenomys sp. sites 10, 11 and 12. The Santa Lucía
River separates the two clusters. The two localities of
C. roigi form cluster III. There are three sampling
sites of Ctenomys sp. in cluster VI (13, 14 and 15)
forming a south-west to north-east line along ground
having a higher elevation. Cluster VII is formed by
four other localities of Ctenomys sp. to the east of
cluster VI (19–22). Finally, localities 23 and 24 of
Ctenomys sp., located to the east of the Iberá Basin,
constitute cluster VIII.
The mean membership of the localities to their
respective clusters was quite varied, although always
consistent between runs (Table 3 and Fig. 3). Clusters
II (C. dorbignyi) and VIII (Ctenomys sp.) had the
localities with the highest values of q and no individuals of mixed ancestry. For the rest of the localities, the percentage of individuals with mixed
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
3
4
8
9
5
16
17
18
6
7
10
11
12
13
14
15
19
20
21
22
23
24
II
III
IV
V
VI
VII
VIII
0.55
0.16
0.21
0.18
0.14
0.27
0.32
–
0.26
–
0.72
0.27
0.28
0.31
0.27
–
0.39
0.47
0.35
0.28
–
0.50
0.39
4
0.73
0.26
0.25
0.29
0.22
–
0.43
0.48
0.38
0.35
8
III
0.04*
–
0.41
0.48
0.35
0.31
9
0.47
0.11
0.16
0.13
–
0.26
0.23
0.30
0.35
0.29
0.28
5
IV
0.17
–
0.32
0.29
0.41
0.45
0.32
0.27
16
0.20
0.18
–
0.38
0.35
0.48
0.51
0.38
0.29
17
0.21
0.21
0.24
–
0.40
0.39
0.43
0.46
0.34
0.29
18
0.49
0.19
0.23
–
0.24
0.32
0.38
0.35
0.40
0.37
0.49
0.55
0.34
0.29
6
V
0.20
–
0.26
0.29
0.35
0.35
0.40
0.36
0.41
0.44
0.33
0.27
7
0.24
0.20
–
0.20
0.24
0.28
0.23
0.35
0.33
0.36
0.42
0.34
0.29
10
0.28
0.26
0.21
–
0.30
0.32
0.41
0.41
0.44
0.44
0.50
0.57
0.44
0.33
11
0.24
0.20
0.14
0.19
–
0.20
0.26
0.32
0.31
0.38
0.38
0.45
0.47
0.35
0.29
12
0.54
0.20
–
0.38
0.32
0.30
0.39
0.35
0.24
0.28
0.34
0.34
0.32
0.30
0.32
0.39
0.38
0.28
13
VI
*Only nonsignificant value (Bonferroni corrected, P > 0.005). Intracluster comparisons are shown in bold.
1
2
I
3
1
2
II
I
0.16
–
0.34
0.32
0.28
0.40
0.35
0.22
0.22
0.26
0.30
0.30
0.25
0.34
0.40
0.35
0.29
14
Table 2. Pairwise FST values between sampling localities (above diagonal) and between clusters (below diagonal)
