f u n g a l b i o l o g y x x x ( 2 0 1 2 ) 1 e7
journal homepage: www.elsevier.com/locate/funbio
Genome wide AFLP markers support cryptic species in
Coniophora (Boletales)
Inger SKREDE*, Tor CARLSEN, Øyvind STENSRUD, H
avard KAUSERUD
Microbial Evolution Research Group (MERG), Department of Biology, University of Oslo, P.O. Box 1066 Blindern, N-0316 Oslo, Norway
article info
abstract
Article history:
Numerous fungal morphospecies include cryptic species that routinely are detected by se-
Received 10 September 2011
quencing a few unlinked DNA loci. However, whether the patterns observed by multi-locus
Received in revised form
sequencing are compatible with genome wide data, such as amplified fragment length
29 February 2012
polymorphisms (AFLPs), is not well known for fungi. In this study we compared the
Accepted 13 April 2012
ability of three DNA loci and AFLP data to discern between cryptic fungal lineages in the
Corresponding Editor:
three morphospecies Coniophora olivacea, Coniophora arida, and Coniophora puteana. The
Joey Spatafora
sequences and the AFLP data were highly congruent in delimiting the morphotaxa into
multiple cryptic species. However, while the DNA sequences indicated introgression or hy-
Keywords:
bridization between some of the cryptic lineages the AFLP data did not. We conclude that
AFLP
as few as three polymorphic DNA loci was sufficient to recognize cryptic lineages within
Coniophora arida
the studied Coniophora taxa. However, based on analyses of a few (three) sequenced loci
Coniophora olivacea
the hybridization could not easily be distinguished from incomplete lineage sorting. Hence,
Coniophora puteana
great caution should be taken when concluding about hybridization based on data from
Hybridization
just a few loci.
Incomplete lineage sorting
ª 2012 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.
Multi-locus sequencing
Introduction
The occurrence of cryptic species is a common phenomenon
in fungi as few morphological characters are available to discriminate among closely related taxa. Although the fruiting
stage in most cases constitutes only a small part of fungi’s
life cycle, fruit body characteristics have often been used
alone to define basidiomycete species. Multi-locus DNA sequencing have for the past decade been used to detect cryptic
lineages within fungal morphological species (Koufopanou
et al. 1997; O’Donnell et al. 2000; Dettman et al. 2003;
Kauserud et al. 2006, 2007a,b; Jargeat et al. 2010; Carlsen et al.
2011). The recognition of the same phylogenetic groups across
multiple gene trees indicates the presence of cryptic species
(genealogical concordance phylogenetic species recognition,
Taylor et al. 2000). Non-congruence of multiple gene trees
may reflect for example recombination, incomplete lineage
sorting or introgression.
It has been debated how many DNA loci and gene trees that
are necessary to recognize cryptic species (Rokas et al. 2003;
Rokas & Carroll 2005). Rokas et al. (2003) suggested that eight
to 20 sequenced loci were necessary to define a cryptic species.
Nevertheless, in a study of yeasts including 106 genes, all clades
were still not supported (Jeffroy et al. 2006). The number of loci
needed to define species will vary based on factors such as time
since divergence, effective population size, substitution rates,
and whether partial or incomplete reproductive barriers exist.
The level of information (number of polymorphisms) per loci
* Corresponding author. Tel.: þ47 22854555; fax: þ47 22854726.
E-mail addresses: [email protected], [email protected], [email protected], [email protected]
1878-6146/$ e see front matter ª 2012 The British Mycological Society. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.funbio.2012.04.009
Please cite this article in press as: Skrede I, et al., Genome wide AFLP markers support cryptic species in Coniophora (Boletales),
Fungal Biology (2012), doi:10.1016/j.funbio.2012.04.009
2
and whether the loci are influenced by selection or evolutionary
constraints might also be important. The comparison to genome wide data such as amplified fragment length polymorphisms (AFLPs) can indicate to what degree a few sequenced
loci can be used to recognize cryptic species. Comparison of
AFLPs and multi-locus sequence data in the basidiomycete
Serpula himantiodes have suggested that AFLPs may detect genetic variation on a finer level than a multi-locus sequences
dataset including only three nuclear markers (Kauserud et al.
