Open Access

Extreme genetic diversity in the lizard Atlantolacerta andreanskyi(Werner, 1929): A montane cryptic species complex

BMC Evolutionary Biology201212:167

DOI: 10.1186/1471-2148-12-167

Received: 12 April 2012

Accepted: 23 August 2012

Published: 4 September 2012

Abstract

Background

Atlantolacerta andreanskyi is an enigmatic lacertid lizard that, according to the most recent molecular analyses, belongs to the tribe Eremiadini, family Lacertidae. It is a mountain specialist, restricted to areas above 2400 m of the High Atlas Mountains of Morocco with apparently no connection between the different populations. In order to investigate its phylogeography, 92 specimens of A. andreanskyi were analyzed from eight different populations across the distribution range of the species for up to 1108 base pairs of mitochondrial DNA (12S, ND4 and flanking tRNA-His) and 2585 base pairs of nuclear DNA including five loci (PDC, ACM4, C-MOS, RAG1, MC1R).

Results

The results obtained with both concatenated and coalescent approaches and clustering methods, clearly show that all the populations analyzed present a very high level of genetic differentiation for the mitochondrial markers used and are also generally differentiated at the nuclear level.

Conclusions

These results indicate that A. andreanskyi is an additional example of a montane species complex.

Keywords

Atlantolacerta andreanskyi Lacertidae Mountain specialist High Atlas Mountains Phylogeography Morocco

Background

An emerging pattern among European biotas is that the accentuated environmental instability that occurred during the Pleistocene did not lead to increased speciation rates, with many species and populations originating during the Miocene and proceeding through the Quaternary [1, 2]. In many species, population fragmentation was triggered by the beginning of the Messinian Salinity Crisis, a short (600 000 years) but crucial period that occurred between 5.9 and 5.3 Mya during which the Mediterranean Sea desiccated almost completely, producing a general and drastic increase in aridity around the Mediterranean Basin [3, 4]. As a result of this increased aridity, forests continued to be replaced by more open and arid landscapes forcing the mesic species to retreat to the moister Atlantic-influenced areas and to the mountainous regions, leading to high speciation in some groups [5, 6].

Various studies have attempted to unravel the different roles that the global aridification at the end of the Miocene and the Pleistocene glacial cycles have played in the diversity and distribution of European faunas [7]. However, little is known about the effects that these climatic changes had on species living further South, in the African continent. Recent assessments of central African chameleons have uncovered evidence of long-isolated evolutionary histories, with the survival of palaeoendemics leading to considerable diversity [8]. In general, reptiles are excellent model organisms to assess the relative role that the Pre-Quaternary and Quaternary major climatic events have played in the origin, evolution and distribution of species [9]. Available data from some herpetofauna indicate that a similar pattern to the neighboring Iberian Peninsula exists in North Africa, with deep lineages originating at the end of the Miocene (Chalcides[10], Acanthodactylus[1113], Podarcis[2, 14, 15], Saurodactylus[16], Ptyodactylus[17], Salamandra[18], Pleurodeles[19]). However, the lack of informative nuclear markers in most of these studies may prevent the recovery of the true evolutionary history of the group [eg. [20, 21], and makes it difficult to ascertain if these lineages correspond to species complexes or not. Since there is a strong likelihood of discordance between gene trees and species trees [2224], information from different genetic markers (mitochondrial and nuclear) is thus necessary for delimiting evolutionary lineages, as well as for establishing phylogenetic relationships.

Despite being key concepts in the fields of systematic and evolutionary biology, recognizing and delimiting species are highly controversial issues ([e.g. [25, 26]). Recognizing species is not only a taxonomic challenge, but is also essential for other biological disciplines such as biogeography, ecology and evolutionary biology [27], and has serious consequences for conservation biology and the design of effective conservation plans [28, 29]. Delimiting species is also the first step towards discussing broader questions on evolution, biogeography, ecology or conservation. Recently, thanks to intellectual progress made in the field with the aim of identifying a common element among all the different species concepts, a single, more general, concept of species known as General Lineage Species Concept has been suggested [30]. This unified species concept emphasizes the common element found in many species concepts, which is that species are separately evolving lineages. Therefore, properties like reciprocal monophyly at one or multiple loci, phenotypic diagnosability, ecological distinctiveness, etc. are not part of the species concept but are used to assess the separation of lineages and to species delimitation [31]. This separation between species conceptualization and species delimitation and the proposal of a unified species concept has concentrated efforts in the development of new approaches for species delimitation, as for example with “integrative taxonomy” [32, 33], among others]. Under this new approach, species delineation is regarded as an objective scientific process that results in a taxonomic hypothesis. Therefore, the level of confidence in the taxonomic hypothesis supported by several independent character sets is much higher than for species supported by only one character [34]. Such an integrative view is especially useful in the case of taxonomic groups that are morphologically conservative, where cryptic species have probably been overlooked [17, 35, 36].