0.25
0.12
–
0.37
0.31
0.34
0.47
0.39
0.29
0.29
0.34
0.37
0.36
0.32
0.41
0.46
0.36
0.32
15
0.47
–
0.27
0.29
0.31
0.34
0.32
0.28
0.46
0.37
0.26
0.26
0.31
0.27
0.41
0.37
0.41
0.48
0.33
0.29
19
VII
0.26
–
0.36
0.33
0.40
0.43
0.38
0.32
0.43
0.39
0.28
0.33
0.36
0.31
0.39
0.35
0.41
0.48
0.37
0.29
20
0.25
0.21
–
0.33
0.31
0.40
0.40
0.36
0.25
0.39
0.31
0.21
0.22
0.28
0.24
0.38
0.36
0.40
0.48
0.36
0.30
21
0.24
0.27
0.19
–
0.28
0.28
0.34
0.35
0.33
0.25
0.36
0.31
0.23
0.24
0.29
0.25
0.35
0.31
0.38
0.44
0.32
0.29
22
–
0.70
0.73
0.71
0.64
0.74
0.61
0.72
0.83
0.68
0.62
0.80
0.72
0.60
0.66
0.84
0.76
0.78
0.83
0.77
0.90
0.74
0.63
23
VIII
0.87
–
0.68
0.69
0.67
0.61
0.69
0.60
0.69
0.79
0.64
0.58
0.75
0.68
0.55
0.62
0.79
0.72
0.74
0.79
0.74
0.86
0.69
0.59
24
SPECIES AND POPULATION BOUNDARIES IN CTENOMYS
373
374
P. MIROL ET AL.
Figure 3. Proportional membership (q) of each individual in the eight population clusters identified by STRUCTURE.
Each animal is identified by a single vertical bar, and each cluster by a colour. On the x-axis are the locality numbers,
following Figure 1 and Table 1. The clusters are indicated under the localities.
ancestry (q < 0.9) varied from 12.5 to 100%. In
general, the second highest probability of membership corresponded to a neighbouring cluster.
PCA showed two axes that were statistically significant according to the broken-stick method (Fig. 4),
which explained 36.4% of the variation across individual genotypes. These axes showed the sampling
sites clustered in four different groups. Groups A and
B coincide with STRUCTURE population clusters
VIII and V, respectively. Group C includes STRUCTURE clusters II, III and VI, and group D includes
STRUCTURE clusters I, IV and VII. As can be seen
from Figure 1, this higher level of clustering closely
reflects geography: group A is east of the Iberá Basin,
group D between the two major water surfaces (the
Iberá Basin and the Santa Lucía River), and groups B
and C west of the Santa Lucía River, although there
is no major geographical barrier separating them.
Regarding species limits, PCA confirmed the close
relationship between C. perrensi and two groups of
Ctenomys sp. (C. perrensi 5 with Ctenomys sp. 16–22,
and C. perrensi 6 and 7 with Ctenomys sp. 10–12,
Fig. 4) on the one hand, and the relationship between
Ctenomys sp. from Mburucuyá (14), Manantiales (15)
and Pago Alegre (13) with C. roigi and C. dorbignyi on
the other.
Figure 5 shows an NJ tree based on pairwise FST
between localities. The NJ tree based on Nei’s D gave
the same result. It is possible to distinguish four main
clades, which are mostly coincident with the groups of
PCA. The main difference is that PCA cluster D
appears to be separated into two groups, one comprising Ctenomys sp. 16–22, and the other comprising C.
dorbignyi 1 and 2 and C. perrensi 5, forming a group
with Ctenomys sp. 23 and 24.
Three different levels of structure were tested by
AMOVA. First, we analysed differences between
species, considering C. roigi, C. dorbignyi, C. perrensi
and Ctenomys sp. Only 4.5% of the variation was
explained by the species level (FCT = 0.045, P > 0.05),
and most of the variation was found among localities
within species and within localities. The result was
also not significant when Ctenomys sp. was excluded
from the analysis and only the three named species
were compared (FCT = 0.085, P > 0.05). Next, we tested
the four groups found with PCA. In this case,
the variation among groups increased to 18.3%
(FCT = 0.183, P < 0.0001). Finally, AMOVA among the
population clusters detected with STRUCTURE analysis showed that 22.8% of the variation was explained
by differences among clusters (FCT = 0.228, P < 0.0001).
In general, FST values within population clusters were
lower than those for the between-cluster comparisons
(Table 2). All localities showed a high degree of differentiation from localities in cluster VIII.