2006). From other organisms we know that AFLP has been successfully used in combination with sequences to increase the
s et al.
resolution of phylogenetic relationships (e.g. Despre
2003; Mitter et al. 2011).
The genus Coniophora DC. (Basidiomycota, Boletales) comprises about 20 morphologically defined wood decaying species (Burt 1917; Ginns 1982; Kirk et al. 2008). Numerous
cryptic species were detected in the three widespread morphological species, Coniophora arida (Fr.) P. Karst., Coniophora
olivacea (Fr.) P. Karst., and Coniophora puteana (Schumach.)
P. Karst. using multi-locus DNA sequencing of three DNA
loci (Kauserud et al. 2007a,b). In addition, Kauserud et al.
(2007a,b) suggested hybridization among lineages in C. arida
and C. puteana. However, whether genealogies of only three
nuclear loci are sufficient to distinguish hybridization from incomplete lineage sorting is unknown.
Here we evaluate whether just a few DNA loci are sufficient to detect cryptic species in Coniophora, by comparing
the multi-locus sequence data to genome wide AFLP datasets obtained from the same isolates of C. arida, C. olivacea,
and C. puteana as used in Kauserud et al. (2007a,b). We also
test whether the AFLP data indicate ongoing hybridization
or whether incomplete lineage sorting is a more plausible
explanation for the observed results in Kauserud et al.
(2007a,b).
Material and methods
Samples
We analyzed 23 isolates of Coniophora arida, 20 of Coniophora
olivacea and 48 of Coniophora puteana. All of the isolates have
been analyzed earlier with multi-locus sequencing and
grouped into cryptic lineages (Table 1) in Kauserud et al.
(2007a,b).
AFLP analysis was carried out according to Kauserud et al.
(2006) using DNA extracts previously described in Kauserud
et al. (2007a,b). The five AFLP primer combinations 6-FAMEcoR1-AC Mse1-CA, 6-FAM-EcoR1-AC Mse1-TA, 6-FAM-EcoR1AC Mse1-GA, 6-FAM-EcoR1-AT Mse1-CA, and 6-FAM-EcoR1AT Mse1-GA were analyzed for all samples. The fragments
were analyzed on an ABI 3730 DNA analyzer (Life Technologies, Foster City, USA) at the ABI-lab at the University of
Oslo (http://www.mn.uio.no/bio/english/research/about/infrastructure/abi-lab/index.html). The raw data was visualized, aligned with the internal size standard (GeneScan 500
Rox, Life Technologies), manually controlled and analyzed
in GeneMapper 4.0 (Life Technologies). Fragment in the size
range 50e500 base pairs were scored as present (1) or absent
(0).
I. Skrede et al.
All samples of C. arida and C. olivacea were analyzed twice
for the primer combination 6-FAM-EcoR1-AC Mse1-CA to
check for scoring error and to calculate an error rate according
to Bonin et al. (2004). The scoring error for C. olivacea and
C. arida was 0.022 and 0.025, respectively. Unfortunately, no
samples were rerun for C. puteana or for the other primer combinations, thus no scoring error could be obtained for the total
datasets.
A parsimony tree was constructed in TNT (Goloboff et al.
2008) with the fragments as ‘taxa’ to visualize their differences. AFLP markers that appeared identical in the parsimony
tree were removed before further analyses. One of the major
problems with AFLPs is that each fragment’s identity is unknown and we do not know whether fragments are linked, homologous or independent fragments supporting a group.
Thus, removing potentially linked fragments also automatically removes support for genetic groups. However, as the
five primer combinations used in this study have few nucleotide differences we expected some homologous fragments,
and therefore considered it important to remove all potentially linked fragments.
In total, the 20 samples of C. olivacea resulted in 511 polymorphic loci. After linked loci were removed, the final analyzed dataset had 358 polymorphic AFLP loci. For the 23
samples of C. arida 445 polymorphic AFLP loci were scored. Removal of potentially linked loci resulted in a dataset of 360
polymorphic loci. Of the 48 analyzed individuals of C. puteana
782 polymorphic AFLP loci were scored. After removing linked
markers 589 polymorphic loci were ready for further analyses.
