Journal of Heredity Advance Access published November 6, 2012
Journal of Heredity
doi:10.1093/jhered/ess092
© The American Genetic Association. 2012. All rights reserved.
For permissions, please email: [email protected].
A Forest Butterfly in Sahara Desert
Oases: Isolation Does Not Matter
Jan Christian Habel, Martin Husemann, Thomas Schmitt, Leonardo Dapporto,
Dennis Rödder, and Sofie Vandewoestijne
Address correspondence to Jan Christian Habel, Natural History Museum Luxembourg, 25, rue Münster, L-2160
Luxembourg, or e-mail: [email protected].
Abstract
Numerous studies addressing the impact of habitat fragmentation on genetic diversity have been performed. In this study,
we analyze the effects of a seemingly nonpermeable matrix on the population structure of the forest-dwelling butterfly
Pararge aegeria in geographically isolated oases at the northern margin of the Sahara desert using microsatellites, morphological characters, and species distribution modeling. Results from all analyses are mostly congruent and reveal 1) a split between
European and North African populations, 2) rather low divergence between populations from the eastern and western part of
North Africa (Morocco vs. Tunisia), 3) a lack of differentiation between the oasis and Atlas Mountain populations, 4) as well
as among the oasis populations, and 5) no reduction of genetic variability in oasis populations. However, one exception to this
general trend resulted from the analyses of wing shape; wings of butterflies from oases are more elongated compared with
those from the other habitats. This pattern of phenotypic divergence may suggest a recent colonization of the oasis habitats
by individuals, which might be accompanied by a rather dispersive behavior. Species distribution modeling suggests a fairly
recent reexpansion of the species’ climatic niche starting in the Holocene at about 6000 before present. The combined results
indicate a rather recent colonization of the oases by highly mobile individuals from genetically diverse founder populations.
The colonization was likely followed by the expansion and persistence of these founder populations under relatively stable
environmental conditions. This, together with low rates of gene flow, likely prevented differentiation of populations via drift
and led to the maintenance of high genetic diversity.
Key words: differentiation, genetic diversity, genitalia, geometric morphometrics, habitat isolation, microsatellites, Pararge aegeria, species
distribution modeling, wing morphology
Anthropogenic habitat fragmentation is one of the major
drivers of biodiversity loss. Fragmentation causes the decline
of habitat size and limits gene flow between populations in
isolated habitat patches. The effects of fragmentation differ depending on the location within the species’ distribution, patch size, and distances among adjacent patches. Small
populations, with large geographic distances among them,
as well as populations at the distribution edge suffer most
severely and often display strong population divergence
and reduced genetic diversity (Lesica and Allendorf 1995;
Allendorf and Luikart 2007). Reduced genetic diversity in
isolated populations has been shown to negatively affect
individual fitness and thus population viability (Saccheri
et al. 1998; Reed and Frankham 2003; Melbourne and
Hastings 2008; Vandewoestijne et al. 2008).
Most research on habitat fragmentation in terrestrial
systems has been performed in regions with strong human
impact (Keller et al. 2004, 2005). These systems are usually
characterized by semipermeable and highly heterogeneous
landscapes, which often include stepping stones and corridors
that aid in maintaining at least low levels of gene flow (Cook
et al. 2002; Hanski and Gaggiotti 2004). In contrast, many
naturally fragmented systems consist of habitat patches
embedded in a matrix of highly unsuitable environment.
Habitat patches in these systems are often separated by much
larger geographic distances and have been isolated over
1
Downloaded from http://jhered.oxfordjournals.org/ at Technical University Munich on December 4, 2012
From the Natural History Museum Luxembourg, Invertebrate Biology, 2160 Luxembourg, Luxembourg (Habel); Biology
Department, Baylor University, Waco, TX 76798 (Husemann); Department of Biogeography, Trier University, 54296 Trier,
Germany (Schmitt); Istituto Comprensivo Materna Elementare Media “Convenevole da Prato”, via 1º Maggio 40, 59100
Prato, Italy (Dapporto); Zoologisches Forschungsmuseum Alexander Koenig, Adenauerallee 160, 53113 Bonn, Germany
(Rödder); and Biodiversity Research Centre, Earth and Life Institute, Université catholique de Louvain (UCL), Croix du Sud
4, B-1348 Louvain-la-Neuve, Belgium (Vandewoestijne).
Journal of Heredity 
2
et al. 2011b; Dincǎ et al. 2011, but see Dapporto et al. 2011b).
Genitalia shape is involved in reproductive isolation and
therefore can be under strong sexual selection (discussed in
Shapiro and Porter 1989). In contrast, wing shape is more
likely influenced by natural selection (Berwaerts et al. 2002).
Variations in these traits may therefore mirror contrasting
behavior and life-histories (Van Dyck et al. 1997; Berwaerts
et al. 2002) and are used in this study to detect different past
and present selection regimes (Dennis and Shreeve 1989;
Joyce et al. 2009). In addition to genetic and morphometric
analyses, we employ a modeling approach to understand past
and present species distribution (and possible range contraction and expansion events). Specifically, we aim to answer
the following questions using a combination of the abovementioned complementary analyses:
(i)Did geographical isolation and variation in ecological
conditions lead to the evolution of distinct genetic and
morphologic lineages?
(ii)Are oasis populations genetically and morphologically
differentiated and impoverished due to their inherently
limited size and isolation?
(iii)When were the Saharan oases colonized by P. aegeria? Did
climatic conditions favor this invasion process?
Materials and Methods
Sampling
A total of 441 P. aegeria individuals were collected in North
Africa (Morocco, Tunisia) and Europe between 2006 and
2010. The sampled individuals represent 14 oasis populations, 5 populations from the Atlas Mountains, 2 populations
from Tunisia, 2 populations from Belgium, and 3 populations
from France (Figure 1). Genetic data were obtained for 24 of
these populations. Details about sampling locations are given
in Table 1. Immediately after netting, individuals were killed
and stored in absolute ethanol or dried in the sun and stored
in envelopes until further processing. Details on samples
from Europe (excluding Le Muy) are given in Vandewoestijne
and Van Dyck (2011).