GENE
FLOW AND POPULATION HISTORY
The probabilities of the two models – pure drift
(‘drift’) and immigration drift equilibrium (‘equilibrium’) – were calculated using the coalescent-based
approach of Ciofi & Bruford (1999) at different scales
of analysis. In the first case, all sampling localities in
which the sample size was 10 or more were considered as different units, and the drift model was more
likely than the equilibrium model [P(drift) = 0.70,
Bayes factor = 2.3]. When all eight STRUCTURE
population clusters were considered as units, we
obtained the same result, although more pronounced
[P(drift) = 0.92, Bayes factor = 15.4]. Finally, the probability of both models was calculated within each
cluster. Clear-cut results were obtained for cluster II
[P(equilibrium) = 0.99, Bayes factor = 124.0], cluster
III [P(equilibrium) = 0.93, Bayes factor = 13.9], cluster
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
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SPECIES AND POPULATION BOUNDARIES IN CTENOMYS
Table 3. Mean value of F, the probability that two alleles are identical by descent, for each locality sampled within the
eight STRUCTURE population clusters, and its 95% higher posterior density (HPD) range
F
95% HPD
% MA
2nd C
Cluster I
Cd S. J. Miraflores
Cd Sarandicito
0.38
0.22
0.222–0.426
0.157–0.305
13
71
V
V
Cluster II
Cd P. Angostura
Cd Mbarigüí
0.64
0.61
0.450–0.675
0.470–0.721
0
0
–
–
Cluster III
Cr A. Peguajó
Cr C. Mansión
0.43
0.39
0.300–0.543
0.177–0.451
0
25
–
V
Cluster IV
Cp Goya
Csp Chavarría
Csp San Roque
Csp S. Rosa
0.15
0.26
0.18
0.29
0.120–0.253
0.126–0.322
0.145–0.297
0.208–0.404
57
71
67
60
V, VII
V, VII
V, VII
VII
Cluster V
Cp 3 de Abril
Cp R. Ambrosio
Csp Saladas 1
Csp Saladas 2
Csp Saladas 3
0.30
0.20
0.17
0.47
0.20
0.177–0.407
0.184–0.354
0.125–0.283
0.363–0.627
0.188–0.377
100
20
10
0
0
Cluster VI
Csp P. Alegre
Csp Mburucuyá
Csp Manantiales
0.31
0.19
0.39
0.222–0.433
0.131–0.268
0.247–0.441
0
36
0
–
II, III
–
Cluster VII
Csp P. Caimán
Csp San Miguel
Csp C. Laurel
Csp Loreto
0.34
0.32
0.21
0.33
0.204–0.411
0.235–0.398
0.155–0.374
0.214–0.383
0
25
75
29
–
III
II, IV
IV
Cluster VIII
Csp Contreras Cué
Csp Tacuarita
1.00
0.90
0.984–1.000
0.839–0.903
0
0
IV
IV
VI
–
–
–
–
The percentage of individuals of mixed ancestry (% MA, q < 0.9) according to STRUCTURE is also indicated. The last
column shows the alternative cluster with the second highest probability of ancestry. Cd, C. dorbignyi; Cp, C. perrensi;
Cr, C. roigi; Csp, Ctenomys sp.
VII [P(equilibrium) = 0.91, Bayes factor = 10.3] and
cluster VI [P(drift) = 0.92, Bayes factor = 11.1]. Within
the rest of the clusters, neither the pure drift nor the
immigration drift equilibrium model was substantially more likely. It is possible that, in many of these
cases, there is gene flow between some but not all the
localities included in the same cluster. In general, F
values (the probability of genes being identical by
descent) were high, in agreement with the isolation of
localities, although this varied substantially among
clusters (Table 3). F was particularly high in all
localities of population clusters II and III (consistent
with high mean membership to the cluster and a
small number of individuals with mixed ancestry
according to STRUCTURE analysis) and VIII (Contreras Cué = 1.00 and Tacuarita = 0.90, as expected
given that almost all loci were monomorphic). The
posterior distribution of F overlapped, in most cases,
for populations within clusters, with two main exceptions: in cluster V, the posterior distribution of F in
Saladas 1 (10, Fig. 1) was different from the others,
with a modal value of 0.47 and no individuals of
mixed ancestry, whereas, in cluster VI, the density
plot of F in Mburucuyá (14, Fig. 1) was also distinct
from the other two localities, with a modal value of
0.19 and 36.36% of individuals of mixed ancestry.
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
376
P. MIROL ET AL.
C
Cd (4)
Cd (3)
Csp (14)
Csp (15)
Cr (9)
Cr (8)
Csp (13)
D
Csp (20)
Csp (19)
Csp (17)
Csp (22)
Csp (23)
Cd (2)
Cd (1)
Csp (24)
Cp (5)
A
Csp (21)
Csp (16)
Csp (18)
B
Csp (11)
Cp (6)
Cp (7)
Csp (12)
Csp (10)
Figure 4. Localities grouped according to the two axes in the principal component analysis that were statistically
significant after broken-stick analysis. Numbers as in Figure 1 and Table 1. Letters A–D correspond to the four different
groups defined.