Data analyses
The AFLP datasets were grouped into lineages with the Bayesian model-based clustering method implemented in structure
2.3.1 (Pritchard et al. 2000; Falush et al. 2007; Hubisz et al.
2009). All isolates were treated as dikaryons and we scored
the dataset as recessive alleles. We used the admixture model
with correlated allele frequencies, with 106 Marcov chain Monte
Carlo (MCMC) iterations and a burn-in of 105 iterations, and repeated the analyses ten times for each K. We further analyzed
the structure results using the software CorrSieve, available as
an R package (R Development Core Team 2004; Campana et al.
2011). The number of groups (K ) in the dataset were estimated
based four graphs obtained from CorrSieve. These four graphs
are: 1) the average log likelihood of the data for each K (lnPD).
2) The correlation between runs ( p-value; Campana et al.
2011; Cockram et al. 2008), meaning whether the same results
are obtained for each of the ten replicates for each K (Q matrix
in CorrSieve). 3) Delta K measures of the rate of change in log
likelihood of the data between successive K’s (Evanno et al.
2005). 4) DeltaF compares the Fst among obtained groups for
each K (Campana et al. 2011). After estimating the K with highest statistical support using a combination of the four graphs
explained above, we analyzed each sub-group separately to visualize whether substructure was present within the groups.
The sub-data analyses were run five times for each K, with
205 MCMC iterations and a burn-in of 105 iterations.
Principal coordinate analyses (PCOs) were conducted in
NTSYS-PC (Rohlf 1990) using the similarity index of Dice
(Dice 1945). We chose the latter to prevent any biasing effect
Please cite this article in press as: Skrede I, et al., Genome wide AFLP markers support cryptic species in Coniophora (Boletales),
Fungal Biology (2012), doi:10.1016/j.funbio.2012.04.009
Genome wide AFLP markers support cryptic species
3
Table 1 e Isolates included in the study and their origin. Information of analyzed samples of Coniophora olivacea, C. arida
and C. puteana. Column a indicates which lineage the samples belong to in Kauserud et al. (2007a,b), column b gives the
number of heterozygote sites (from Kauserud et al. 2007a,b) and c the number of AFLP fragments (after correcting for
missing data).
Isolate
MUCL1000
MUCL30744
MUCL31020
MUCL20565
MUCL28146
MUCL30396
MUCL30566
P1
P155
P159
P160
P167
P168
BamEbw-109
59-1913/4
52-1094/8
82-97/3
81
8
76-77/2
CCBAS 524
MUCL30793
NBRC6275
FP 94445
MD189
DAOM194147
# 309
FP100258
FP104403
RLG-5410
S31
DAOM137404
DAOM21055
DAOM31271
DAOM52883
DAOM138703
DAOM137693
DAOM145623
DAOM137908
DAOM137779
DAOM147445
DAOM196038
DAOM198027
DAOM17535
DAOM137697
DAOM216046
DAOM102773
FP105938
Taxon
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
puteana
Origin
a
b
c
Isolate
DEU
BEL
BEL
GBR
NLD
BEL
BEL
DEU
DEU
SWE
SWE
DEU
DEU
DEU
NOR
NOR
NOR
NZL
USA
NOR
CZE
1
1
1
2
1
2
1
1
1
1
1
1
1
2
1
1
1
2
1
1
2
2
1
3
1
2
3
1
1
1
1
1
1
3
2
2
3
1
1
3
1
1
3
1
1
2
3
1
4
1
10
0
2
0
8
3
2
2
0
3
1
0
1
7
1
0
0
0
0
0
3
1
4
8
5
0
11
2
3
0
10
18
0
1
0
0
8
6
0
4
18
0
0
2
18
19
134
142.4
108
110
134
114
125
135
118.5
125.7
137
139
137
97
130.5
137
116
109
139
123.3
100
105
144
97
120
128
75.4
121
132
119
128.5
98
112.5
103
98
106
104
124
113.1
116
105
128
89
108
108
117
100
112
MUCL20566
P154
P161
82e34/3
DAOM145707
DAOM189127
FP100279
FP100334
FP104386
L-9083
L-9712
MD-204
MD-264
Mad4705
RLG-6096
FP105383
FPL1
L-11386
DAOM216045
MUCL45999
MUCL14244
MUCL30714
MUCL40342
MUCL30844
472
MUCL31038
DAOM176418
RLG6926
RLG-6888
FP97436
FP94481
FP90087
FP104424
FP103793
FP100307
DAOM52839
DAOM138685
DAOM137396
FP103970
FP56488
FP105382
DAOM196973
DAOM175779
JPN
USA
USA
CAN
CAN
USA
USA
USA
USA
CAN
CAN
CAN
IND
CAN
CAN
CAN
USA
USA
CAN
RUS
CAN
CAN
CAN
CAN
CAN
USA
of combining monospore cultures with dikaryotic mycelium
in the same analysis. This approach has proven successful
in earlier studies of individuals with varying karyotypes
(Jørgensen et al. 2008; Carlsen et al. 2010).
