Evolutionary History of the Morocco lizard-Fingered Geckos of the Saurodactylus brosseti Complex

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DOI: 10.1007/s11692-017-9417-8
Cite this publication
Abstract
Studies of biodiversity in the Maghreb have revealed high genetic diversity and divergent genetic lineages among many taxa including squamates. Geographic barriers such as the Atlas Mountains are one of the key factors promoting genetic differentiation and the high levels of endemism. The lizard-fingered gecko Saurodactylus brosseti is endemic to Morocco. Its range includes both sides of the Atlas Mountains, and although high diversity was previously identified within the species, much of the range was unsampled. To understand the evolutionary and biogeographical history of this species, we used mitochondrial and nuclear DNA sequence data from 64 populations sampled across the entire species range. We employed phylogenetic methods based on gene trees, species trees as well as a time calibrated Bayesian genealogy and coalescent species delimitation approaches. We uncovered four highly divergent and allopatric mitochondrial lineages that did not share nuclear haplotypes at variable nuclear loci, suggesting the four groups have been evolving independently since the Miocene, according to our molecular dating estimates. These results coupled with the geographic pattern of genetic diversity suggest a possible role of the Atlas Mountains for the divergence observed between the four lineages of S. brosseti, while each lineage probably later underwent several episodes of fragmentation followed by (re-) expansion during Pleistocene climatic oscillations. Bayesian species delimitation analysis indicates that the four lineages may well be distinct species but we suggest that detailed morphological analyses are needed prior to taxonomic changes. The four lineages represent ancient independent evolutionary units, and deserve conservation management as distinct entities.
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Evol Biol
DOI 10.1007/s11692-017-9417-8
RESEARCH ARTICLE
Evolutionary History oftheMorocco lizard-Fingered Geckos
oftheSaurodactylus brosseti Complex
DanielaRosado1,2· CatarinaRato1· DanieleSalvi1,3 · DavidJamesHarris1
Received: 5 January 2017 / Accepted: 30 March 2017
© Springer Science+Business Media New York 2017
These results coupled with the geographic pattern of
genetic diversity suggest a possible role of the Atlas Moun-
tains for the divergence observed between the four lineages
of S. brosseti, while each lineage probably later underwent
several episodes of fragmentation followed by (re-) expan-
sion during Pleistocene climatic oscillations. Bayesian spe-
cies delimitation analysis indicates that the four lineages
may well be distinct species but we suggest that detailed
morphological analyses are needed prior to taxonomic
changes. The four lineages represent ancient independent
evolutionary units, and deserve conservation management
as distinct entities.
Keywords Morocco· Atlas Mountains· Saurodactylus
brosseti· Phylogeography· Bayesian species delimitation
Introduction
The Maghreb region of Northwest Africa is part of the
Mediterranean biodiversity hotspot (Myers et al. 2000).
Studies on the Maghreb biodiversity carried out in the last
couple of decades have often revealed high genetic diver-
sity within various taxa including reptiles (e.g. Bshaena
and Joger 2013; Metallinou etal. 2015; Rato et al. 2016),
highlighting that there is still much diversity to be uncov-
ered within the region. Many studied species were shown
to be complexes of highly divergent genetic lineages, and
therefore studying genetic diversity in other taxa in this
region is extremely important for the identification of
cryptic taxa. Moreover, underestimation of biodiversity
can be anticipated in regions such as North Africa due to
its many remote areas with difficult access (Ficetola etal.
2013), especially in groups such as reptiles where distribu-
tion ranges are continuously being redefined after each field
Abstract Studies of biodiversity in the Maghreb have
revealed high genetic diversity and divergent genetic line-
ages among many taxa including squamates. Geographic
barriers such as the Atlas Mountains are one of the key
factors promoting genetic differentiation and the high lev-
els of endemism. The lizard-fingered gecko Saurodactylus
brosseti is endemic to Morocco. Its range includes both
sides of the Atlas Mountains, and although high diversity
was previously identified within the species, much of the
range was unsampled. To understand the evolutionary and
biogeographical history of this species, we used mitochon-
drial and nuclear DNA sequence data from 64 populations
sampled across the entire species range. We employed phy-
logenetic methods based on gene trees and species trees as
well as a time calibrated Bayesian genealogy and coales-
cent species delimitation approaches. We uncovered four
highly divergent and allopatric mitochondrial lineages that
did not share haplotypes at variable nuclear loci, suggest-
ing the four groups have been evolving independently since
the Miocene, according to our molecular dating estimates.
Electronic supplementary material The online version of this
article (doi:10.1007/s11692-017-9417-8) contains supplementary
material, which is available to authorized users.
* Daniele Salvi
danielesalvi.bio@gmail.com
1 CIBIO Research Centre inBiodiversity andGenetic
Resources, InBIO, Universidade doPorto, Campus Agrário
de Vairão, Rua Padre Armando Quintas, No 7, Vairão,
VilaDoConde4485-661, Portugal
2 Departamento de Biologia, Faculdade de Ciências da
Universidade doPorto, Porto4099-002, Portugal
3 Departament ofHealth, Life andEnvironmental Sciences,
University ofLAquila, 67100LAquila, Coppito, Italy
Evol Biol
1 3
expedition (e.g. Damas-Moreira etal. 2014; Harris et al.
2010; Rosado etal. 2016).
Many phylogeographic studies have identified the pres-
ence of significant geographic barriers as one of the key
factors promoting genetic differentiation and the origin
of evolutionary lineages (e.g. Lansari etal. 2015). In the
western Maghreb, there are ancient topographical forma-
tions such as the Moulouya River Valley, the Rif and the
Atlas Mountains that act as physical, ecological and cli-
matic barriers to dispersion, causing genetic differentiation
between populations (Barata etal. 2008; Brown etal. 2002;
Velo-Antón etal. 2015). Besides acting as a barrier to spe-
cies coming from the North and from the Sahara, the Atlas
Mountains have been shaping the separation of Morocco
into two bioclimatic regions; a more Mediterranean cli-
mate to the West (arid to semi-arid environment) and more
Saharan environments found in the East (Bons and Geniez
1996). The orogeny of the Atlas Mountains is estimated to
have occurred around 9million years ago (Mya) in the late
Miocene (Gómez etal. 2000). These mountains are recog-
nized as having played an important role in the genesis of
high levels of endemism in many taxa including reptiles
and amphibians (Cox etal. 2006). Indeed, various studies
have highlighted the role of these mountains in the vicari-
ant events behind genetic subdivision in lizards of the gen-
era Agama and Acanthodactylus (Brown etal. 2002; Fon-
seca etal. 2008), the freshwater turtle Mauremys leprosa
(Veríssimo et al. 2016), the water frogs Pelophylax saha-
ricus (Lansari et al. 2015), the bat Myotis natteri (Sali-
cini etal. 2013), and scorpions of the genera Buthus and
Androctonus (Coelho etal. 2014; Habel etal. 2012; Huse-
mann etal. 2012).