Figure 5. Neighbor-joining tree based on FST pairwise distances between localities. Letters A–D refer to the principal
component analysis groups in Figure 4.
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
SPECIES AND POPULATION BOUNDARIES IN CTENOMYS
Finally, a BOTTLENECK analysis under the SSM
model (Cornuet & Luikart, 1996) in those localities
with a sample size of at least 10 individuals did not
detect a heterozygote deficit, which could be a result
of inbreeding, or heterozygote excess, caused by
recent bottlenecks.
DISCUSSION
GENETIC
DIVERSITY AND POPULATION STRUCTURE
Levels of microsatellite variability found within the
perrensi group are amongst the highest in Ctenomys.
Within the perrensi group, the mean number of alleles
per locus was 13.00, ranging from 5 to 19. Considering each one of the three named species, the mean
numbers of alleles per locus were 3.31 for C. roigi
(range, 1–7), 6.75 for C. perrensi (range, 2–12) and
7.13 for C. dorbignyi (range, 5–11). An estimation of
the number of alleles per locus within each STRUCTURE cluster showed clusters II, III and VIII with
low variability (means between 1.19 and 3.31), with
the rest, including most of the Ctenomys sp. localities,
with a mean number of alleles per locus between 5.00
and 7.75. Up to now, the highest numbers of alleles
per locus have been found in C. minutus from Brazil
(mean, 9.3; range, 5–15; Gava & Freitas, 2004), C.
rionegrensis from Uruguay (mean, 8.3; range, 6–14;
Wlasiuk, Garza & Lessa, 2003) and C. haigi from
Argentina (mean, 7.5; range, 3–13; Lacey, 2001); other
species are less variable (El Jundi & Freitas, 2004;
Cutrera et al., 2006; Fernandez-Stolz, Stolz & Freitas,
2007; Mora, 2008). Despite the overall high levels of
variability in the perrensi group, the mean number of
alleles per locus was much lower when looking at
each particular sampling locality. This difference
between overall and local levels of genetic variability
is probably a reflection of the metapopulation structure, consisting of small populations restricted to
sometimes very small patches of favourable habitat.
If the high overall level of variation was the consequence of the mixing together of well-established
species, we should have observed that a significant
proportion of the variability was explained by the
among-species comparison in AMOVA, and our
results indicated that only 4.5% of the variance was
explained by this component (P > 0.05).
The STRUCTURE analysis showed eight population clusters as the likely subdivision of the dataset
and reflects the putative evolutionary units. These
clusters fit closely to the geographical distribution of
the localities, although they do not correspond to the
species limits. Ctenomys dorbignyi appears to be
divided into two different population clusters: the two
southern localities in cluster I and the two northern
localities in cluster II. Although the splitting of such
377
geographically separated units of the same species
into two different clusters is not particularly surprising, it is extraordinary that the genetic distance
between them, measured by pairwise FST values, was
amongst the highest of all comparisons (0.32,
Table 2). FST values within each of the C. dorbignyi
population clusters were of the same magnitude (0.26
within cluster I and 0.28 within cluster II), even
though the geographical distance between Sarandicito and San Joaquín de Miraflores (cluster I) was
260 km, whereas the distance between Mbarigüí and
Paraje Angostura (cluster II) was an order of magnitude lower, at only 27 km. These two localities fitted
the migration drift equilibrium model. It is possible
that both localities correspond to points of a continuously distributed population, or, at least, to populations connected by favourable habitat patches, as
there are no permanent water surfaces between them.
Mbarigüí is the type locality of C. dorbignyi (Contreras & Contreras, 1984) and individuals from Mbarigüí and Sarandicito differ substantially according to
Contreras & Scolaro (1986) based on morphometrics.
Giménez et al. (2002) also found that the cytochrome
b sequences of specimens from Mbarigüí were distinctive from those of Sarandicito and San Joaquín de
Miraflores, with the Mbarigüí specimens closer to
C. roigi and C. perrensi than to any of the other C.
dorbignyi localities, differing by only one nucleotide of
402 bp from the C. roigi haplotype, and by six nucleotides from its conspecifics.