Phylogenetic analysis of the multi-locus sequence data for
C. puteana from Kauserud et al. (2007a) were performed in phylogenetic analysis using parsimony (PAUP) (Swofford 2002) using only isolates included in the AFLP analyses to make sure
the groupings in the parsimony can be compared to the
AFLP data. For C. olivacea and Coniophora arida all isolates
Taxon
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
C.
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
olivacea
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
arida
Origin
a
b
c
DEU
DEU
CAN
NOR
CAN
AUS
USA
USA
USA
USA
USA
USA
USA
USA
USA
USA
GBR
USA
CAN
CUB
BEL
BEL
ZIM
GBR
NZL
BEL
CAN
USA
USA
USA
USA
USA
USA
USA
USA
CAN
CAN
CAN
USA
USA
USA
CAN
CAN
6
6
3
3
4
5
3
3
2
3
3
1
1
1
3
2
4
2
4
1
1a
3
1b
3
3
3
1a
1b
1a
1b
1a
1a
1b
1b
2
1a
2
1a
1b
1a
1a
2
1a
1
0
3
1
0
1
0
0
3
0
4
0
0
0
0
0
0
0
0
0
0
1
0
4
4
0
8
0
25
0
7
0
2
0
5
5
0
3
0
2
8
5
4
80.8
64
114
109
90
64.3
63
81
102
51.6
94
89
99
89
87.1
93
69.2
79.9
79
97
110
120
126
128
68.2
89
137
81
117.4
110
120
119
128
121.3
109
113
93
112
110.7
113.7
104.8
88.4
118.7
used in Kauserud et al. (2007a) were included in the AFLP
data and the phylogenetic trees were therefore adopted from
Kauserud et al. (2007a).
Results and discussion
AFLPs and multi-locus sequences gave largely a consistent
pattern of dividing the three morphospecies into cryptic species (Figs 1 and 2). However, the number of inferred genetic
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4
I. Skrede et al.
A
B
C
Please cite this article in press as: Skrede I, et al., Genome wide AFLP markers support cryptic species in Coniophora (Boletales),
Fungal Biology (2012), doi:10.1016/j.funbio.2012.04.009
Genome wide AFLP markers support cryptic species
groups from the STRUCTURE analyses was different than the
multi-locus phylogenies (Fig 1; Supp. Fig 1). According to the
structure analysis the optimal delineation of the Coniophora
olivacea AFLP dataset was four genetic groups (Fig 1A; Supp.
Fig 1). The same four groups can easily be recognized in the
principal coordinate analysis (PCO; Fig 2). Based on the
multi-locus DNA phylogeny, Kauserud et al. (2007a) recognized
six cryptic species in C. olivacea. The structure analyses of the
sub-datasets identified five groups, but could not place the isolate representing group 5 in Kauserud et al. (2007a; Fig 1A;
Supp. Fig 2). When K ¼ 6 was applied on the full dataset in
structure, the same six groups was found in the AFLP dataset
as in the multi-locus DNA phylogeny (Fig 1A). These six groups
were also separated in the PCO plot (Fig 2A).