Nevertheless, the outcome of such population subdivi-
sions caused by the Atlas Mountains can be quite distinct
among taxa, depending essentially on the time scale and
duration of their isolation. For instance, the Buthus scor-
pions have likely evolved within several microrefugia
located in the foothills of the Atlas mountains during the
climatic oscillations of the Pliocene and Pleistocene (Huse-
mann etal. 2012), while other taxa such as the water frogs
Pelophylax saharicus present a much older subdivision into
two major lineages, which seem to be related to the uplift
of the Atlas mountains about 9Mya (Lansari etal. 2015).
The use of a phylogeographic approach coupled with time
calibrated phylogenies has revealed not only the spatial but
also the temporal extent of diversification in the studied
organisms, thereby providing insights regarding the role of
the Atlas Mountains in the origin and the maintenance of
the high level of diversity and endemism observed within
the western Maghreb region.
Geckos of the genus Saurodactylus, an extremely old
taxon (approximately 82–114Mya, Gamble et al. 2011)
belonging to the Sphaerodactylidae family, are endemic
to the Western Maghreb region. The genus currently
comprises three species: S. mauritanicus (Duméril and
Bibron 1836), S. fasciatus Werner 1931 and S. brosseti
Bons and Pasteur 1957. The Morocco lizard-fingered
gecko Saurodactylus brosseti (previously considered as
a subspecies of S. mauritanicus) is endemic to Morocco,
ranging from central regions to the north of Western
Sahara in the south through isolated populations along
the coast (Bons and Geniez 1996). Saurodactylus brosseti
is much more rare on the drier, Eastern side of the Atlas
Mountains, where few specimens have been recorded
but with new localities being recently identified (Harris
etal. 2010; Rosado etal. 2015). Rato and Harris (2008)
observed a very high level of genetic divergence (up to
11.4%) for the mitochondrial ND4 gene region between
populations of S. brosseti, even though sampling did not
include either the isolated Eastern or Southern popula-
tions. Such levels of divergence are much higher than the
ones observed for many recently described lizard species
(e.g. Ahmadzadeh etal. 2013; Metallinou and Carranza
2013; Vasconcelos and Carranza 2014), suggesting an
underestimated diversity within S. brosseti. Therefore,
given the distribution of this species on both sides of the
Atlas, its high intraspecific divergence, and the fact that
isolated populations from remote regions of its range
have not previously been analyzed, this species represents
a good case study to explore the processes responsible for
the origin and the maintenance of the high level of diver-
sity and endemism observed within the western Maghreb.
However, relative to the previous study (Rato and Harris
2008), an increased geographic sampling, both in terms
of individuals and populations, and the use of a multilo-
cus approach is essential to better understand the evolu-
tionary and biogeographical history of this species and
to assess whether its genetic lineages represent distinct
cryptic species.
In this study, we used mitochondrial and nuclear DNA
sequence data from 64 S. brosseti populations sampled
across the entire species range. We carried out extensive
field research in order to collect samples from isolated
populations including remote areas with difficult accessibil-
ity. We used phylogenetic methods based on gene trees and
species tree as well as a time calibrated Bayesian geneal-
ogy and coalescent species delimitation approaches. The
main aim of this study is to infer the evolutionary history of
the Maghreb endemic gecko Saurodactylus brosseti and to
investigate the role of geographic barriers such as the Atlas
Mountains in shaping the observed phylogeographic pat-
tern. A further goal of this study is to assess whether the
mitochondrial lineages of S. brosseti identified by Rato and
Harris (2008) show reciprocal monophyly for the employed
nuclear markers, and to assess whether they may corre-
spond to distinct species.
Evol Biol
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Materials andMethods
Sampling andLaboratory Procedures
We collected 115 samples of Saurodactylus brosseti from
64 localities, including 15 samples analyzed by Rato and
Harris (2008). The geographic location of each individual
is represented in Fig. 1 and detailed information, includ-
ing GenBank accession numbers, in Table1. Geckos were
caught by hand, geographic data recorded with GPS, and a
small piece of tail tip was collected and stored in 96% etha-
nol before releasing the animal.
Genomic DNA was extracted from tail tissue using
the High Salt method (Sambrook et al. 1989). DNA
amplification was performed through Polymerase Chain
Reaction (PCR) for one partial mitochondrial ribosomal
gene (12S rRNA), one partial mitochondrial coding gene
(ND4), two partial nuclear coding genes (ACM4 and
MC1R) and one nuclear intron (BZW1). We selected
these gene fragments as many studies showed that they
are informative for intraspecific and interspecific stud-
ies in squamates (e.g. Mendes et al. 2016; Rato et al.
2012; Salvi etal. 2014; Sampaio etal. 2015). PCR con-
ditions and primers were according to the references in
Table2. Each PCR had a total volume of 25μl, using the
MyTaq™ Red DNA Polymerase (BIOLINE), with 5 μl
of Buffer, 5mM of each dNTP, 15mM MgCl2, 1U of Taq
DNA Polymerase, 0.4mM of each primer, and 10–25ng
of DNA. PCR products were checked with electrophore-
sis, in 2% agarose gels stained with GelRed nucleic acid
stain (BIOTIUM), and positive amplification products
were sent to Beckman Coulter Genomics (UK) for purifi-
cation and Sanger sequencing.
Fig. 1 Bayesian tree inferred with BEAST based on mitochondrial
DNA sequence data (12S + ND4). Bayesian posterior probabili-
ties (>95%) are shown above tree branches and bootstrap values for
Maximum Likelihood analyses (>50) are shown below branches (a).
Splits ages between the four main lineages and within the North and
South lineages are represented in the timeline below the tree (see the
text for further details and High Posterior Density intervals, HPD).