Molecular Analyses
DNA was isolated from the thorax using the DNeasy Blood
and Tissue kit (Qiagen, Hilden, Germany) following the
manufacturer’s protocol for tissue samples. Six polymorphic
microsatellite loci (Helsen et al. 2010) were amplified. These
microsatellites were already successfully used previously
for P. aegeria (Vandewoestijne and Van Dyck 2010) and
show high levels of polymorphism. Most of these loci are
characterized by insertions and deletions of single base pairs
in the flanking regions; these substitutions of single base
pairs lead to deviations from the repeat motif (occurrence
of 1-nucleotide steps, supplementary appendix 1 online) as
described in Helsen et al. (2010). Sequencing studies showed
that such deviations from the microsatellite motif occur at
nonnegligible rates in the flanking regions of microsatellites
(Angers and Bernatchez 1997; Grimaldi and Crouau-Roy
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long time scales. Although being of particular interest for
the study of island biogeography, very few studies on the
effects of fragmentation have been performed in naturally
fragmented landscapes.
A unique and well-suited model system to analyze naturally fragmented populations is represented by forest species
living in oases within deserts. These rare conditions can be
found in the oases of the northwestern Sahara desert. These
oases are strictly isolated from each other by large areas of
stone desert since the final desiccation of the Sahara approximately 6000 years ago (de Noblet-Ducoudré and Prentice
2000). Forest-like habitats within these oases are lined up
along several vadis (rivers only temporarily carrying water)
extending from the southern High Atlas Mountains of
Morocco into the Sahara desert.
Butterfly species of the subfamily Satyrinae are good
models to study evolutionary processes, including effects
of population isolation. Many insular and geographically
restricted endemics are known for this subfamily (Cesaroni
et al. 1994; Dapporto 2010; Schmitt and Besold 2010;
Thomson 2011). The Speckled Wood butterfly Pararge
aegeria (Linnaeus 1758) is a particularly suitable model species to study the impact of population isolation because it
is restricted to shady and moist forest habitats, whereas arid
conditions are completely unfavorable (Wiklund and Persson
1983; Shreeve 1984; Merckx et al. 2003; Stevens 2004).
Pararge aegeria is widespread over the Western Palaearctic
region, ranging from North Africa to Scandinavia and from
Iberia to the Urals. The species is common in the Central and
Southern European broad-leaved forests, but also occurs in
fragmented agricultural landscapes along hedgerows (Dover
and Sparks 2000; Merckx et al. 2003; Vandewoestijne and
Van Dyck 2010). Populations of the Western Mediterranean
area show significant genotypic and phenotypic variation
on large as well as regional scales (Weingartner et al. 2006;
Dapporto 2010). South of the Atlas Mountains, the butterfly species is strictly confined to oases where it occurs in
shady areas with a dense cover of olive trees and date palms
(personal observations).
We sampled 14 oasis populations of P. aegeria stretching
250 km along an east–west transect from the Todra Valley to
the Guir River vadi in the transitional zone between the High
Atlas and the northern Sahara. Additionally, 5 populations
from the Atlas Mountains, 2 from Tunisia, and 5 from Europe
(3 from France and 2 from Belgium) were analyzed (Table 1).
We used 6 microsatellite loci as our genetic marker system to
calculate parameters of genetic differentiation, genetic variability and to generate estimates of effective population sizes
(Ne). This noncoding marker system is highly polymorphic
and fast evolving and hence is well suited for intraspecific
analyses of genetic divergence (Selkoe and Toonen 2006). In
contrast, the morphological characters (wing shape and genitalia phenotypes) analyzed here are known to be frequently
affected by natural (and sexual) selection and therefore might
provide information on local selective pressures. Genitalia
can evolve relatively independently from environmental factors and thus are often highly correlated with neutral genetic
divergence (Cesaroni et al. 1994; Dapporto 2010; Dapporto
Habel et al. • Low Differentiation Among Oasis Populations of Butterflies
Table 1 Sampling localities of Pararge aegeria. Given are the name of the sampling location, with gps coordinates, and numbers of
collected individuals analyzed for microsatellites (µsats), wing shape (W) and genitalia morphology (G), as well as sampling dates
Oases (Morocco)
Tunisia
Europe
Site
Gps coordinates
N (µsats)
N (W)
N (G)
Sampling dates
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
31°32ʹN; 5°34ʹW
31°36ʹN; 4°57ʹW
31°41ʹN; 4°56ʹW
31°48ʹN; 4°57ʹW
31°53ʹN; 4°55ʹW
31°49ʹN; 4°36ʹW
31°58ʹN; 4°27ʹW
32°04ʹN; 4°23ʹW
31°51ʹN; 4°16ʹW
31°56ʹN; 3°36ʹW
31°57ʹN; 3°32ʹW
31°38ʹN; 4°12ʹW
31°33ʹN; 4°42ʹW
31°33ʹN; 4°50ʹW
31°12ʹN; 7°58ʹW
31°14ʹN; 7°37ʹW
32°04ʹN; 6°40ʹW
32°51ʹN; 5°56ʹW
33°27ʹN; 5°13ʹW
36°43ʹN; 9°11ʹE
36°46ʹN; 8°41ʹE
50°39ʹN; 4°53ʹE
50°48ʹN; 4°43ʹE
46°22ʹN; 5°06ʹE
46°11ʹN; 4°56ʹE
43°28ʹN; 6°34ʹE
21
30
25
24
22
28
29
30
23
—
21
21
30
24
26
17
11
10
9
20
20
20
20
20
20
—
15
29
25
23
20
25
30
30
17
25
21
—
30
20
—
—
—
—
—
31
25
20
—
—
—
—
13
24
19
18
13
21
24
22
15
15
18
—
28
34
—
—
—
—
12
28
21
20
—
—
—
20
7-3-2009; 14-3-2010
13-3-2010
13-3-2010
21-5-2008; 6-3-2010
6-3-2010
20-5-2008; 7-3-2010
7-3-2010
10-3-2010
22-5-2008; 7-3-2010
21-5-2008; 8-3-2010
21-5-2008; 8-3-2010
23-5-2008; 10-3-2010
12-3-2010
12-3-2010
13-5-2008
14-5-2008
16-5-2008
9-5-2008
18-5-2008
5-2010
5-2010
8-2007
8-2007
8-2007
8-2007
4-2006
1997) and were frequently observed for microsatellites
in Lepidoptera. This was explained by high similarities in
flanking regions between different microsatellites within the
same species and/or the lack of conserved flanking regions
(Meglécz et al. 2007).