Normally, high altitude species carry signatures of the expansion and contraction cycles occurred during glacial and interglacial periods [3739]. Because of this, they are of particular interest to study historical responses to climate change, since they are adapted to a small window of environmental changes, and usually present low tolerance to high temperatures [40]. In Europe, high altitude species often seem to have persisted through glacial periods by short movements to lower altitudes rather than to the classic "southern refugia" of lowland species. In this way current ranges may primarily reflect postglacial expansions [41]. However, it is not clear if the same phenomenon occurs in African montane taxa.

Atlantolacerta andreanskyi (Werner, 1929) is a lacertid lizard endemic to the western and central parts of the High Atlas Mountains of Morocco. It is restricted to areas above 2400 m [42, 43], where it is frequently found in the vicinity of small watercourses or plateaus in the top of the mountains that retain some water from rain or snowmelt. Habitat is normally screes and areas with boulders, meadows and, in particular, the base of cushion-like thorny plants in these places [42]; personal observation]. Although A. andreanskyi had initially been placed in several different genera within the subtribe Lacertini [4448], recent phylogenetic analyses based on mitochondrial DNA and a combination of mitochondrial and nuclear markers [49, 50] suggest that A. andreanskyi is a member of the subtribe Eremiadini, and apparently sister to the remaining Eremiadini. This position would conform to this species lacking the synapomorphies that characterize most other Eremiadini, namely a derived condition of the ulnar nerve and the presence of a fully developed armature in the hemipenis, which has folded lobes when retracted. It is also distinctive within the Eremiadini regarding the presence of enlarged masseteric scale [49]. Because of its phylogenetic position, without close relationship to any other genus of Eremiadini and its distinctive morphology it was recently placed in a new monotypic genus, Atlantolacerta[49]. Atlantolacerta andreanskyi is distributed across 440 Km (straight line) of mountainous terrain, with the different populations presenting an apparently disjunct distribution ([42, 43]; see Figure 1). As with many montane species, the situation observed in A. andreanskyi is similar to an archipelago, with the different “islands” being represented by mountaintops disconnected due to areas of unsuitable habitat below 2400 m. As a result of this scenario, minimal gene flow is currently expected between the different populations; however, it is not known how the different climatic events occurred during the Miocene and Pleistocene have affected this species. Even though some aspects of the biology of A. andreanskyi are already well known [e.g. [51, 52], the genetic structure of the different populations, as well as the relationships between the different populations have never been assessed before.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-12-167/MediaObjects/12862_2012_Article_2101_Fig1_HTML.jpg
Figure 1

Atlantolacerta andreanskyi distribution map. The color dots represent the localities of the populations sampled for this work, J. Awlime (yellow), J. Sirwa (pink), Oukaimeden (red), Toubkal (orange), Tizin Tichka (dark blue), J. Azourki (light blue), Outabati (light green), and J. Ayache (dark green). The white dots represent the distributions of the species by Bons and Geniez [42].

Therefore, in order to shed some light on the previous questions and attempt to assess the evolutionary history of the species and identify the number of lineages, we sampled the distribution area of the species and performed several combined phylogenetic reconstructions and clustering analyses, using both mtDNA and nuclear markers.

Results

Mitochondrial genealogies

A total of 1108 base pairs (bp) of concatenated mtDNA (12S rRNA 330 bp, ND4 592 bp and tRNA-His 186 bp) were obtained for 89 A. andreanskyi. The concatenated alignment of the ingroup sequences revealed 30 haplotypes (3 from Tizin Tichka, 7 from J. Ayache, 5 from J. Sirwa, 2 from Oukaimeden, 7 from J. Azourki, 2 from Outabati, 2 from Toubkal and 2 from J. Awlime) and contained 241 variable sites, of which 232 were parsimony informative.