STRUCTURE population cluster III includes both
sampling localities of C. roigi, which could constitute
a single population: they are very close geographically
(approximately 3.6 km), the habitat between them
shows no major discontinuity, their pairwise FST distance is the only comparison that is not statistically
significant (Table 2), the most probable population
model within the cluster is the migration drift equilibrium, indicating gene flow (Table 3), and they constitute the only remaining area of distribution of the
species. This species is a candidate for conservation
efforts within the genus. However, the population
does not seem to be genetically depauperate or to
have suffered recent bottlenecks. A BOTTLENECK
analysis under the SSM model (Cornuet & Luikart,
1996), considering both localities of C. roigi together,
did not detect heterozygote deficit that could be a
result of inbreeding (Wilcoxon test, P = 0.879) or
heterozygote excess caused by recent bottlenecks
(Wilcoxon test, P = 0.145), and the allelic frequency
distribution was ‘L’-shaped, as expected for equilibrium populations.
The localities of C. perrensi on both sides of the
Santa Lucía River form two different STRUCTURE
population clusters (IV and V), which could indicate
that the river is initiating differentiation within the
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
378
P. MIROL ET AL.
species. The localities of Ctenomys sp. included in
both clusters have diploid numbers ranging from 51
to 66 (Giménez et al., 2002), whereas C. perrensi has
2n = 50. Our results could indicate that the permanent barrier of the Santa Lucía River between the
localities of C. perrensi is allowing incipient differentiation among them, whereas, at the same time, other
populations around the species on either side of the
river are derived from them and in occasional contact
with them, with no interruption of gene flow.
Although chromosomal variation is high, mainly as a
result of the fixation of rearrangements in small
populations that become isolated, individuals could
still retain the possibility to hybridize.
However, it is interesting that, at a higher level of
grouping, as shown by PCA (Fig. 4), clusters IV and V
are not directly related. Instead, cluster IV is related
to clusters I and VII, whereas cluster V constitutes a
separate group or branch (also shown by the NJ tree,
Fig. 5). If this grouping reflects an ancestral relationship, it would mean that populations from the southern part of the distribution of C. dorbignyi are
involved in the origin of populations between the
Santa Lucía River and the Iberá wetland. As stated
before, C. dorbignyi has a diploid number/
fundamental autosomal number (2n/FNa) of 70/80,
and a possible scenario of hybridization with C. perrensi could have been the origin of the intermediate
diploid numbers associated with Ctenomys sp. in
cluster IV. On the other hand, all localities in cluster
VII have lower diploid numbers (42–46, Table 1),
which points to a scenario of a general trend to a
reduction in chromosomal numbers from south to
north. All four localities within this cluster are
located within a line of suitable habitat, and they
could be fitted to a model of migration drift equilibrium; therefore, it is possible that they are connected
through significant migration during favourable climatic periods.
STRUCTURE population cluster VI contains three
localities. The cluster could be fitted to a model of
pure drift (Table 3) and, in PCA, appears to be related
to clusters II and III. Both results together could
indicate that, even when there is currently no connection between the three localities, the existing
populations are the result of hybridization between C.
roigi and the northern populations of C. dorbignyi.
Unfortunately, there is a lack of information on chromosomes for these localities, apart from Mburucuyá
(14, Fig. 1), where the three individuals karyotyped
showed diploid numbers from 51 to 53, and fundamental number 76, the same as C. roigi. On the other