The forward primers were labelled with the fluorescent
dyes 6FAM, 6TAMN, HEX (IDTdna) using multiplex PCR
following a modified protocol provided by Helsen et al.
(2010). Although 8 primer pairs were originally included
in multiplex PCR, only 6 amplified successfully for North
African samples. Multiplex amplifications were performed
with QIAGEN Multiplex PCR kit (Qiagen) using a Veriti
Thermal Cycler (Applied Biosystems). PCR conditions were
as follows: 95 °C for 15 min, followed by 32 cycles (94 °C for
30 s, 60 °C for 90 s, 72 °C for 90 s) and 72 °C for 30 min, terminating at 25 °C. Amplified fragments were visualized with
an ABI PRISM 3100 Genetic Analyser (Applied Biosystems).
Allele sizes were scored against the internal standard ROX400SD (Applied Biosystems) using GeneMapper 3.5 (Applied
Biosystems).
The program Micro-Checker (Van Oosterhout et al.
2004) was used to test for distortion of the genotypic data
as a result of stutter bands, large allele dropouts, or null
alleles (Selkoe and Toonen 2006). Allelic richness (AR) was
calculated using FSTAT (Goudet 1995). Nonhierarchical and
hierarchical analyses of genetic variance (analyses of molecular variance [AMOVAs]), calculations of observed (Ho) and
expected (He) heterozygosities, tests for Hardy–Weinberg
equilibrium, linkage disequilibrium, and isolation-by-distance
analyses were performed with Arlequin 3.1 (Excoffier et al.
2005).
AMOVAs were carried out using 2 different approaches:
the conventional F-statistics based on allele frequencies
and the infinite alleles model and the microsatellite-specific
R-statistics based on allele lengths and the stepwise mutation
model (Slatkin 1995). The latter assumes that alleles similar
in length are more likely to share a more recent common
ancestor. Both approaches comprised 3 hierarchical levels:
within populations, among populations within regions, and
among regions. Differentiation between populations was
estimated as pairwise RST values.
Isolation-by-distance was tested using Mantel tests performed on matrices of pairwise geographic distances (km)
and linearized pairwise RST values, that is, RST/(1 − RST).
Pairwise geographic distances were calculated with GeoDist
(Heidenreich A, unpublished data). Mantel tests were performed using the isolation-by-distance web service version
3.23 (Jensen et al. 2005); hereby, isolation-by-distance tests
were performed on different geographical units to adjust
for swamping effects large geographic distances can have
on the method. Mantel tests were performed using 30 000
randomizations. Isolation by distance is one of the most
common patterns observed in natural populations not disrupted by natural or anthropogenic barriers and hence can
be seen as a null model in studies of population divergence.
If genetic differences among specific populations are higher
than the isolation-by-distance model predicts barriers to gene
flow often can be invoked. If genetic differentiation is less
3
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Atlas Mountains
(Morocco)
Name of locality
Gorges du Todra
South Goulmima
Gaouz
Taltfraout
Tardirhoust
Tarda
Errachidia
Ifri
Source Bleu de Meski
Boudnib
Sahli
Zrigat
Ba Touroug
Mellaab
Imlil
Vallée d’Ourika
Cascade d’Ouzout
Kasba Tadla
Azrou
Ain Draham
Béjà
Gerompont (Belgium)
Meerdaal (Belgium)
Corgens (France)
Bois Fevrier (France)
Le Muy, Var (France)
Journal of Heredity 
pronounced than predicted, the effects of genetic drift are
mediated by large population sizes or gene flow.
Analyses of population structure were performed using
a Bayesian approach as implemented in STRUCTURE 3.1
(Pritchard et al. 2000). For each run, burn-in and simulation
lengths were 100 000 and 300 000, respectively. The calculations were run under the admixture model with correlated
gene frequencies. To obtain information about recent bottlenecks, each sample was tested using the Wilcoxon test as
implemented in BOTTLENECK (Cornuet and Luikart 1997).
This test is based on the expectation that a recent reduction
in effective population size leads to a reduction in both allele
numbers and gene diversity. As allele number decreases faster
than gene diversity in a recently bottlenecked population, the
observed gene diversity is higher than the expected equilibrium gene diversity (Luikart and Cornuet 1998).
Additionally, effective population sizes Ne were estimated
with ONeSAMP (Tallmon et al. 2008). Ne is an important
population genetic parameter because it indicates a population’s potential to evolve via random processes. Generally,
populations with small Ne are more susceptible to genetic
drift; consequently geographically separated populations
4
with small Ne will differentiate faster and loose rare alleles
quicker than larger populations.