Colored dots in the timeline represent the beginning of the Pliocene
(red dot 5.3Mya) and Pleistocene (purple dot 2.6Mya). Sampling
locality codes are represented after specimen codes. Geographic loca-
tion of samples used in this study and of main lineages is represented
in the map (b). Dashed outline shape in the map corresponds to the
distribution range of S. brosseti according to Joger etal. (2006) and
sampling locality codes (1–64) relate to Table1. (Color figure online)
Evol Biol
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Table 1 Samples code, locality, lineage, haplotypes and GenBank accession numbers of the 115 S. brosseti specimens included in this study
Sample code (DB) Lineage Map locality Lat Long 12S Haplotypes GenBank accesion numbers (12S/ACM4/BZW1/MC1R/ND4)
ACM4 MC1R BZW1
A B A B A B
24150 East 1 31.621 −5.560 s3 m39 m39 b28 b28 KY904793/-/KY904910/KY905011/KY905070
1025 East 2 31.621 −5.561 s1 m39 m39 b29 b29 KY904743/-/KY904908/KY904967/KY905037
3348 North 3 32.128 −6.316 s16 a1 a1 m35 m35 KY904795/KY904879/-/KY905013/KY905071
1199 North 4 32.141 −6.399 s16 a1 a1 m35 m35 b32 b32 KY904749/KY904833/KY904953/KY904971/KY905041
2173 North 5 31.887 −6.914 s17 b32 b32 EU014308.1/-/KY904955/-/EU014333.1
2174 North 5 31.887 −6.914 s17 b32 b32 EU014309.1/-/KY904928/-/EU014334.1
930 North 6 32.661 −7.793 s16 a1 a1 m34 m24 b31 b36 KY904823/KY904897/KY904929/KY905033/KY905092
931 North 6 32.661 −7.793 s16 m35 m35 KY904824/-/-/KY905034/KY905093
932 North 6 32.661 −7.793 s16 a1 a1 m35 m24 b34 b37 KY904825/KY904898/KY904949/KY905035/KY905094
916 North 7 32.526 −7.863 s16 a1 a1 m28 m28 b33 b33 KY904818/KY904893/KY904952/KY905029/KY905087
920 North 7 32.526 −7.863 s16 a1 a1 b33 b33 KY904819/KY904894/KY904947/-/KY905088
923 North 7 32.526 −7.863 s16 a1 a3 m28 m27 b33 b33 KY904820/KY904895/KY904945/KY905030/KY905089
926 North 7 32.526 −7.863 s16 a1 a1 m30 m29 b33 b33 KY904821/KY904896/KY904946/KY905031/KY905090
927 North 7 32.526 −7.863 s16 m35 m35 b33 b37 KY904822/-/KY904950/KY905032/KY905091
2179 North 8 33.250 −8.500 s18 a1 a1 EU014300.1/KY904865/-/-/EU014325.1
2190 North 8 33.250 −8.500 s17 a1 a1 m35 m35 b37 b37 EU014314.1/KY904872/KY904922/KY905008/EU014339.1
9070 North 9 31.889 −7.942 s16 KY904815/-/-/-/-
9081 North 9 31.889 −7.942 s16 KY904816/-/-/-/KY905086
9082 North 9 31.889 −7.942 s16 KY904817/-/-/-/-
1404 North 10 31.750 −8.738 s16 a1 a1 m26 m35 b37 b37 KY904758/KY904839/KY904930/KY904977/KY905045
2187 North 11 31.683 −8.850 s4 a1 a1 EU014302.1/KY904869/-/-/EU014327.1
2191 North 11 31.683 −8.850 s17 a1 a1 EU014312.1/KY904873/-/-/EU014337.1
2192 North 11 31.683 −8.850 s16 KY904792/-/-/-/-
2193 North 11 31.683 −8.850 s17 a1 a1 EU014304.1/KY904874/-/-/EU014329.1
2194 North 11 31.683 −8.850 s11 a1 a1 EU014303.1/KY904875/-/-/EU014328.1
13414 North 12 31.507 −8.156 s16 a1 a1 m20 m35 b47 b47 KY904752/KY904836/KY904899/KY904973/KY905042
2185 North 13 31.617 −8.000 s17 EU014301.1/-/-/-/EU014326.1
2184 North 14 31.486 −7.984 s17 a1 a1 EU014310.1/KY904867/-/-/EU014335.1
25201 East 15 30.825 −7.308 s2 a1 a1 m40 m38 b27 b28 KY904794/KY904877/KY904909/KY905012/-
100 North 16 30.953 −8.250 s7 a7 a7 m8 m9 b2 b2 KY904742/KY904826/KY904956/KY904966/KY905036
2195 North 17 31.067 −8.683 s12 EU014305.1/-/-/-/EU014330.1
6813 North 18 31.098 -8.938 s13 KY904796/-/KY904903/-/KY905072
6861 North 18 31.098 −8.938 s13 m1 m13 KY904797/-/KY904954/KY905014/KY905073
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Table 1 (continued)
Sample code (DB) Lineage Map locality Lat Long 12S Haplotypes GenBank accesion numbers (12S/ACM4/BZW1/MC1R/ND4)
ACM4 MC1R BZW1
A B A B A B
142 North 19 30.990 −9.040 s13 m36 m14 KY904759/-/-/KY904978/KY905046
11966 North 20 31.260 −9.162 s15 a1 a5 KY904747/KY904830/-/-/-
11970 North 20 31.260 −9.162 s15 a1 a1 m35 m35 b48 b30 KY904748/KY904831/KY904911;KY904912/KY904970/KY905040
11941 North 21 31.326 −9.384 s16 a1 a1 m35 m32 b39 b51 KY904744/KY904827/KY904923;KY904924/KY904968/KY905038
11974 21 31.326 −9.384 a1 a1 b50 b51 -/KY904832/KY904940/-/-
20091 North 22 31.122 −9.430 s15 a1 a1 m37 m35 b41 b46 KY904781/KY904858/KY904931;KY904932/KY904997/KY905067
20092 North 23 30.998 −9.583 s14 a1 a1 m42 m35 b38 b51 KY904782/KY904859/KY904926;KY904927/KY904998/KY905068
20094 23 30.998 −9.583 b39 b39 -/-/KY904902/-/-
2186 North 24 31.500 −9.767 s17 a1 a1 m35 m35 EU014311.1/KY904868/-/KY905007/EU014336.1
20086 North 25 31.481 −9.760 s14 a1 a1 KY904779/KY904856/-/-/KY905065
20087 North 25 31.481 −9.760 s16 a1 a1 m35 m31 b51 b45 KY904780/KY904857/KY904948/KY904996/KY905066
20113 North 25 31.481 −9.760 s16 a1 a1 m35 m31 b51 b43 KY904786/KY904861/KY904942/KY905002/KY905069
20114 North 25 31.481 −9.760 s14 m35 m35 b51 b43 KY904787/-/KY904901/KY905003/-