Analyses of the concatenated mtDNA data were mostly congruent (Figure 2A). Seven well-supported lineages were recovered from these analyses (pp > 0.95 and BP > 70%), corresponding to the populations from J. Awlime, J. Sirwa, Tizin Tichka, J. Azourki, Outabati, J. Ayache, and Oukaimeden and nearby Toubkal that were grouped together. Regarding the relationships among these clades, we could distinguish three main groups, Oukaimeden and Toubkal with J. Sirwa from the southern end of the distribution range; J. Ayache with Outabati from the northern distribution, and Tizin Tichka with J. Azourki from the central distribution range. The population from J. Awlime, from the extreme South of the range, is a genetically distinct lineage related to the northern group, although, both ML and BI analysis weakly support this topology (see Figure 2A).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-12-167/MediaObjects/12862_2012_Article_2101_Fig2_HTML.jpg
Figure 2

Trees resulting from partitioned Bayesian analysis. (A) mitochondrial DNA tree (12S, ND4 and flanking tRNA-His), (B) nuclear concatenated tree (RAG1, ACM4, MC1R, PDC and C-MOS), (C) Concatenated tree from the combined mitochondrial and nuclear DNA data. The partitions used the models described in the text. Bayesian posterior probabilities (0–1) and bootstrap values (> 50%) for ML (1–100) are indicated near the branches, (D) Species tree from mitochondrial and nuclear DNA data from the Bayesian Inference of Species Trees (STARBEAST). Clade posterior probabilities are shown to the left of the nodes, and divergence times and 95% intervals (calculated in BEAST using only ND4 + tRNA-His), to the right of the nodes. The trees were rooted using Podarcis bocagei, P. hispanica and P. carbonelli. The colors represent the different populations.

All the populations present a low level of diversity in the mitochondrial DNA (uncorrected genetic distances 0–0.5% for the ND4 + tRNA-His and 0 – 0.2% for the 12S; see Table 1), and a very high level of genetic divergence between populations (5.5 – 16.5% in the ND4 + tRNA-His and 2.5 – 6.6% in the 12S).
Table 1

Genetic distances and divergence time estimate between populations

A

 

Pop p-distance (%) 12S, ND4

Tizin Tichka

Oukaimeden

J. Sirwa

J. Ayache

Outabati

J. Azourki

Toubkal

J. Awlime

 

0.1

0.4

0.3

0.5

0.2

0

0.4

0.1

Tizin Tichka

 

13.1

12.7

14.5

10.5

15.3

12.9

13.6

0

        

Oukaimeden

4

 

7.7

15

13.2

16.1

1.7

13.2

0

        

J. Sirwa

4.2

2.8

 

16.1

12.7

16.5

7.5

11.6

0.2

        

J. Ayache

5.4

5.7

4.8

 

12.7

5.5

14.4

14.1

0.1

        

Outabati

4.3

4.3

3.8

6.6

 

14.2

13.2

13.1

0.1

        

J. Azourki

5.4

5.7

4.2

1.6

6

 

16

14

0

        

Toubkal

3.7

0.3

2.5

5.4

4

5.4

 

12.6

0

        

J. Awlime

4

4.7

4.5

5.1

6.4

5

4.3

 

0

        

B

     

C

  

Pop p-distance (%) 12S and ND4

J. Awlime

    
 

JAy + Out

Tiz + JAz

 

Ouk + JSi + Tou

 

Beast Ma (95% HPD)

ND4

 

0.9

2.3

0

1.5

    

JAy + JAz

 

13.7

13.4

14.6

 

North - South

7.6 (4.3-11.9)

2.9

     

Jaw - Ouk

5.6 (2.5-9.7)

Tiz + Ou

5.9

 

12.9

12.4

 

JAz + JAy - Out + Tiz

6.4 (3.1-10.2)

5.2

     

Ouk - JSi

2.9 (1.0-5.6)

J. Awlime

5

5.2

 

12.2

 

Out - Tiz

4.3 (1.4-7.8)

0.1

     

JAz - JAy

2.4 (0.8-4.4)

Ouk + JSi + Tou

5.1

4.1

4.6

  

Ouk - Tou

0.5 (0.1-1.2)

0.4

       

(A) Genetic distance (12S and ND4 + tRNA-His) between all the populations and (B) between main groups; and (C) divergence time estimates, calculated using BEAST with ND4 and tRNA-His. The the diversity of each population is below the population's names.