Wing Shape Morphometrics
Digital images of the ventral side of the forewings of 386
P. aegeria individuals from 16 populations (13 oases, 2 from
Tunisia, and 1 from Belgium; Table 1) were taken with a
digital camera (Canon M1, 50-mm lens) under standardized
light conditions (Häuser et al. 2005). Seven landmarks on
wing veins (crossing points or at the wing margin) of the
forewing were used to characterize the wing shape and the
relation between wing width (distance between landmarks 5
and 7) and wing length (distance between landmarks 1 and
5) (Figure 2). Landmarks were digitized with the program
TpsDig 2.16 (Rohlf 2010a). The order of specimens was
randomized using TpsUtil 1.46 (Rohlf 2010b). We applied a
generalized least square Procrustes fit analysis using TpsRelw
1.49 (Rohlf 2010c) to obtain relative warps (similar to principle components [PCs]), which can be used as shape variables. The Levene’s test of homogeneity of variances was
used to verify if PC scores showed different variances among
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Figure 1. Geographic locations of the 26 sampling sites of Pararge aegeria over North Africa and Europe (Morocco, Tunisia,
Belgium, France). The detailed inlay shows 14 sampled populations of the oases at the southern edge of the Atlas Mountains.
Numbers of localities coincide with Table 1. Elevations more than 500 m asl are shaded in light grey.
Habel et al. • Low Differentiation Among Oasis Populations of Butterflies
populations. A full cross-validation test was conducted to
identify variances among oasis and non-oasis populations
using U tests. To evaluate whether different populations can
be distinguished on the basis of different wing shape, we performed a discriminant function analysis of PC scores using
population membership as a priori grouping variable. Using
PC scores in discriminant analyses allows for the detection
of shape variation among groups. A full cross-validation test
was applied to evaluate the power of the discriminant functions and the possibility to blindly attribute each specimen to
the original group. Statistic analyses were conducted with the
SPSS 17 software package (IBM, Somers, NY).
Wing Coloration Analyses
Genitalia Morphology
Figure 2. (a) Forewing of Pararge aegeria with selected
landmarks used to calculate total wing shape, wing length
(between 5 and 7), and wing width (between 1 and 5). (b) Valva
and (c) aedeagus with selected landmarks (black dots) and
sliding semilandmarks (white dots).
The 9th abdominal segment of male butterflies is divided
into a dorsal tegumen and a ventral vinculum forming a
structure for the attachment of genital parts and a pair of
lateral clasping organs (valvae). Males also have a median
tubular organ (called the aedeagus), which is directly involved
in the insemination of females. Lateral sections of the valvae
and the aedeagus showed significant differences among
Mediterranean populations of P. aegeria in previous analyses
and hence are suitable and sensitive characters for detecting
intraspecific differentiation (Dapporto 2010). Male genitalia
from 359 individuals belonging to 18 populations (13
oasis populations, 2 from Tunisia, each one from the Atlas
Mountains, Belgium, and France; Table 1) were dissected
using standard procedures; abdomina were boiled in 10%
caustic potash. Genitalia were cleaned and the left valva and
the aedeagus removed. Tegumen and valva were mounted
on Euparal between microscope slides and cover slips.
Aedeagi were included in dimethyl hydantoin formaldehyde
resin without using cover slips in order to avoid possible
deformations. Lateral views of aegeadi and valvae were
photographed with a digital camera mounted on a dissecting
microscope. We applied a combination of landmarks and
sliding semilandmarks (Bookstein 1991). Three landmarks
on the outlines of valvae and 5 landmarks defining the shape
of aedeagi were considered (Figure 2b). Landmark choice
was based on easy and secure identification of homologous
structures across individuals (type II and type III landmarks).
An additional set of 38 points for valvae and 23 points for
aedeagi were defined as sliding semilandmarks using TpsUtil.
Although landmarks represent fixed homologue points,
sliding semilandmarks provide an opportunity to digitize
outlines when few landmarks are available. Each point is
allowed to slide along a tangential direction so that the contour
is homologue, whereas each individual point does not need to
be (Perez et al. 2006). Landmarks and sliding semilandmarks
were digitized with TpsDig and the order of specimens
randomized using TpsUtil. We applied a generalized least
square Procrustes fit analysis using TpsRelw to obtain relative
warps (PCs). Only PCs explaining more than 1% of the total
variance were considered in successive analyses (Dapporto
2010). We performed discriminant function analysis by using
the aedeagus and valva PCs as predictors and population
membership as a priori grouping variable.
5
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The same digital pictures as used for wing shape analyses were
transformed, splitting the photographs into 3 spectral colors
(red, green, and blue) using the program Matlab R2007a 7.4
(The Mathworks Inc.). These 3 colors were merged into a
mean grey value. A wing polygon bordered by the landmarks
1, 5, 6, and 7 was used to curtail a defined and identical wing
area for each individual to calculate the degree of melanization. The total sum of pixels within this polygon was counted
for each picture. A threshold value was selected being sensitive enough to classify certain proportions of the pixels being
white and black. All pixels falling below this threshold were
recorded as black, all others as white. The ratio divided by the
total number of pixels (in %) was analyzed for the wing of
each individual. Furthermore, we calculated the same ratio (in
%) using each spectral range (red, green, and blue) separately.
We performed discriminant function analysis by using the
percentages of each threshold value for each color as predictors and population membership as a priori grouping variable.
Journal of Heredity 
Table 2 Means and SDs of parameters of genetic diversity analyzed for geographic groups of Pararge aegeria: allelic richness (AR),
percentage of expected heterozygosity (He), and percentage of observed heterozygostiy (Ho). Furthermore, the ratio of wing length
to wing width is given as parameter “wing design.” Calculations were performed using 4 and 6 microsatellite loci. Differences between
North Africa and Europe were tested by using Kruskal–Wallis ANOVA
AR_4
AR_6
He_4
He_6
Ho_4
Ho_6
Wing design
Oases
Atlas Mountains Tunisia
North Africa
Belgium
France
P value
7.78 (±0.42)
6.85 (±0.24)
84.6 (±1.6)
73.7 (±14.3)
59.2 (±18.1)
60.7 (±18.4)
1.23 (±0.03)
7.38 (±0.83)
6.24 (±0.38)
82.6 (±3.9)
79.3 (±11.5)
60.7 (±6.0)
61.9 (±8.8)
—
7.72 (±0.31)
6.75 (±0.47)
84.3 (±1.6)
79.7 (±6.3)
60.1 (±0.8)
61.6 (±0.9)
1.22 (±0.03)
6.88 (±0.14)
—
76.4 (±0.3)
—
72.7 (±1.5)
—
1.17 (±0.04)
7.73 (±1.08)
—
84.1 (±6.7)
—
78.0 (±0.8)
—
—
ns
ns
ns
ns
ns
ns
<0.05
7.99 (±0.38)
7.17 (±0.11)
85.8 (±2.5)
86.3 (±1.2)
60.2 (±3.1)
62.36 (±4.86)
1.18 (±1.19)
ns, not significant.