hand, Manantiales (15) and Mburucuyá (14) showed a
cytochrome b haplotype found in C. perrensi, but
differing by only 1 bp from the common haplotype of
C. dorbignyi in Mbarigüí (Giménez et al., 2002).
Finally, STRUCTURE population cluster VIII
deserves particular consideration. The genetic
pattern displayed by the two localities in this cluster
is characteristic of populations recently founded by a
few individuals or having suffered a very recent
bottleneck. Each locality is restricted geographically
to a small area of around 4 km2 (Contreras & Scolaro,
1986) and, although they are only separated by
approximately 12 km, currently there appears to be
no gene flow between them. In Contreras Cué, all
individuals were homozygous for the 16 loci, whereas,
in Tacuarita, there was one individual heterozygous
for Hai4 and three heterozygotes for Hai11, all the
rest being homozygotes. Both sampling sites showed
the same allele in all loci but two. The alleles fixed in
each one were generally common alleles found in
some of the other localities sampled. They showed no
private alleles. From the two possible scenarios –
founder event or bottleneck – we consider the first as
the most probable: a reduction in population size
would have led to a reduction of allele numbers and
heterozygosity at polymorphic loci, but would have
been unlikely to generate an almost complete loss of
variability as shown in these two cases (Garza &
Williamson, 2001; Whitehouse & Harley, 2001;
Fernandez-Stolz et al., 2007; Bergl et al., 2008).
Therefore, the recent foundation of these two isolates
from very few individuals is the most probable explanation of the data. However, the source population is
not clear. If it was located west of the Iberá wetland,
that would imply that the individuals would have
dispersed between 75 and 100 km over the water
surface – the Iberá wetland originated at the end of
the Pleistocene, 10 000 years ago (Stevaux, 2000;
Orfeo, 2005), and these two localities are of a much
more recent origin. However, the wetland features
extensive areas of floating soils (Gantes & Torremorell, 2005) that could have dispersed animals from
western populations.
On the other hand, no Ctenomys populations have
been detected to date south of these localities and east
of the Iberá. Finally, the last possible source for these
two localities from the east is C. torquatus, a species
with a distribution in southern Brazil and Uruguay
(Fernandes et al., 2007). The species is phylogenetically close to the perrensi group (Parada, 2007), and
has 2n = 40–46 (Freitas & Lessa, 1984; Gonçalves,
2007), similar to the 2n = 41–42 described in cluster
VIII. Therefore, populations in the western part of the
species distribution (at the border between Rio
Grande do Sul, Brazil, and Corrientes, Argentina)
could be candidates as source populations. However,
according to Giménez et al. (2002), both localities
Contreras Cué and Tacuarita showed a unique cytochrome b haplotype, identical to the C. roigi haplotype and also found in localities belonging to clusters
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
SPECIES AND POPULATION BOUNDARIES IN CTENOMYS
V, VI and VII. An alignment of this haplotype with the
haplotypes of C. torquatus obtained from the
sequences available in GenBank showed a range of
11–18 bp differences, which suggests that the source
population did not belong to C. torquatus. Furthermore, a recent study based on cytochrome b
sequences showed populations of C. torquatus and
Ctenomys sp. with the same diploid number to be
phylogenetically different. The phylogenetic tree
resulted in a monophyletic clade for all C. torquatus
populations and a different monophyletic clade for all
the perrensi group populations, despite their similarity in chromosomal number (Fernandes et al., 2009).
SPECIES
BOUNDARIES
Rather than fixed entities, ‘species’ should be considered as evolving lineages or evolving populations (Hey
et al., 2003 and references therein). This is particularly
relevant to the case of the Ctenomys species inhabiting
the Iberá wetland, given the complex scenario of
populations and species coming into contact and isolation at different times and the underlying evolutionary
processes triggered by this situation.
Traditionally, three different species have been
described in the area, based on geographical range,
morphology and chromosomes. These pioneer studies
also recognized the existence of a group of populations
that could not easily be assigned to any of the three
species (Ortells, 1995 and references therein). Later,
and based on chromosomes and cytochrome b
sequences from many of the same localities as in the
present work, Giménez et al. (2002) suggested the
existence of two new species (called species a and b).
Species a includes localities 19–22 (cluster VII)
plus localities in cluster VIII. It is characterized by
diploid numbers of 41–47 and fundamental autosomal numbers of 72–74. Regarding cytochrome b
sequences, most of the localities show the haplotype
characteristic of C. roigi, whose 2n/FNa is 48/76.