Nuclear genealogies

A total of 77 specimens of A. andreanskyi were sequenced for five nuclear genes. The ACM4 was 447 bp long, presenting 47 haplotypes and 34 polymorphic sites, 33 of them parsimony informative; C-MOS was 534 bp long, with 32 haplotypes and 21 polymorphic sites, all of them parsimony informative; MC1R was 635 bp long, with 57 haplotypes and 36 variable sites, 35 of them parsimony informative; PDC was 441 bp long, with 60 haplotypes and 29 variable sites, 26 of them parsimony informative; RAG1 was 528 bp long, with 38 haplotypes and 19 variable sites, 18 of them parsimony informative.

The differences in the genetic distances between the lineages are congruent with the geographic distance between them, supporting the grouping of the lineages in three main groups as seen in the analysis of mitochondrial sequences.

The concatenated analyses of the 5 unphased nuclear markers are congruent with the results obtained in the mitochondrial DNA tree, although with some differences (Figure 2B). Despite recovering the three main groups observed in the mtDNA analysis, according to the nuclear markers the J. Awlime population is not sister to the northernmost populations but branches off inside a polytomy with the westernmost lineages at the base of the tree. It is possible to distinguish some of the lineages, although in some cases they are not monophyletic. The J. Ayache population is monophyletic but makes Outabati paraphyletic. The same happens with Tizin Tichka, which makes the population from J. Azourki paraphyletic. The population from Oukaimeden is polyphyletic.

Concatenated analysis (mtDNA and nDNA)

The results of the ML and BI analyses of the mtDNA and nDNA (Figure 2C) support the same seven lineages as recovered in the mitochondrial analysis, although in this case J. Awlime is sister to the central and northern lineages (Tizin Tichka, J. Azourki, Outabati, and J. Ayache) instead of being sister to only the northernmost lineages (Figure 2A). As in the mtDNA analysis (Figure 2A), the relationship of J. Awlime with the central and northern lineages is very poorly supported. This result was expected, given the higher resolving power of the mtDNA that contributed with 241 variable sites versus the 150 from the nDNA.

Nuclear networks

As show in Figure 3 and Table 2, there is a moderate degree of haplotype sharing between populations, with most of them lacking private alleles for the nuclear genes analyzed.
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-12-167/MediaObjects/12862_2012_Article_2101_Fig3_HTML.jpg
Figure 3

Parsimony networks corresponding to MC1R (A), RAG1 (B), C-MOS (C), ACM4 (D) and PDC (E) nDNA sequence variation from all the populations. The colors used were the same as the used in the map (Figure 1) and trees (Figure 2), J. Awlime (yellow), Toubkal (orange), Oukaimeden (red), J. Sirwa (pink), Tizin Tichka (dark blue), J. Azourki (light blue), Outabati (light green), and J. Ayache (dark green). Lines represent a mutation step, circles represent haplotypes and dots missing haplotypes. The size of the circles is proportional to the number of alleles.

Table 2

Percentage of private alleles in all the populations and for each nuclear locus

Private Alleles (%)

MCIR

RAGI

C-MOS

ACM4

PDC

J. Awlime

33

50

0

67

0

J. Sirwa

96

12

0

42

92

Toubkal

50

0

50

100

50

Oukaimeden

41

33

70

29

57

Tizin Tichka

75

59

23

71

100

J. Azourki

60

100

60

84

90

Outabati

100

54

43

9

83

J. Ayache

92

85

57

80

20

Clustering analysis and individual assignment

In our study, the obtained K differs with the combination between the ancestry model and the allele frequency model. When combined the No Admixture Model (ancestry model) with the Allele Frequencies Independent Model (allele frequency model) the best resulting K values where for K = 3: South (Oukaimeden, J. Sirwa, Toubkal and J. Awlime), center (Tizin Tichka and J. Azourki) and North (Outabati and J. Ayache) groups. With the other three combinations between the models the best result were for K = 6: J. Sirwa, Tizin Tichka, J. Azourki, Outabati, J. Ayache, and a group formed by Oukaimeden, Toubkal and J. Awlime (Figure 4).
https://static-content.springer.com/image/art%3A10.1186%2F1471-2148-12-167/MediaObjects/12862_2012_Article_2101_Fig4_HTML.jpg
Figure 4