In order to assess the current and past potential distribution
of P. aegeria, we obtained a comprehensive set of species
records from our own field surveys and from scientific collections linked to the Global Biodiversity Information Facility
(www.gbif.org). Including only specimen-based information
with a spatial precision of coordinates meeting the resolution
of the climate data (see below), we excluded all raster data. As
the remaining records showed a high degree of spatial clustering caused by locally varying sampling efforts rather than
by actual preference of the species to specific habitats, we
applied a spatial filter leaving 2753 randomly selected records
with a minimum distance to the nearest neighbor of 0.092°
(median = 0.425°). The minimum distance corresponds to a
separation of approximately 2 grid cells of the climate data.
A set of 19 bioclimatic variables (Busby 1991; Beaumont
et al. 2005), representing average conditions between 1950
and 2000 with a spatial resolution of 0.041°, was obtained
from the worldclim database (www.worldclim.org; Hijmans
et al. 2005a). In order to assess the species’ past potential
distribution, we downloaded 2 different sets of bioclimatic
variables for the last glacial maximum (LGM) 21 000 before
present (BP) derived from the Community Climate System
Model version 3 (CCSM; Otto-Bliesner et al. 2006) and
the Model for Interdisciplinary Research on Climate version 3.2 (MIROC; Hasumi and Emori 2004) and spatially
downscaled by R.J. Hijmans for worldclim. Using the same
downscaling procedure as for the LGM dataset described by
Peterson and Nyári (2008), we downscaled further CCSM
and MIROC simulations for 6000 BP using the respective
original files downloaded from the Paleoclimatic Modelling
Intercomparison Project 2 (http://pimp2.lsce.ipsl.fr/).
A variety of different modeling algorithms for Species
Distribution Model (SDM) applications is currently available.
Herein, we selected a simple profiling technique termed
BIOCLIM (Busby 1991) assessing a multivariate rectangular
environmental space occupied by the target species. BIOCLIM
does not allow the incorporation of complex interactions
between variables, but is at the same time not sensitive to high
intercorrelations among predictors. Furthermore, BIOCLIM
can be used to visualize the spatial distribution and position
of areas within the bioclimatic envelope spanned by the
species records in an easily interpretable form.
6
We believe that BIOCLIM may outperform other more
complex algorithms in this specific case because the study
species virtually occurs all over the study area. Presence/
absence or presence/pseudo-absence methods are likely
to fail in effectively discriminating between environmental
conditions at the species records and random background
conditions. BIOCLIM models were computed in Cran R
version 2.14 using the dismo package (Hijmans et al. 2011).
Past bioclimatic conditions unavailable within the study area
under current conditions were quantified using Multivariate
Environmental Similarity Surfaces (MESS; Elith et al. 2010).
Results
Molecular Data
The 6 microsatellite loci showed only marginal evidence of
null alleles or large allele dropouts for the populations analyzed (Locus 11: 3 of 21; Locus 16: 2 of 21; Locus 17: 3 of
21; Locus 19: 2 of 21; Locus 2: 4 of 21; and Locus 7: 3 of
21). All microsatellite loci were polymorphic throughout all
21 populations from North Africa. Locus-specific allele frequencies for each population are given in the Supplementary
Appendix 1 online. The mean value of AR was 6.75 (± 0.47
standard deviation [SD]) and 7.72 (± 0.31 SD) calculated
for a reduced set of 4 microsatellites (data for 2 of the 6
loci were unavailable for the European samples). Measures
of heterozygosity were also high: He = 79.7% (± 6.3 SD)
(4 loci: 84.3% ± 1.6 SD) and Ho = 61.6% (± 0.9 SD) (4
loci: 60.1% ± 0.8 SD) (Table 2). Population-based diversity
estimates are given in the Supplementary Appendix 3 online.
Heterozygosity deficiency was detected for the following
populations: Gaouz, Taltfraout, Ifri, Sahli, Zrigat, Corgens,
and Bois Fevrier (P < 0.05). No heterozygostiy excess was
detected. Calculation of effective population sizes yielded
fairly similar estimates for all populations ranging from 97
to 156 individuals (values of each population are given in
Supplementary Appendix 3 online). Neither the calculations of potential population bottlenecks nor the estimates
of effective population sizes revealed significant differences
between oases and other populations (Mann–Whitney U
tests, all P > 0.05). Genetic diversity was not significantly
different among oases, the Atlas Mountains and Tunisia
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Species Distribution Modeling
Habel et al. • Low Differentiation Among Oasis Populations of Butterflies
Table 3 Nonhierarchical analyses of molecular variance on Pararge aegeria populations. Hereby, 3 levels are taken into account: genetic
variance among populations, among individuals within populations, and within individuals. Given are values of genetic variance with
respective R-statistics metric (in parenthesis). For the AMOVA analyses, we predefined the following regions: Oases, Atlas Mountains,
Tunisia, Morocco + Tunisia, Belgium, France, and Central Europe
Oases
Atlas Mountains
Tunisia
Morocco + Tunisia
Belgium
France
Central Europe
All
Among populations (RST)
Among individuals within populations (RIS)
Within individuals
0.30590 (0.00565)
−0.48498 (−0.01129)
−0.74915 (−0.01229)
0.24942 (0.00469)
−0.82953 (−0.00872)
7.96330 (0.07297**)
2.71967 (0.02682)
17.32697 (0.19075***)
4.04940 (0.07520)
6.95335 (0.16010)
6.66813 (0.10804)
4.76885 (0.09004)
14.88065 (0.15516)
3.10781 (0.03072)
8.65922 (0.08775)
12.58912 (0.17126***)
49.79808
36.47692
55.05263
48.19277
81.02381
98.05319
90.01685
60.92063
*P < 0.05; **P < 0.01; ***P < 0.001.