According to the authors, species b includes localities
10–17 (clusters IV, V and VI), characterized by
2n/FNa of 51–66/76–80 and cytochrome b haplotypes
also found in C. roigi and C. perrensi. All these
localities showed an exceptional chromosomal polymorphism, that would be expected if they were the
result of hybridization between different chromosomal forms (Searle, 1993), and it was postulated that
they could have arisen by extensive hybridization
between C. dorbignyi (2n/FNa = 70/80) and C. perrensi (2n/FNa = 50/80).
Our results indicate a more complex evolutionary
scenario than suggested hitherto. First, there are
clear differences between southern and northern
localities of C. dorbignyi. This result is in agreement
with previous results on morphology and cytochrome
379
b sequences (Contreras & Scolaro, 1986; Giménez
et al., 2002), and we can postulate that, despite conserving chromosomal numbers, C. dorbignyi has two
evolutionarily divergent lineages. Within the genus
Ctenomys, many species show stable and shared
karyotypes, whereas others are characterized by polymorphisms and polytypy. This has been related to the
amplification/deletion of a satellite DNA present in
the genus (Slamovits et al., 2001). Although C. dorbignyi has not been studied from this perspective, it is
possible that this is a species in which chromosomal
rearrangements do not play a role in differentiation.
Ctenomys roigi appears to constitute a compact
entity represented by a unique population. However,
its gene pool is also present in those localities of
cluster VI which are probably the result of hybridization between C. roigi and the northern populations
of C. dorbignyi, and not, as proposed previously
(Giménez et al., 2002), between C. perrensi and C.
dorbignyi.
Ctenomys perrensi also appears to be separated into
two different lineages. The localities of Ctenomys sp.
Saladas 1, 2 and 3 seem to be related directly to the
sampling locality of C. perrensi at the Paraná River
margin. Although animals in Saladas showed higher
diploid numbers than C. perrensi, they had the same
fundamental autosomal number, and the extra chromosomes could be the product of centric fissions.
The Goya locality of C. perrensi, on the other hand,
appears to be directly related to the rest of the sampling sites that are located between the Iberá wetland
and the Santa Lucía River, possibly through hybridization with the southern populations of C. dorbignyi.
The nature of the environment, which produces contractions and expansions of the portion of suitable
habitat during unfavourable/favourable climatic oscillations, results in a metapopulation structure that
promotes the high levels of chromosomal variation of
these populations.
The group of localities in cluster VII appears to be
quite differentiated relative to all other localities
examined, with distinctive diploid numbers, ranging
from 42 to 46. This could constitute another separate
evolutionary lineage. Finally, Contreras Cué and Tacuarita are the result of recent founding events, and
their source population needs to be found with a more
extensive sampling west of the Iberá wetland.
As stated by Hey et al. (2003), sometimes it is
better to present the full picture that research has
revealed, despite its complexity, rather than to simplify artificially. The pattern depicted by different
markers in the perrensi lineages of Ctenomys seems
complex and, in some instances, contradictory. Incongruence between phylogenies based on different
datasets is often the first hint of reticulate events
(Arnold & Meyer, 2006; Giebler & Englbrecht, 2009).
© 2010 The Linnean Society of London, Biological Journal of the Linnean Society, 2010, 100, 368–383
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P. MIROL ET AL.
Indeed, hybridization explains most of the characteristics of the system. Hybridization in animals is now
being recognized as a more common process in generating new forms than previously thought (Machado
et al., 2002; Melo-Ferreira et al., 2005; Putnam,
Scriber & Andolfatto, 2007; Good et al., 2008; Vigfúsdóttir, Pálsson & Ingólfsson, 2008). In a fluctuating
environment, such as the Ibera wetland, the populations are not isolated for the time span needed for
allopatric speciation to occur, and even when they
accumulate differences during the isolation period,
these are not sufficient to impede hybridization when
they come into contact. In this case, a lack of a tie-up
between the species and the genetic structure is
expected, as well as a lack of congruence among
different genetic characteristics. Chromosomal characters will not necessarily change during a period of
isolation, but microsatellites would be expected to
diverge rapidly. This could explain the finding of
different populations of the same species, such as C.