20117 North 25 31.481 −9.760 s14 a1 a1 m35 m35 b51 b51 KY904788/KY904862/KY904907/KY905004/-
11953 North 26 31.277 −9.794 s16 a1 a1 m35 m31 KY904745/KY904828/-/KY904969/KY905039
11955 26 31.277 −9.794 b40 b42 -/-/KY904918;KY904919/-/-
11962 North 26 31.277 −9.794 s16 a1 a1 b49 b43 KY904746/KY904829/KY904939/-/-
20077 North 27 30.544 −9.706 s14 a1 a1 m21 m21 KY904778/KY904855/-/KY904995/KY905064
20093 North 27 30.544 −9.706 s16 m43 m35 b44 b35 KY904783/-/KY904920;KY904921/KY904999/-
20095 North 27 30.544 −9.706 s16 a1 a1 m33 m33 KY904784/KY904860/-/KY905000/-
2189 North 28 30.417 −9.583 s5 a1 a1 KY904791/KY904871/-/-/-
19958 North 29 30.299 −9.521 s9 a7 a7 KY904764/KY904842/-/-/-
13762 North 30 30.403 −8.620 s6 m13 m5 KY904753/-/-/KY904974/-
13769 North 31 30.354 −8.577 s6 KY904754/-/-/-/-
13770 32 30.142 −8.485 m41 m7 -/-/-/KY904975/-
13771 North 33 30.059 −9.084 s9 a7 a7 m13 m11 KY904755/KY904837/-/KY904976/-
779 North 34 30.058 −9.087 s8 b3 b1 KY904798/-/KY904914;KY904915/-/KY905074
786 North 34 30.058 −9.087 s9 KY904799/-/-/-/-
787 North 34 30.058 −9.087 s10 a7 a7 KY904800/KY904881/-/-/KY905075
788 North 34 30.058 −9.087 s9 m6 m6 KY904801/-/-/KY905015/KY905076
793 North 35 30.028 −9.052 s9 a7 a7 m6 m12 KY904803/KY904883/-/KY905017/-
789 North 36 29.950 −9.010 s9 a7 a1 m1 m10 KY904802/KY904882/-/KY905016/-
2196 South 37 30.100 −9.550 s28 a1 a1 m47 m59 EU014306.1/KY904876/-/KY905009/EU014331.1
20076 North 38 30.038 −9.639 s4 m3 m45 KY904777/-/-/KY904994/KY905063
20096 North 38 30.038 −9.639 s4 m3 m25 b4 b5 KY904785/-/KY904904/KY905001/-
Evol Biol
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Table 1 (continued)
Sample code (DB) Lineage Map locality Lat Long 12S Haplotypes GenBank accesion numbers (12S/ACM4/BZW1/MC1R/ND4)
ACM4 MC1R BZW1
A B A B A B
2197 North 39 29.883 −9.600 s4 m82 m82 EU014307.1/-/-/KY904962/EU014332.1
2198 North 39 29.883 −9.600 s17 m4 m23 EU014313.1/-/-/KY905010/EU014338.1
19955 North 40 29.865 −9.500 s5 m22 m22 KY904762/-/-/KY904981/KY905049
19956 North 40 29.865 −9.500 s5 m13 m14 KY904763/-/-/KY904982/-
19957 40 29.865 −9.500 m22 m10 -/-/-/KY904983/-
13772 Anti-Atlas 41 29.765 −9.136 s21 a1 a1 m18 m17 KY904756/KY904838/-/KY904987/KY905043
13773 Anti-Atlas 42 29.772 −9.167 s22 m50 m17 KY904757/-/-/KY904986/KY905044
807 Anti-Atlas 43 29.743 −8.961 s20 a1 a1 m15 m16 -/KY904886/-/KY904988/KY905079
803 Anti-Atlas 44 29.512 −9.062 s19 a1 a1 m15 m19 b26 b26 -/KY904885/KY904933/KY904989/KY905078
794 South 45 29.580 −9.396 a1 a1 m47 m56 b17 b22 -/KY904884/KY904917;KY904916/KY905018/KY905077
800 South 45 29.580 −9.396 s27 m65 m66 b16 b15 -/-/KY904951/KY905019/-
853 South 45 29.580 −9.396 s27 m55 m71 -/-/-/KY905020/KY905080
854 South 45 29.580 −9.396 s27 a1 a1 m57 m58 -/KY904887/-/KY905021/KY905081
12163 South 46 29.703 −9.673 s26 a1 a5 m60 m58 KY904750/KY904834/-/KY904972/-
13287 46 29.703 −9.673 a1 a1 -/KY904835/-/-/-
12166 South 47 29.628 −9.884 s29 KY904751/-/-/-/-
20191 47 29.628 −9.884 a1 a4 m61 m69 -/KY904863/-/KY905005/-
20192 South 47 29.628 −9.884 s29 a4 a4 m67 m69 b16 b16 KY904789/KY904864/KY904906/KY905006/-
20056 South 48 29.651 −9.990 s29 a1 a5 m51 m69 KY904776/KY904854/-/KY904993/KY905062
860 South 49 29.596 −10.028 s31 a1 a1 m63 m69 b25 b11 -/KY904888/KY904937;KY904938/KY905022/KY905082
861 South 49 29.596 −10.028 s31 m60 m59 -/-/-/KY905023/KY905083
866 South 49 29.596 −10.028 s29 a1 a1 m59 m62 -/KY904889/-/KY905024/KY905084
867 South 49 29.596 −10.028 s30 a1 a5 m46 m62 b23 b23 -/KY904890/KY904944/KY905025/KY905085
868 49 29.596 −10.028 m69 m69 -/-/-/KY905026/-
869 South 49 29.596 −10.028 s30 a1 a6 -/KY904891/-/-/-
870 49 29.596 −10.028 m51 m46 -/-/-/KY905027/-
871 49 29.596 −10.028 m51 m51 b13 b14 -/-/KY904943/KY905028/-
2188 50 29.400 −9.717 a1 a5 -/KY904870/-/-/-
20020 South 51 29.387 −10.172 s24 a1 a1 m61 m68 KY904773/KY904851/-/KY904992/KY905059
1590 South 52 29.092 −9.886 s25 a1 a1 m53 m72 b24 b23 KY904760/KY904840/KY904934/KY904979/KY905047
1615 South 52 29.092 −9.886 s25 a1 a1 m54 m64 KY904761/KY904841/-/KY904980/KY905048
19972 South 53 29.064 −9.933 s23 a1 a4 m48 m70 b12 b13 KY904768/KY904845/KY904935/KY904985/KY905053
7057 54 28.819 −10.384 a1 a1 m77 m82 -/KY904880/-/KY904958/-
20194 South 54 28.819 −10.384 s34 KY904790/-/-/-/-
20040 South 55 28.719 −10.302 s33 a1 a1 m52 m79 KY467067/KY904853/-/KY904965/KY905061
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Table 1 (continued)
Sample code (DB) Lineage Map locality Lat Long 12S Haplotypes GenBank accesion numbers (12S/ACM4/BZW1/MC1R/ND4)
ACM4 MC1R BZW1
A B A B A B
19981 South 56 28.499 −10.428 s36 a1 a1 m49 m80 KY904769/KY904847/-/KY904963/KY905055
19993 South 56 28.499 −10.428 s36 a1 a1 m76 m78 KY904771/KY904849/-/KY904991/KY905057