Groups
Among groups (RCT)
Among populations within
groups (RSC)
Among individuals within
populations (RIS)
Within individuals
Oases vs. Atlas Mountains
Morocco vs. Tunisia
North Africa vs. Central
Europe
Belgium vs. France
−0.73165 (0.00395)
1.76532 (0.02528*)
54.95278 (0.42576***)
0.26710 (−0.01093)
−0.02416 (−0.00035)
0.60815 (0.00821***)
12.89644 (0.19132***)
13.41835 (0.19704***)
12.58912 (0.17126***)
54.51061
54.68072
60.92063
−1.61731 (−0.01604)
3.79824 (0.03707*)
8.65922 (0.08775*)
90.01685
*P < 0.05; **P < 0.01; ***P < 0.001.
(Kruskal–Wallis test, P > 0.05). No difference in genetic
diversity was found between North Africa and Europe either
(Kruskal–Wallis test, P > 0.05).
Nonhierarchical AMOVAs detected a moderate genetic
variance within individuals throughout all populations
analyzed (12.59, RIS: 0.171, P < 0.0001). The major proportion
of molecular variance was detected among populations
(17.33, RST: 0.191, P < 0.0001). Analyses for each geographic
group separately (Oases, Atlas Mountains, Tunisia, Morocco
and Tunisia, Belgium, France, and Central Europe) yielded
no significant genetic differentiation among the North
African populations, among any of the oasis populations,
or among the populations from the Atlas Mountains and
between Morocco and Tunisia (Table 3). Results obtained
from F-statistics are given in the Supplementary Appendix
4 online.
The hierarchical analysis of molecular variance indicated a shallow split between Morocco and Tunisia (RCT:
0.025, P < 0.05). However, considerable differentiation was
detected between European and African populations (54.95,
RCT: 0.426, P < 0.0001) (Table 4). Respective values obtained
for F-statistics are fairly similar and are provided in the
Supplementary Appendix 5 online.
Population- (neighbor joining tree, not shown) and individual-based (Figure 3) genetic analyses supported a significant split between Europe and North Africa (Figure 3a) and
absence of differentiation within North Africa (Figure 3b,
3c). No significant isolation by distance was detected for
North Africa excluding the oases (N = 7 populations),
z = 199.0357, r = 0.3400, P = 0.1455, including the oases
(N = 20), z = 1240.9531, r = 0.221, P = 0.082, Morocco
including the oases (N = 18), z = −89.7969, r = −0.2281,
P = 0.9146, the oases populations themselves (N = 13),
z = 88.9228, r = 0.3088, P = 0.0700, or Europe (N = 4),
z = 44.1515, r = −0.0723, P = 0.7104. However, significant results were obtained for Morocco excluding the oases
populations (N = 5), z = −1.4181, r = 0.5748, P = 0.0084
and for the complete set including all samples (N = 24),
z = 77696.6009, r = 0.8098, P < 0.0000 (Table 5). Respective
graphs are given in Supplementary Appendix 2 online.
Wing Morphometrics
The morphometric analyses of the wings resulted in 12 PCs
explaining a cumulative variance of 100%. Levene’s test
revealed that only PC1 contained slightly significant different
variances separating populations (Levene statistic = 1.949,
P = 0.014). However, oasis populations did not show lower
variances compared with the remaining Moroccan and
Tunisian populations (Mann–Whitney U = 23.0, P = 0.578).
Discriminant analysis extracted 6 functions showing a significant Wilks’s lambda (P < 0.05). These functions cumulatively explained 86.9% of the variance. The cross-validation
test only assigned a low percentage of individuals (29.5%)
to their original groups. Oasis populations showed particularly low percentages of correctly assigned individuals
(17.4%) compared with the Atlas and Tunisian populations
(35.7%) and the Belgian population (74.6%). Discriminant
7
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Table 4 Hierarchical variance analyses of Pararge aegeria populations. Hereby, 4 levels of genetic variance were taken into account:
genetic variance found among predefined groups, among populations within groups, among individuals within populations, and genetic
variance found within individuals. Predefined geographic groups are in accordance with the groups given in Table 3. Given are variance
values with respective R-statistics (in parenthesis)
Journal of Heredity 
Table 5 Results obtained from isolation-by-distance analyses
performed separately for different geographic groups
Group
functions 1 and 2 explained the largest fraction of variance
(52.3%, Wilks’s lambda = 0.36, P < 0.001; and 9.5%, Wilks’s
lambda = 0.127, P < 0.001) and were mainly correlated with
PC1 and PC5. Visual inspection of the plotted scores for
these variables and thin-plate splines revealed that European
and the Atlas and Tunisian specimens have lower PC1 values with respect to most oasis population. Thin-plate spline
grids reveal that PC1 is linked to wing elongation, with oasis
populations showing more elongated wings (Figure 4a). The
ratio of wing width to wing length shows slight evidence that
individuals living in oases have more elongated wings (oases,
ratio wing length/wing width, 1.23 [± 0.03]) if compared
with individuals from Europe (1.17 [± 0.04]) or Tunisia (1.18
[± 1.19]) (P < 0.05) (Table 2). Population-specific values are
given in the Supplementary Appendix 3 online.