Population structure estimation. Each individual is represented by a thin vertical line, which is partitioned into K colored segments that represent the individual’s estimated membership fractions in K clusters. The bigger vertical divisions separate individuals from different populations. Populations are labeled below the figure. The colors used are the same used in Figure 1 and Figure 2.

Species tree and divergence time estimates

The results of the clustering analysis with K = 6 were used to define the species for the species tree analysis in STARBEAST. The tree inferred with information from mitochondrial and nuclear markers (phased) (figure 2D) recovered the same topology as in Figure 2C, with all the relationships between the lineages supported by previous analyses.

The divergence time estimates were calculated for the six populations (Table 1). High effective sample sizes were observed for all parameters in all BEAST analysis (posterior ESS values > 1000 for all four analyses) and assessment of convergence statistics in Tracer indicated that all analyses had converged. Maximum clade credibility tree for ND4 + tRNA-His was identical in topology to those produced by Bayesian and ML analyses. According to the inferred dates resulted from BEAST (Figure 2D), the two main mitochondrial lineages of A. andreanskyi (South versus central and North) split approximately 7.6 Ma (95% high posterior density (HPD) interval 4.3-11.9 Ma). The populations that are grouped in the three main clades (South, central and North) split approximately at the same time, being Tizin Tichka and J. Azourki the first to split at about 4.3 Ma (1.4-7.8), followed by Oukaimeden and J. Sirwa 2.9 Ma (1–5.6), and Outabati and J. Ayache 2.4 Ma (0.8-4.4). Tizin Tichka and J. Azourki diverged from Outabati and J. Ayache approximately 6.4 Ma (3.1-10.2).

Discussion

Extreme mtDNA diversity in A. andreanskyi

Several recently published analyses of North African herpetofauna have revealed high levels of endemism and cryptic species [12, 14, 15, 17]. In this analysis, the surprising result was the extreme diversity of mitochondrial DNA found between almost all the populations analyzed The genetic differentiation observed between populations (2.8% - 6.6% in 12S and 5.5% - 16.5% in ND4 + tRNA-His) is similar and, in some cases, higher than the divergence found between Iberolacerta species (between 7.4% and 8.2% in the cytochrome b gene, [53]), a lacertid genus with most of its species occurring in the mountains of the Iberian Peninsula [41, 54]. Initially considered one species, there are now seven recognized species of Iberolacerta in the Iberian Peninsula. Genetic differentiation between these species is lower than between the different populations of A. andreanskyi.

Although the mitochondrial phylogeny supports the existence of seven distinct groups, the clustering analysis only supports the existence of six lineages (J. Sirwa, Tizin Tichka, J. Azourki, Outabati, J. Ayache and a lineage formed by Oukaimeden, Toubkal and J. Awlime). Toubkal samples were always part of the same lineage as Oukaimeden, although, they show some divergence at least at the mitochondrial DNA level (1.7% in ND4 + tRNA-His and 0.3% in 12S). This is not unexpected, as these populations are geographically very close and are part of the High Atlas Mountains, where interconnectivity between populations could occur. The mitochondrial phylogenetic analyses supported the existence of a seventh isolated lineage, J. Awlime, however clustering analysis and the nuclear phylogeny did not support the distinctiveness of this population, possibly because of the small sampling size. Unfortunately, despite multiple attempts to sample in this remote region, only three individuals were captured. The analyses also could not recover the genetic relationship between J. Awlime and the other populations, because its position in the trees fluctuated between the two main groups (North and South), without support in any of the trees.