20026 South 57 28.498 −10.478 s36 a1 a2 b19 b20 KY904774/KY904852/KY904913/-/KY905060
20042 South 57 28.498 −10.478 s36 m52 m82 b19 b18 KY904775/-/KY904925/KY904957/-
20043 57 28.498 −10.478 b9 b10 -/-/KY904905/-/-
19999 South 58 28.607 −10.519 s37 a1 a1 m75 m81 KY904772/KY904850/-/KY904964/KY905058
19968 South 59 28.628 −10.791 s35 m75 m74 KY904765/-/-/KY904984/KY905050
19983 South 60 28.416 −11.398 s35 a1 a2 m73 m73 b21 b21 KY904770/KY904848/KY904941/KY904990/KY905056
874 South 61 28.028 −11.357 a1 a1 -/KY904892/-/-/-
19971 South 62 28.221 −11.750 s32 a1 a2 m82 m82 b8 b8 KY904767/KY904844/KY904900/KY904960/KY905052
19970 South 63 26.155 −14.418 s32 a1 a2 m82 m82 KY904766/KY904843/-/KY904959/KY905051
19974 South 64 26.075 −14.457 s3 a1 a2 m82 m82 b6 b7 -/KY904846/KY904936/KY904961/KY905054
Table 2 Primers and amplification conditions used in this study
Gene 12S ND4 ACM4 BZW1 MC1R
Step T (ºC) Time X T (ºC) Time X T (ºC) Time X T (ºC) Time X T (ºC) Time X
Initial Denaturation 95° 11 94° 31 94° 51 94° 31 92° 21
Denaturation 95° 15ʺ35 94° 335 94° 51 94° 337 92° 235
Annealing 48° 15ʺ94° 30ʺ94° 30ʺ32 94° 30ʺ92° 1
Extension 72° 10ʺ48° 30ʺ55° 45ʺ62° 45ʺ55° 45ʺ
Final Extension 72° 101 72° 40ʺ1 72° 11 72° 172° 11
Primer Forward 12S L ND4 Tg-F Tar1 MC1R F
Primer Reverse 12S H LEU Tg-R Tar2 MC1R R
Citation Kocher etal. (1989) Arévalo etal. (1994) Gamble etal. (2008) Fujita etal. (2010) Pinho etal. (2010)
Evol Biol
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Phylogenetic Analyses andMolecular Dating
We used Geneious Pro v 4.8 (Biomatters) to assemble the
contigs and MAFFT v 7.0.17 (Katoh and Standley 2013) to
align each set of sequences, using the default parameters.
ND4, MC1R, ACM and BZW1 coding sequences were
translated into aminoacids to confirm and correct the frame
of the alignment to 1. SplitsTree (Huson 1998) was used to
check for recombination in nuclear genes. For each nuclear
gene, an input file with identification of all heterozygous
positions was created using SeqPHASE (Flot 2010). This
file was then used to run PHASE v 2.1.1 (Stephens etal.
2001; Stephens and Donnelly 2003; Stephens and Scheet
2005) in order to reconstruct the phase of each haplotype.
The threshold used to run PHASE was 0.60, as this has
been shown to reduce the number of unresolved genotypes
with little or no increase in false positives (Garrick etal.
2010). We inferred phylogenetic relationships between hap-
lotypes for each locus, using Median Joining networks. We
used the software Network v 5 (available at http://fluxus-
engineering.com; Bandelt etal. 1999), with the parameter
epsilon set to 0. For the 12S marker we also used the par-
simony connection criterion implemented in the software
TCS (Clement etal. 2000) in order to test whether distinct
mitochondrial lineages clustered in distinct networks.
Phylogenetic analyses based on concatenated mitochon-
drial data (mtDNA data) were performed using Maximum
Likelihood (ML), and Bayesian Inference (BI) methods
using S. fasciatus as an outgroup (Rato and Harris 2008).
Only individuals sequenced for both 12S and ND4 were
used in this concatenated analysis. In order to determine
the best fitting nucleotide model and loci partition for each
phylogenetic analysis, we used the software Partition-
Finder v 1.1 (Lanfear etal. 2012). We tested in Partition-
Finder schemes including codon partitions for ML analy-
ses, whereas for BI analyses we selected among schemes
with genes unpartitioned (to avoid over-parameterized
models). Parameters used were branchlengths = linked and
model_selection = BIC. We implemented partition schemes
and models as described in Table3. Before running the ML
inference, similar haplotypes were collapsed using the soft-
ware ALTER (Glez-Peña etal. 2010). The ML analysis was
conducted with the software GARLI v 2.0 (Genetic Algo-
rithm for Rapid Likelihood Inference; Zwickl 2006) where
a tree search was conducted using between 1000 and 5000
generations (parameter genthreshfortopoterm) considering
a stochastic algorithm, each resulting in a single best tree.
The resulting likelihood values were compared and the best
value was obtained for 1000 generations. Bootstrap sup-
port was calculated from 1000 bootstrap (BP) pseudo-rep-
licates (Felsenstein 1985), using genthreshfortopoterm set
to 1000 and the best tree as a starting tree. A majority rule
consensus tree was generated using the software Phyutility
v 2.2 (Smith and Dunn 2008).
The software BEAST v 1.8.0 (Drummond and Ram-
baut 2007) was used for BI analyses with node age esti-
mation. Two individual runs of 30 × 106 generations were
performed with a sampling frequency of 3000, substitution
models and clock models were unlinked and trees linked as
mitochondrial markers are actually linked in the genome.