Wing Coloration
By using 20 different threshold values for each color, we
obtained 60 color variables; 51 of them showed variances different from zero. These variables were included in a discriminant function analysis, which extracted 3 functions showing a
significant Wilks’s lambda (P < 0.05). These functions cumulatively explained 79.0% of the variance. The cross-validation
test assigned only a low percentage of individuals (20.1%) to
their original group. The oasis and the other North African
populations showed low percentages of correctly assigned
individuals (15.8% and 21.0%); however, the oasis population 6
showed a rather high correct assignment (52.2%). As for wing
shape, the Belgium population showed a high correct assignment value (88.2%). Functions 1 and 2 explained the largest
fraction of variance (48.1%, Wilks’s lambda = 0.23, P < 0.001;
and 19.6%, Wilks’s lambda = 0.426, P < 0.001). The inspection
of values from variables entered in the discriminant analysis
8
r
P
24
20
77696.6009
1240.9531
0.8098 <0.0001
0.2201 0.0820
7
199.0357
0.3400
0.1455
18
−89.7969
−0.2281
0.9146
5
−1.4182
0.5748
0.0084
13
4
88.9228
44.1515
0.3088
−0.0723
0.0700
0.7104
revealed that individuals from Europe tend to be darker compared with individuals from North Africa.
Genitalia Morphometrics
The morphometric analyses of aedeagi and valvae resulted in
14 and 11 PCs, respectively, explaining more than 1% of the
total variance, resulting in a cumulative variance of 92.5% and
93.8%. Levene’s test revealed that only PC1 and PC11 showed
slightly significant different variances between populations
(PC1 Levene statistic = 1.859, P = 0.024; PC11 Levene
statistic = 1.743, P = 0.042). However, oasis populations
did not show lower variances compared with the Atlas and
Tunisian populations (Mann–Whitney U = 23.0, P = 0.578).
Discriminant analysis extracted 3 functions showing a
significant Wilks’s lambda (P < 0.05). These functions
cumulatively explained 80.1% of the variance. The crossvalidation test assigned only a low percentage of individuals
(18.3%) to their original group. Oasis populations showed
particularly low percentages of correctly assigned individuals
(10.5 ± 6.7% SD) compared with non-oasis populations of
North Africa (35.8%) and the European populations (94.1%).
Functions 1 and 2, represented in Figure 4b, explained the
largest fraction of variance (48.1%, Wilks’s lambda = 0.23,
P < 0.001; and 19.6%, Wilks’s lambda = 0.426, P < 0.001).
Aedeagus PC1 and PC2 were mostly correlated with the first
2 discriminant functions. Visual inspection of the plotted PC
scores and thin-plate splines distinguished European from
North African populations, mainly by the presence of a
larger cornutus in the latter ones. Within the North African
populations, no distinction was found between populations
from oases, Morocco, and Tunisia (Figure 4b).
Species Distribution Modeling
The BIOCLIM SDM developed under current conditions
correctly identified major parts of Europe and North Africa
as adequately situated within the bioclimatic envelope defined
by the species records (Figure 5). Current distribution positions the oases populations at the southernmost margin of
the species’ distribution range. The northern part of North
Africa yielded permanently suitable climatic conditions over
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Figure 3. Bayesian analysis performed on populations of
Pararge aegeria using the STRUCTURE software (Pritchard
et al. 2000). (a) Performing the analysis for K = 2 including
all samples available (dividing Europe from North Africa),
(b) performing the analyses for K = 2 including all individuals
collected in North Africa (western vs. eastern), and (c)
performing the analysis for K = 2 with all samples from
the western part of North Africa (Oasis populations vs.
populations from the Atlas Mountains).
All populations
North Africa
(with oases)
North Africa
(without oases)
Morocco (with
oases)
Morocco
(without oases)
Oases
Europe
Number of
populations Z
Habel et al. • Low Differentiation Among Oasis Populations of Butterflies
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Figure 4. Graphic representation of geometric morphometric variables mostly correlated with discriminant functions among
areas. Variations in shape along both axes are shown in thin-plate spline deformation grids. (a) Forewing: the dashed lines only
represent an indication for wing shape, but has no morphometric values; (b) male genitalia: numbered squares represent means ±
SD values, populations are numbered as in Table 1, black squares, European populations; grey squares, non-oasis population from
North Africa; white squares, oasis populations.
the past 21 000 years for P. aegeria. The species was modeled to be widely distributed over major parts of the North
African region with a southern distribution limit at the northern edge of the Sahara desert during the Pleistocene. The
model suggested that during the Holocene (6000 BP) the
species retracted northwards. Under this scenario P. aegeria
disappeared from the Saharan oases, but likely persisted in
the northern part of North Africa, where climatic conditions remained stable. Starting at about 6000 years BP, rising
humidity at the northern edge of the Sahara has resulted in
the southwards expansion of P. aegeria and the recolonization
of the oases studied here.
9
Journal of Heredity 
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Figure 5. Spatial distribution of the bioclimatic envelope of Pararge aegeria derived from a BIOCLIM SDM under current and
past conditions (6000 and 21 000 years BP) assuming 2 different climate scenarios (CCSM and MIROC). Areas with bioclimatic
conditions during the past, which are currently not realized within the shown geographic extent, are indicated as extrapolation
areas (MESS).
10
Habel et al. • Low Differentiation Among Oasis Populations of Butterflies
Discussion
Lack of Intraspecific Differentiation
Our data show strong genetic and morphologic cohesiveness of populations from both oases and even across all
of Morocco and Tunisia. Two nonexclusive scenarios can
explain the lack of differentiation for the isolated oasis populations: 1) a recent colonization from interconnected source
populations from the North and hence a limited divergence
time and 2) ongoing gene flow among local oasis populations. In addition, the oases provide relatively stable habitat
conditions limiting population size fluctuations and therefore
the influence of bottlenecks and similar random processes.
The SDMs suggest that P. aegeria was widely distributed
over the North African region during the LGM (21 000 BP).