Non-reciprocal monophyly in nuclear markers and species delimitation

In the phylogenetic analyses of the concatenated nuclear loci, some of the lineages supported by mtDNA data were not monophyletic. This was observed only between the geographically closest lineages, as in the case of Oukaimeden and J. Sirwa; Tizin Tichka and J. Azourki; and Outabati and J. Ayache, that presumably were in contact more recently than the others. This may be due to the larger effective population size of the nuclear DNA compared to the mitochondrial DNA and the consequent stronger effect of the incomplete lineage sorting at each single nuclear loci [55]. Additionally, the slow evolutionary rate of some of these markers may be a factor. The conjugation of these two effects probably explains the absence of concordance in the single nuclear gene trees (not show), although the same general topology was recovered in the concatenated nuclear phylogeny. Reciprocal monophyly is one of the primary criteria to delimit species [31, 56]. Although it is possible to delimit species without observing monophyly in gene trees, since a considerable amount of time must pass after the beginning of divergence of species until they show reciprocal monophyly at a sample of multiple loci [57, 58]. Pinho et al.[59] have shown that Podarcis from the Iberian Peninsula and North Africa have a similar pattern (between mtDNA and nuclear) but in a smaller time window and using faster evolving nuclear loci and, in contrast to our case, some populations are in contact.

Although we are aware that the determination of K, in STRUCTURE, is only an ad hoc guide to describe consistence between models and the data [60], the program has been commonly used for this end [61]. Several methods based on Bayesian clustering have been developed [6264], however, STRUCTURE is the most widely used, and various studies show its efficiency in assigning individuals to their population of origin [6568] and its ability to construct an appropriate clustering hypothesis [61]. However, in the present example the analysis was limited because it was based only in haplotype information. The obtained K differ with the combination model used, but in most of the combinations the analysis supports a K = 6 corresponding to the geographical populations and to the results recovered by the other analyses. This analysis also placed the samples from the J. Awlime population together with the Oukaimeden lineage, possibly due to the limited haplotype sampling. Similarly, the concatenated phylogenetic tree, based on all the genes, supports the existence of 7 lineages giving once more a low support to the relationship between J. Awlime and the other lineages.

The networks of the individual nuclear loci show high percentage of private alleles in some of the lineages, which fluctuate depending on the gene.

Dating the trees

All the lineages are grouped in two main clusters, the northern group composed by J. Ayache, Outabati, J. Azourki and Tizin Tichka; and the southern group that includes Oukaimeden and J. Sirwa. The divergence obtained for these two lineages was around 7.6 Mya, (4.3-11.9), which coincides approximately with the time of the final closing of the Rifian Strait (7.2 Mya; [3]). During the Miocene, tectonic activity in the region was intense and included the uplift of the Atlas Mountains that occurred around 9.0 Mya [69, 70]. It was more or less at the same time that Podarcis invaded North Africa (7.5 ± 1.2 Mya, [2]) and the Iberian clade of Iberolacerta started to fragment (6.1 Mya, [1]). The split of the six lineages must have occurred later, probably during the Quaternary Glaciations (4.3 ± 3; 2.4 ± 2; 2.9 ± 2 Mya). However, the confidence intervals obtained were very large, increasing the time window for the events and the associated error. Determination of the time of the speciation events is important to understand the evolutionary biogeography of species [71]. However, it is difficult to estimate ages in phylogenies without several sources of error. Clearly the lineages of A. andreanskyi are pre-Pleistocenic and, as found in Central African chameleons [8] can be considered paleoendemics. However, without better calibration points it is difficult to date the split of the lineages more precisely than this.

Conclusions

Phylogeographic assessments of several taxa in northwest Africa have indicated the presence of cryptic diversity in organisms ranging from scorpions [72] to mammals [73], and reptiles are not an exception [e.g. [11, 17, 74]. What is exceptional in the case of A. andreanskyi are the high levels of mitochondrial divergence between almost every sampled populations, ranging from 5.5 up to 16.5% (ND4 + tRNA-His) between populations separated by low geographic distances (for example just 60 Km between Oukaimeden and J. Sirwa and 45 Km between Oukaimeden and Tizin Tichka). Six of the eight analyzed populations are highly distinct based on both mtDNA and multiple nuclear markers. This raises the issue not of whether A. andreanskyi is a species complex, but just how many species may occur within the group. Presumably, far more than the six possible species identified in this study, since, probably, many populations remain unsampled. However, preliminary morphological analyses suggest that all the different populations included in the present study are very homogeneous (unpublished data). This may imply the presence of cryptic diversity, but definitive conclusions should wait until a complete morphological study is carried out (work in progress).