dorbignyi and C. perrensi, in different clades based on
microsatellite frequencies, even when they share
diploid numbers. On the other hand, the grouping of
some localities of C. perrensi with localities of
Ctenomys sp. in close geographical proximity based on
microsatellite results could be explained by recent
hybridization between these forms. They share
alleles, as a result of hybridization, and this outweighs any association between C. perrensi populations isolated for a longer period of time. The finding
of one individual of Ctenomys sp. in Saladas 1 (locality 10, Table 1) with the characteristic D-loop haplotype of C. perrensi from locality 3 de Abril (6, Table 1)
(M.J. Gómez Fernández, Museo Argentino de Ciencias
Naturales, Buenos Aires, Argentina et al., unpubl.
data) supports this hypothesis. It could be argued
that the significant values of FST found among pairs of
localities is evidence against ongoing gene flow or
hybridization. However, significant values of differentiation can be obtained even when populations show
overlapping allelic distributions, as was shown in
species of the Hawaiian silversword Dubatia (Friar,
Cruse-Sanders & McGlaughlin, 2007).
Mitochondrial DNA, on the other hand, shows an
erratic sharing of haplotypes among geographically
distant localities. The phylogenetic tree reconstructed
using cytochrome b (Giménez et al., 2002) does not
show species as reciprocally monophyletic. In rapidly
radiating taxa, such as Ctenomys radiating explosively
over the last 1.8 million years, the chance of incomplete
lineage sorting increases and generates complex patterns of admixture, making the recognition of species
boundaries difficult. There are many examples in the
literature in which incomplete lineage sorting obscures
phylogenetic relationships (see, for example, Belfiore,
Liu & Moritz, 2008; Geraldes et al., 2008; Pinho,
Harris & Ferrand, 2008; Chen, Bi & Fu, 2009; Rabosky
et al., 2009). Although it is usually difficult to distinguish between this phenomenon and ongoing gene
flow, the contradictory results obtained with microsatellites and mitochondrial DNA indicate that, in the
perrensi lineages, the sharing of cytochrome b haplotypes is a result of the maintenance of the ancestral
condition and not of gene flow. Other factors that could
add to this lack of congruence are differences in home
range between sexes and male-mediated dispersion,
which are characteristic of the species of the genus
(Malizia & Busch, 1991; Cutrera et al., 2006). An
example of how male-mediated dispersion and/or sex
differences in home ranges produce contradictory mitochondrial and microsatellite results can be found in the
Iberian lizard Lacerta schreiberi (Godinho, Crespo &
Ferrand, 2008).
The nature of the perrensi lineages, with their
patterns of divergence within species without karyotypic change, and hybridization between species
accompanied by massive chromosomal variation, is
better understood within this framework of alternating periods of isolation and hybridization. The evolutionary signature of the perrensi group of Ctenomys
seems to be like ice ages speeded up: a dynamic system,
going through phases of isolation when genetic drift is
the process shaping populations and leading to the
fixation of chromosomal rearrangements, and phases
of gene flow between differentiated populations that
have not reached reproductive isolation.
ACKNOWLEDGEMENTS
We are grateful to all the people who have helped us
in collecting Ctenomys, especially local people in Corrientes who have given permission to work on their
land. This study was supported by a Central Research
Fund grant of the University of London to CGF and
PMM, the Wellcome Trust to MDG and JBS and the
University of York to JBS. MDG is grateful to the
Facultad de Ciencias Exactas, Químicas y Naturales
(UNaM) for helping with field trip expenses. PMM
acknowledges the support from Consejo Nacional de
Investigación, Ciencia y Técnica, Argentina, and CJB
acknowledges the continuous support of Fundaçao
Oswaldo Cruz, Conselho Nacional de Desenvolvimiento Científico e Tecnlógico and Fundaçao de
Amparo a Pesquisa do Estado de Rio de Janeiro for
financing his scientific activities in Brazil.
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SUPPORTING INFORMATION
Additional Supporting Information may be found in the online version of this article:
Table S1. Genetic variability in the sampling localities of Ctenomys analysed. A, number of alleles per locus per
locality; AR, allele richness; At, total number of alleles per locus; He, expected heterozygosity; Ho, observed
heterozygosity. Departure from Hardy–Weinberg equilibrium, following Bonferroni correction, is indicated by an
asterisk.
Please note: Wiley-Blackwell are not responsible for the content or functionality of any supporting materials
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