In Coniophora arida, two groups and a third admixed group
were recognized in structure when analyzing the full dataset
(Fig 1B, Supp. Fig 1). After analyzing the sub-datasets separately,
these two groups (both including the admixed group) were separated into altogether five groups (Fig 1B, Supp. Fig 2). These five
were also apparent in the PCO analyses (Fig 2B). The three
groups recognized using K ¼ 2 correspond well with the multilocus sequence phylogeny (Fig 1B). Results from the five groups
recognized using the sub-datasets further subdivide ‘Group1’
into three groups. ‘Group 1a and 1b’ in the multi-locus sequence
phylogeny was not recognized by the AFLP data.
Also a good correspondence was observed in the Coniophora
puteana complex between the AFLP data and the multi-locus
phylogeny (Fig 1C). For K ¼ 2 two groups and a third admixed
group were recognized by structure (Fig 1C). These three
groups separate Phylogenetic Species S1 into two groups,
and group PS2 and PS3 from Kauserud et al. (2007b) into one
group (Fig 1C). Separate structure analyses of the two subgroups (the admixed included in both subgroups) recognize
the admixed group as a separate group, PS2 and PS3 as separate groups and further separate PS2 into two groups (Supp.
Fig 2; Fig 1C). These groups are identical as using K ¼ 6 on
the full dataset (Supp. Fig 1; Fig 1C).
Overall, the main genetic structure observed in Kauserud
et al. (2007b) with five cryptic species in C. arida, six in C. olivacea and three in C. puteana were recognized in the PCO plots
and the structure analyses of the genome wide AFLP data,
but the hierarchy of the groups were somewhat different. It
has been claimed that analyzes of a high number of independent loci are necessary to resolve the relationship among
closely related species because of factors like incomplete lineage sorting (Gee 2003; Rokas et al. 2003). Recent acquirement
of many fungal genomes has opened the possibility to do phylogenomics, and a study of 106 genes in yeast showed that
even when many markers were included, the phylogenetic
signal did not improve with the number of sequences
5
(Jeffroy et al. 2006). Our analyses indicate that a few sequenced
loci, in our case three, might be sufficient to separate between
cryptic species. However, this obviously depends on how recently the cryptic lineages diverged.
Based on the DNA sequence data it was earlier hypothesized that hybridization might have occurred between various
lineages in the C. arida and C. puteana species complexes
(Kauserud et al. 2007a,b). In a few dikaryotic isolates of C. arida
and C. puteana, highly divergent alleles with affinity to different sub-groups or presumed cryptic species co-existed. These
observations were interpreted as introgression of alleles
across lineages. However, the AFLP data did not support
this, as the same putative hybrid isolates neither appeared
as admixed multi-locus AFLP genotypes in the structure analyses nor in the PCO plots. In addition, we did not find more
AFLP fragments in the putative hybrids as expected for hybrids (Table 1). Notably, a recombinant ITS type was detected
in C. puteana (PS3) that combined sequence motifs from different lineages (PS1 and PS3), but this isolate did not constitute
a mixed AFLP genotype as could be expected. In light of the
new genome wide data we find it more likely that the dikaryotic isolates previously interpreted as hybrids includes ancient allelic variants not sorted out by genetic drift
(i.e. incomplete lineage sorting).
In some cases, the structure analyses indicated that other
isolates (not hypothesized as hybrids by the DNA sequences)
were admixed, combining genetic components from different
groups. The isolate representing group 5 in the multi-locus
phylogeny of C. olivacea is one such example. In the K ¼ 4 analysis in structure, this isolate came out as highly admixed
(Fig 1A), and the structure analyses of the sub-dataset could
not place the isolate in either group. However, using other settings of the full dataset (K ¼ 6) this isolate represents a distinct
non-admixed group. Hence, concerning putative admixed
AFLP genotypes, we would argue that results from various
runs of structure using different K’s must be evaluated in
parallel.