Absolute divergence times were estimated implementing
an uncorrelated relaxed lognormal clock. We used previ-
ous estimates of mean rates of molecular evolution for
the 12S marker in Tarentola sp. geckos from the Canary
Islands (mean: 0.0102, stdev: 0.00207, substitutions per
site per million years; Carranza and Arnold 2012) as a prior
for the 12S.ucld.mean (normal distribution; m = 0.0102,
s = 0.001). This evolutionary rate was used for divergence
time estimation due to the lack of a fossil calibration for
Saurodactylus. Additionally, we implemented the multispe-
cies coalescent model of *BEAST to assess relationships
between main clades recovered in phylogenetic analyses
based on single locus and concatenated matrix. This spe-
cies tree approach assumes no gene flow between taxa,
which in our case is supported by the reciprocal monophyly
observed at the mitochondrial loci and the lack of haplo-
type sharing observed at nuclear loci (see Results). Two
individual runs of 100million generations were performed
with a sampling frequency of 10,000. Posterior trace plots
and effective sample sizes of the Bayesian runs were moni-
tored in Tracer v 1.6 (Rambaut etal. 2014) to ensure con-
vergence. The results of the individual runs were combined
in LogCombiner and the ultrametric tree and Bayesian Pos-
terior Probabilities (PP) at each node was obtained with
TreeAnnotator, discarding 25% of the samples as burnin
(both provided with the BEAST package).
Interspecific mtDNA sequence divergence between
major clades recovered in phylogenetic analyses was
Table 3 Partition schemes and models used in phylogenetic analyses
Dataset (Software) Partition Model
mtDNA (GARLI) 12S, ND4_pos1,
tRNAhist
HKY+G
ND4_pos2 TIM+I
ND4_pos3 TrN
mtDNA (Beast) 12S HKY+I+G
ND4 TN93+I+G
mtDNA+nDNA (*Beast) 12S HKY+I+G
ND4 TN93+I+G
ACM4 HKY
MC1R HKY+I+G
bzw1 HKY+G
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calculated in MEGA v 6 (Tamura etal. 2013) as uncor-
rected p-distances.
Bayesian Species Delimitation
Bayesian species delimitation was conducted using BP&P
v 2.2 (Rannala and Yang 2003; Yang and Rannala 2010),
with the full phased dataset for the three nuclear loci, in
order to test species limits within the nuclear data on groups
derived from mtDNA. The model assumes no admixture
following speciation, which is an assumption motivated
by the biological species concept and a reversible-jump
Markov Chain Monte Carlo (rjMCMC) to estimate the
posterior distribution for species delimitation models.
Ensuring adequate rjMCMC mixing involves specifying a
reversible jump algorithm to achieve dimension matching
between species delimitation models with different num-
bers of parameters (Yang and Rannala 2010). We ran the
analysis for both algorithm0 and algorithm1 with a fine-
tuning parameter of ε = 15 and ε = 2 respectively, in order
to guarantee a good mixing and similar results between
rjMCMC algorithms (Yang and Rannala 2010). Each spe-
cies delimitation model was assigned equal probabilities
for rooted trees (speciesmodelprior = 1). The rjMCMC
analysis was run for 200,000 generations with a sampling
interval of 1 and a burn-in of 20,000 (10%). As guide tree
we used the species tree obtained with *BEAST. The esti-
mation of the marginal posterior probability of speciation
associated with each node in the guide tree (i.e. speciation
probabilities as in Leaché and Fujita 2010) is performed by
summarizing the probabilities for all models that support
a particular speciation event. Speciation probability values
of ≥95% are considered as a strong support for a speciation
event (Leaché and Fujita 2010).
The PP for models can be mainly affected by the prior
distributions on the ancestral population size (θ) and root
age (τ), with large values for θ and small values for τ favor-
ing conservative models containing fewer species (Yang
and Rannala 2010). Therefore, we conducted the analy-
ses considering three different combinations of prior set-
tings for these parameters (as in Leaché and Fujita 2010),
which are given as gamma G(α, β) distributions: relatively
large ancestral population size and shallow divergences
(θ = G[1,10], τ = G[2,2000]), relatively large ances-
tral population size and deep divergences (θ = G[1,10],
τ = G[1,10]), and relatively small ancestral population size
and shallow divergences (θ = G[2,2000], τ = G[2,2000]).
The first combination is a conservative one, favoring
models containing fewer species. Only speciation events
supported by all models were accepted. This cumulative
approach, although quite conservative is, in our opinion,
the most objective one to interpret the results. All analyses
were run twice to confirm consistency between runs.
Results
We obtained 1055 bp of the mitochondrial DNA for 72
individuals of S. brosseti (370bp for 12S and 685bp for
ND4). For the nuclear markers we obtained 352bp from
71 individuals for ACM4, 582bp from 79 individuals for
MC1R and 1014bp from 47 individuals for BZW1. At the
BZW1 fragment some individuals presented some indels,
which were phased manually. Nucleotide sequences gen-
erated in this study have been submitted to GenBank and
accession numbers are reported in Table1.
Phylogenetic Analysis andMolecular Dating
Maximum Likelihood and Bayesian trees based on mito-
chondrial data produced very similar topologies and
revealed four major groups (PP = 1, BP equal to 72, 99, 94,
and 50 for the North, Anti Atlas, East, and South groups,
respectively). A clear geographical distribution can be seen
when plotting the different lineages geographically (Fig.1).
The ‘South lineage’ (green) ranges from Agadir (Morocco)
to the most southern known location of the species (West-
ern Sahara); the ‘North lineage’ (blue) has a distribution
from some kilometers south of Agadir and extending up to
the most northern known geographic location; the ‘East lin-
eage’ (orange) is represented by two locations, east of the
High Atlas; the ‘Anti-Atlas lineage’ (pink) is located in the
region of the Drâa Valley. In this region North and South
lineages also occur in sympatry, representing the region
with the highest genetic diversity.
Relationships among the main clades as depicted by
mitochondrial ML and BI trees and a multilocus species
tree show that the South lineage is sister to a clade com-
posed of the East, Anti-Atlas and North lineages, with the
East lineage sister to the Anti-Atlas and North lineages
(Fig.1 and Supplementary Fig.1).
Divergence time estimates revealed that the first split
between S. brosseti lineages occurred at around 8.05Mya
(High Posterior Density interval, HPD: 13.2–4.8), sepa-
rating the South lineage from the other three. At around
5.61Mya (HPD: 9.2–2.8), the East lineage separated from
the North and Anti-Atlas lineages, which split from one
another roughly at 4.49Mya (HPD: 7.4–2.4). The species
tree obtained by *BEAST revealed the same phylogenetic
relationships between the four main lineages as the ML and
BI trees based on mtDNA data, (Supplementary Fig.1).
Genetic distances between lineages for 12S and ND4 are
presented in Table4. Average sequence divergence between
groups within S. brosseti ranged from 6.9% (between North
and Anti-Atlas lineages) to 11.3% (between South and
Anti-Atlas) for ND4 and from 4.8% (between East and
Anti-Atlas) to 5.9% (between East and South) for 12S.