The species presumably disappeared from its southernmost
distribution range during the Holocene (6000 BP), but subsequently reexpanded southwards leading to the recolonization
of the Saharan oases (Figure 5). Hence, the models indicate
that the oases were recently colonized from more northern
areas (i.e., Atlas Mountains).
Interestingly, oases populations, although being similar in
their genetic makeup and in the studied coloration and genitalia
phenotypes, show a slightly elongated wing design compared
with individuals from Europe and Tunisia, which distinguishes
them from the remaining studied populations. Previous studies
on the wing design in P. aegeria revealed that elongated wings
can be associated with increased dispersal ability ( Van Dyck
et al. 1997; Berwaerts et al. 2002 ). Therefore, the detected phenotypic uniqueness in wing design of oases populations in this
butterfly species might be caused by selection towards increased
dispersal behavior most probably during the colonization process of the oases habitats, when only the most mobile individuals were probably capable of reaching these habitat islands.
Population genetic and phylogeographic studies on
P. aegeria support the capability of long-distance dispersal in
High Intraspecific Variability
Small areas located at the margin of a species distribution
often do not host permanent populations, but represent
sinks within the metapopulation network (Dennis et al.
11
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Given the ecologic characteristics of P. aegeria, we expected
to detect significant genetic and morphologic differentiation
among oasis populations as a result of strong isolation of
habitat patches in the unfavorable matrix of the Saharan
desert. However, our findings strongly contrasted with these
initial predictions: The only major split found in our data was
detected between North African and European populations;
isolated oasis populations maintain similar intraspecific
genetic variability compared with the interconnected
populations from the Atlas Mountains, Tunisia, and Europe
and no significant genetic differentiation was found among
the oasis populations. In addition no differentiation was
detected over the entire North African region. In contrast
to the genetic homogeneity and the lack of differentiation
in wing color and genitalia morphology among the oasis
populations, wing design differed from all other geographic
groups. Indeed, oases butterflies are characterized by a more
elongated wing design. The 2 unexpected findings: 1) the
lack of differentiation among isolated oases populations and
2) the high level of genetic diversity within oasis populations
are discussed in further detail below.
the wake of habitat expansion, as for example, shown for
the colonization of Europe from North Africa (Weingartner
et al. 2006) and the rapid colonization of the agricultural
landscape in some parts of Europe (Vandewoestijne and Van
Dyck 2010). The high dispersal ability and the results of species distribution modeling support the hypothesis of a fairly
recently colonization of the oases.
Similar to the genetic data, wing coloration and genitalia
morphology showed no significant differentiation, but relatively high within-population variation across North Africa.
This was unexpected given the diverse ecological environments the populations were found in (e.g., desert oases vs.
mountain populations). However, these findings are congruent with previous studies on P. aegeria in Europe revealing
high variability of this species for both morphological and
life-history traits (Nylin et al. 1989; Van Dyck and Wiklund
2002; Kemp et al. 2006).
The lack of differentiation in phenotypic characters might
be explained by similar selective pressures due to high similarity in climatic and ecological conditions across oasis habitats.
Yet, one would expect that neutral characters would diverge
in isolated populations as a result of drift, especially when
considering the relatively small effective population sizes in
oases (97–156 individuals).
It appears likely that the lack of genetic differentiation
is the result of a fairly recent invasion of the oases by a
large number of founder individuals. The lack of isolationby-distance for the oases populations further adds evidence for this scenario. However, frequent migration of
P. aegeria individuals among oases through passive migration events (e.g., caused by sand storms) is rather unlikely,
given the extremely unfavorable climatic conditions outside the oases and the large distances among oasis habitats.
Yet, only few migrants are needed to prevent differentiation when strongly divergent selective pressures are
missing. Additionally, similar environmental conditions
across the oases since the recolonization might have prevented any morphological differentiation of these isolated
populations.
The lack of genetic differentiation between these populations
is unlikely to be the result of an insufficient resolution of the
selected microsatellite markers; the same loci showed low but
significant genetic differentiation along a latitudinal transect in
Europe and even at the landscape level (Vandewoestijne and
Van Dyck 2010). Furthermore, data obtained with the same set
of microsatellites also indicate the presence of 4 genetic clusters
within the P. aegeria species complex: the first cluster is formed by
the North European subspecies P. aegeria tircis, whereas the more
southern P. a. aegeria is divided into 3 lineages: North Africa,
Iberia, and other southern European regions (unpublished data).
The southern European and North African lineages have been
supported in this study.
Journal of Heredity 
Conclusion
During the late Pleistocene, P. aegeria occurred over major
parts of North Africa including the northern edge of the
Sahara desert. The species retracted northwards during the
early Holocene but reexpanded southwards in later stages
of the Holocene. In the northern part of North Africa,
P. aegeria persisted during phases of northward retractions
and occurred in interconnected populations. This resulted
in a lack of genetic differentiation between the western and
eastern margins of the North African distribution range,
which still persists today. The oases on the southern margin
of the distribution were presumably colonized fairly recently
by immigrants showing a rather high dispersal behavior,
being mirrored in the slightly elongated wing design found
for individuals of oases populations, but not in populations
of Europe or Tunisia. Habitat stability over the last millennia probably prevented strong population fluctuations and
concomitant loss of genetic diversity. This, together with
low rates of ongoing gene flow likely prevented the divergence of local populations.
Supplementary Material
Supplementary material can be found at http://www.jhered.
oxfordjournals.org/.
12
Funding
Fonds National de la Recherche Luxembourg (FNR);
German Academic Exchange Service (DAAD); Natural
History Museum Luxembourg.
Acknowledgments
J.C.H. was a DAAD postdoctoral fellow and S.V. was a FRSFNRS postdoctoral fellow. We thank Joachim Besold for
statistical help.
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Received November 1, 2011; Revised September 27, 2012;
Accepted September 28, 2012
Corresponding Editor: Adalgisa Caccone
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