Also in other fungi, hybridization has been hypothesized
based on co-occurrence of divergent alleles. Hybridization
was suggested within the Tricholoma scalpturatum complex as
three loci (ITS, gpd, and tef ) were incongruent and a recombinant ITS sequence was observed (Jargeat et al. 2010). Very similar to our observation in C. puteana, a recombinant ITS
sequence was also found between Flammulina rossica and
Flammulina velutipes (Hughes & Petersen 2001). It would be interesting to see whether complimentary data would support if
these specimens represent hybrids or whether incomplete lineage sorting also may account for their observations. Because
many fungi have large population sizes, genetic drift may
work more slowly compared to other organisms. Ancestral
Fig 1 e STRUCTURE analyses of the AFLP datasets, and comparison to the multi-locus sequence datasets results of sTRUCTURE
analyses based on AFLP data from the three species A) Coniophora olivacea, B) C. arida, and C) C. puteana, and comparison the
previously published multi-locus sequence data. The columns to the left represent parsimony trees, as observed in Kauserud
et al. (2007a,b). Branches with bootstrap support above 75 are indicated with black dots. *Indicates isolates suggested to be
hybrids in Kauserud et al. (2007a,b). The mid column is the results from STRUCTURE analyses of the AFLP data using all individuals. The bar graphs represent the probability of assignment of each individual to a group. The column to the far right is
the probability of assignment of an individual after analyzing subsets of the data. Bold text indicates the K with highest
statistical support.
Please cite this article in press as: Skrede I, et al., Genome wide AFLP markers support cryptic species in Coniophora (Boletales),
Fungal Biology (2012), doi:10.1016/j.funbio.2012.04.009
6
I. Skrede et al.
(Conifer root rot) (Spiers & Hopcroft 1994; Brasier 2000;
Newcombe et al. 2000; Linzer et al. 2008; Brasier & Kirk 2010).
Introgression between Heterobasidion annosum into the invasive
Heterobasidion irregulare species in Italy was recently confirmed
using AFLP and multi-locus sequencing (Gonthier et al. 2007;
Gonthier & Garbelotto 2011). Coniophora is also prone to
human-mediated dispersal (through for example international
trade of construction wood) resulting in large geographic distribution size of each cryptic species, but apparently not hybridization. Based on mating studies, the presence of four
intersterility groups has earlier been observed in C. puteana
(Ainsworth & Rayner 1990), which largely corroborate our observations. To our knowledge, similar barriers to gene flow
have not been investigated in C. arida and C. olivacea by use of
mating studies. Some of the presumed cryptic species of
C. arida, C. olivacea, and C. puteana have overlapping geographic
ranges, which would have enabled hybridization (Kauserud
et al. 2007a,b). There are potentially numerous pre- and postzygotic barriers that hinder interspecific gene flow in fungi.
Although interspecific crosses of homokaryons may result in
dikaryons (e.g. Kauserud et al. 2007c), the dikaryons may be unable to fructify, the resulting spores may be sterile or the F1 hybrid genome may not be able to survive due to e.g. lack of
substrate specificity (Garbelotto et al. 1998).
To conclude, our results suggest that a few numbers of sequenced loci can be enough to reveal the presence of cryptic
species in fungi. However, caution should be taken when
interpreting the co-existence of divergent and recombinant
alleles as hybrids. Preferably to reveal hybridization, different
types of DNA markers should be analyzed in parallel, representing both nuclear and mitochondrial DNA.
Acknowledgments
This study is supported by the Research Council of Norway
(NFR186174/V4) and the University of Oslo. Jørn Henrik
Sønstebø and two anonymous reviewers are thanked for helpful comments on the manuscript.
Fig 2 e PCOs of the three species C. olivacea, C. arida and C.
puteana based on AFLP data. The plots are colored based on
the groups recognized in the STRUCTURE analyses for easy
comparison. The dotted lines define the statistically highest
supported groups using the full dataset.
Appendix A. Supplementary material
Supplementary data related to this article can be found
online at doi:10.1016/j.funbio.2012.04.009.
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