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The parsimony network analysis based on 12S
shows the same four main clades as distinct networks
(Supplementary Fig.2) and allows the assignation of 28
specimens (not included in the ML and BI tree because
they were lacking the ND4 sequence) to their correspond-
ing geographical clade (Figs.1, 2).
Phylogenetic networks for two of the three nuclear
genes show an overall pattern of no haplotype sharing
between populations belonging to distinct mitochondrial
lineages, with only a few exceptions (Fig. 2). ACM4
shows little variation with the most common haplotype,
a1, shared by 65 individuals belonging to all lineages and
six haplotypes, which differ from a1 by one substitution.
BZW1 and MC1R show 51 and 82 unique haplotypes,
respectively, which are grouped according to mitochon-
drial lineages, although without a clear pattern of rela-
tionships between groups. For both markers, haplotypes
are not shared among populations belonging to distinct
mitochondrial lineages with a single exception (MC1R,
haplotype m82).
Table 4 Average genetic distances between lineages for 12 S and
ND4. Between brackets are the average net genetic distances between
lineages
ND4 North Anti-Atlas East South
12s
North 0.069
(0.052)
0.084
(0.069)
0.110 (0.070)
Anti-Atlas 0.053
(0.033)
0.080
(0.077)
0.113 (0.085)
East 0.049
(0.031)
0.048
(0.044)
0.110 (0.084)
South 0.057
(0.034)
0.051
(0.042)
0.059
(0.052)
Fig. 2 Haplotype Median Joining networks of the ACM4 (a),
MC1R (b) and BZW1 (c) nuclear gene fragments. Haplotypes are
represented by circles with sizes proportional to their frequency and
colored according to the mitochondrial lineages of the respective pop-
ulation as in Fig.1. White haplotypes indicate individuals for which
mitochondrial data (and thus lineage information) were not available.
Mutational steps between haplotypes are indicated with numbers
above network lines (absent when there is only 1 mutational step).
Haplotypes are coded and reported in Table1 (a1 to a7 for ACM4;
m1 to m82 for MC1R; b1 to b51 for BZW1)
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Bayesian Species Delimitation
Bayesian species delimitation analysis supports the given
candidate species when applying both the 95% threshold
and the cumulative approach. Regardless of the prior dis-
tribution for both the ancestral population size (θ) and root
age (τ), the speciation probabilities are always equal to 1 in
all runs (Supplementary Fig.1).
Discussion
Phylogeographic Pattern andInferred Evolutionary
Scenario
The Bayesian and Maximum Likelihood analyses identified
four lineages within Saurodactylus brosseti (Fig. 1), two
widespread ones (North and South Morocco) and two with
small ranges (East Morocco and Anti-Atlas). These line-
ages show a clear allopatric distribution and a narrow area
of co-occurrence in the region of the Drâa Valley (Fig.1),
where three out of four lineages occur (North, South and
Anti-Atlas). The mitochondrial genetic distances between
lineages within S. brosseti (up to 5.9 and 11.3% for 12S and
ND4 respectively, Table4) are at the same level or even
higher than percentages observed in interspecific compari-
sons among other geckos and squamate taxa (e.g. 3.6% for
12 S between Phelsuma m. grandis and P. parkeri, Rax-
worthy etal. 2007; 4.6% for 12S between Acanthocadtylus
boskianus and (A) schreiberi; Tamar et al. 2014; 3% for
12S between Trachylepis vato and T. nancycoutuae; Lima
etal. 2013; 6.7% for ND4 between Blanus tingitanus and
(B) mettetali; Sampaio etal. 2015). Nuclear data confirm
the mitochondrial differentiation observed within S. bros-
seti, since populations belonging to distinct mitochondrial
lineages have no haplotype sharing at the two variable
nuclear loci (MC1R and BZW1). The observed pattern of
cyto-nuclear congruence with four highly divergent allopat-
ric lineages suggests that these groups have been evolving
independently in different portions of the current species’
range for a long time.
The absolute divergence time estimations suggest
that S. brosseti may have started diverging into different
groups about 8.05Mya, with the split between the South
lineage from the North, East and Anti-Atlas lineages. This
date approximately coincides with the uplift of the Atlas
Mountains, which started around 9 Mya and was com-
pleted before 5Mya (Gómez etal. 2000). Approximately
5.61 Mya the East lineage split from the South and the
common ancestor of the North and Anti-Atlas lineages;
these two finally would have separated around 4.49Mya.
The Atlas mountains chains have likely acted as a geo-
graphic barrier to dispersal, causing allopatric divergence
in many taxa, with an identified North/South differentiation
in various species, including squamates (Brown etal. 2002;
Coelho etal. 2014; Fonseca etal. 2012; Habel etal. 2012;
Husemann etal. 2012; Lansari et al. 2015; Salicini etal.
2013; Veríssimo et al. 2016). A similar pattern has been
observed also in Arabia’s Hajar arid mountain systems, an
important centre of diversification (e.g. Garcia-Porta etal.
2017). Thus, it is highly probable that such orogenic pro-
cesses allowed for the divergence observed in S. brosseti as
well, possibly leading to the fragmentation of the species
into four allopatric populations inhabiting isolated sub-
ranges. The independent evolution of each group during
several million years, would explain their reciprocal mono-
phyly observed both at the mitochondrial and the nuclear
level.
Within the widespread lineages (the North and the South
lineages), we observed further phylogeographic subdivi-
sions (Fig.1), occurring during the late Pliocene. The North
lineage is formed by two main clades that are geographi-
cally located north (haplotypes s11 to s18, Supplemen-
tary Fig. 1) and south (haplotypes s4 to s10, Supplemen-
tary Fig.2) of the High Atlas, which have split at around
3.15 (HPD 5.2–1.7)Mya. A similar dating 3.48Mya (HPD
5.7–1.8) was obtained for the subdivision of the South line-
age into two clades, one inhabiting the south of the Anti-
Atlas (haplotypes s23–s31, Supplementary Fig.2) and the
other occupying in regions south of the Atlas Mountains
chain (haplotypes s32–s37, Supplementary Fig.2).
These intra-lineage primary partitions may well be
explained as a consequence of the environmental change
occurring in the late Pliocene in North Africa. It is known
that climatic variability has an effect in the landscape eco-
logical composition, being important for faunal adapta-
tion, selection and evolution (deMenocal 2004). It has been
suggested that varied fauna would have gradually shifted
their habitat as the climate progressively changed to better
fauna-specific conditions (Potts 1998). The period between
3.5 and 3.2 Mya was characterized by dry phases with
associated arid conditions, while before 3.5 Mya strong
short-term fluctuations of climate with a humid and prob-
ably warm climate have been prevailing (Dupont and Leroy
1995). In fact, there is indication of a first step toward drier
climate between 3.5 and 3.2Mya and a second, stronger
one starting at about 2.6 Mya (Dupont and Leroy 1995).
Evidence from pollen records of West Africa also points to
a replacement from humid to arid-adapted fauna, favored
by an aridification shift between 3.2 and 2.6Mya (deMeno-
cal 1995, 2004). Saurodactylus brosseti typically live in hot
arid areas, although they select a microhabitat within this
dry environment which is cooler and moister, using avail-
able refuges during the day and having crepuscular activity
(Meek 2008). Assuming that the environmental require-
ments of S. brosseti have not changed since the Pliocene,
Evol Biol
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the increasing aridification of that time (around 3.5 Mya)
would have forced the species to isolate in optimal climatic
pockets. This isolation could consequently have led to the
primary partitions within the North and South lineages.
Within a more recent timeframe, many populations’
splits within widespread lineages (North and South line-
ages) are placed within the late Pliocene and Pleistocene
and are likely associated to environmental changes asso-
ciated to the climatic changes that occurred during this
period. These events caused multiple events of isolation
and differentiation of populations, followed by expansion
and connectivity (Hewitt 1996) and have been invoked to
explain the existence of intraspecific (geographically coher-
ent) genetic lineages, observed in several squamates from
North Africa such as Podarcis (Lima etal. 2009), Acan-
thodactylus erythrurus and A. pardalis groups (Fonseca
etal. 2008, 2012). In the case of S. brosseti the isolation
in distinct refugia may explain the Pleistocene intra-lineage
splits as well as the restricted area of co-occurence of line-
ages in the northern portion of the range, which may have
arose as a result of secondary contact between expanding
populations following climatic amelioration during post-
glacial stages. Although it was not considered in this study,
given the allopatric subdivisions within these two wide-
spread lineages, it is suggested that future studies take into
consideration the occurrence of further speciation events,
as more candidate species could possibly receive full sup-
port by BP&P.
Systematic andConservation Implications
Although within the Drâa valley region we have an instance
of co-occurrence of three of the four S. brosseti lineages,
there is no evidence that these lineages underwent genetic
admixing. Overall, we observed a striking nuclear pattern
of no haplotype sharing between populations belonging to
distinct mitochondrial lineages (Fig.2). Such a pattern sug-
gests that there has been no gene flow between these popu-
lations since their secondary contact. The observed pattern
of complete lineage sorting in nuclear genealogies further
reinforces the hypotheses that they have been evolving
independently for a long time without exchanging alleles,
or in other words, that the four lineages achieved effective
reproductive isolation.
These results combined with the high genetic distance
observed between lineages (exceeding the value of inter-
specific genetic distance between many squamate sister
species) and results from species delimitation indicate
that these taxa may well be distinct species (Supplemen-
tary Fig.1). Currently, there is still wide disagreement on
species delineation procedures and there is not a consen-
sus on a set of specific statistical rules by which one can
consider a lineage or a population to be a different species
(Fujita etal. 2012). Best taxonomic practice implies the
incorporation of as many lines of evidence as possible
including genetics, morphology, ecology and behavior,
following an integrative approach (Padial etal. 2010). To
our knowledge, there are no discriminant morphological
characters allowing the diagnosis and the identification of
the four main lineages of S. brosseti, which can therefore
be considered a complex of cryptic species (see also Rato
and Harris 2008). However, a detailed analysis of mor-
phological variation as well of ecological requirements of
S. brossetis lineages are currently in progress and will
possibly allow an integrative taxonomic assessment of
these taxa.
Irrespective of their taxonomic designation, the four
lineages of S. brosseti represent independent Evolutionary
Significant Units (ESUs) sensu Moritz (1994) of ancient
origin (Miocene), thus deserving management as sepa-
rate conservation entities. Should these four lineages be
described in the future as different species, brosseti would
remain the specific name for the denominated “North” line-
age in this paper, as the type locality is Wadi Souss (south
to Agadir) (Bons and Pasteur 1957). We highlight the situa-
tion of the lineages with small ranges, specifically the East
and Anti-Atlas lineages, that are particularly worthy of
conservation attention as they may well face high extinc-
tion risk under the current climate change scenario. Addi-
tionally, any habitat alteration is likely to have an impact
on the isolated populations of the East lineage. Although
new populations have been reported for this lineage (Har-
ris etal. 2010; Rosado etal. 2016), densities are apparently
low (pers. obs.), and the habitat of previously recorded pop-
ulations from the Drâa valley no longer appears suitable,
since the narrow valleys where they have historically been
reported have now been heavily manipulated for irrigation
(pers. obs.). The number of known populations of this line-
age is therefore small, and these appear to be isolated and
vulnerable to extinction.
In conclusion, this study highlights the need of detailed
phylogeographic surveys across the Maghreb region in
order to assess cryptic diversity and conservation priori-
ties in this biodiversity hotspot. The Moroccan endemic
lizard-fingered gecko, Saurodactylus brosseti, represents
one additional case within the western Maghreb for which
the pattern of diversity and the processes responsible for
their formation were unexplored. The implementation of
a range-wide sampling, a multi-locus dataset, and time
calibrated phylogenetic approach proved fruitful for the
inference of the evolutionary and biogeographic processes
responsible for the origin and distribution of the extensive
genetic diversity observed in S. brosseti and will warrant
further taxonomic revision. In the future, the application of
such an approach to additional Maghrebian species is cru-
cial to understand the factors that led to high endemism and
Evol Biol
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diversity observed in this region, and for taking actions on
biodiversity conservation.
Acknowledgements Fieldwork was supported by the ‘Percy
Sladen Memorial Fund’ grant (to DS in 2013) and the ‘British Her-
petological Society’ and ‘the Explorers Fund’ grants (to DR in 2014
and 2015 respectively). CR, DS and DJH are supported by Fundação
para a Ciência e Tecnologia (FCT, Portugal) under the Programa
Operacional Potencial Humano—Quadro de Referência Estratégico
Nacional funds from the European Social Fund and Portuguese
Ministério da Educação e Ciência (CR: post-doctoral Grant SFRH/
BPD/92343/2013; DS: post-doctoral Grant SFRH/BPD/105274/2014;
DJH: contract IF/01627/2014). DS is currently supported by the pro-
gram ‘Rita Levi Montalcini’ for the recruitment of young researchers
at the University of LAquila.
Compliance with Ethical Standards
Conflict of interest The authors declare that they have no conflict
of interest.
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