Current models of reptiles species accessed for the region indicate low levels of diversity across much of the High Atlas Mountains [75]. Indeed only a few species are recorded at altitudes above 2000 m; typically A. andreanskyi, Quedenfeldtia species (Q. trachyblepharus and Q. moerens), Chalcides montanus and Vipera monticola [e.g. [42, 76]. However, the finding of high genetic diversity in A. andreanskyi indicates that unidentified lineages occur, and that the other high mountain species should also be assessed as possible cryptic species candidates. Our results are also essential from a conservation point of view, as many forms may actually have smaller ranges than currently thought, and small isolated populations on high mountains have been identified as those of high concern under typical global warming scenarios [77]. Given these results it is necessary to increase the sampling in order to understand the relationship of J. Awlime with the other populations and try to find new populations. Furthermore it is very important to conduct a through morphological study to determine if there is phenotypic variation, and then to revise the taxonomy of the genus Atlantolacerta.

Methods

Species concept and integrative approach

Although the present study does not include a taxonomic revision of the genus Atlantolacerta, like many other works in which some of the authors of the present manuscript have participated [35, 78, 79], we advocate for the use of the General Lineage Species Concept proposed by de Queiroz [30]. Two lines of evidence have been defined on the basis of alleged independence of their respective datasets: mitochondrial DNA and nuclear DNA. In the present study, we have decided to retain as “putative species” only these lineages that were recovered as monophyletic in the phylogenetic analysis of the mtDNA data and that were supported by the analysis of the nDNA using STRUCTURE v.2.3.2 [60]. Within the framework of an integrative approach, and pending the inclusion of morphological data, this would correspond to Integration by total congruence (ITC). However, it is important to take into account that in the absence of a thorough morphological analysis we do not consider the molecular data presented here enough to revise the taxonomy of the genus Atlantolacerta.

DNA extraction, amplification, and sequencing

A total of 92 individuals from eight different populations distributed across the entire range of Atlantolacerta andreanskyi were sampled for this study: 14 from Oukaimeden, 15 from Tizin Tichka, 14 from Jebel Ayache, 15 from Jebel Azourki, 14 from Outabati, 15 from Jebel Sirwa and 2 from Toubkal and 3 from J. Awlime (Figure 1 and Table 3). Specimens were caught by hand, identified on the basis of external features, measured and photographed for later morphological studies. Tail tips where collected and stored in 96% ethanol, after which individuals were released in the same place where they were caught.
Table 3

Samples used in the work with localities (GPS coordinates; WGS84 coordinate system) and GenBank accession numbers for all the sequenced genes

      

GenBank Acession codes

Specimen code

Alleles

Population

Latitude

Longitude

Altitude

12S

ND4 + tRNA-His

PDC

ACM4

C-MOS

MC1R

RAG1

1152

1152a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462053

JX462189

JX461527

JX461879

JX485185

JX461693

JX461351

 

1152b

      

JX461528

JX461880

JX485186

JX461694

JX461352

1149

1149a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462062

JX462194

JX461523

JX461875

JX485189

JX461689

JX461349

 

1149b

      

JX461524

JX461876

JX485190

JX461690

JX461350

1148

1148a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462064

JX462195

JX461521

JX461873

JX485191

JX461687

JX461347

 

1148b

      

JX461522

JX461874

JX485192

JX461688

JX461348

1150

1150

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462061

JX462191

2556

2556a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462060

JX462192

JX461593

JX461947

JX485195

JX461947

JX461417

 

2556b

      

JX461594

JX461948

JX485196

JX461948

JX491418

2578

2578

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462059

JX462193

2626

2626a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462058

JX462190

JX461625

JX461979

JX485193

JX461793

JX461447

 

2626b

      

JX461626

JX461980

JX485194

JX461794

JX461448

5058

5058a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462054

JX462196

JX461643

JX461999

JX485205

JX461815

JX461469

 

5058b

      

JX461644

JX462000

JX485206

JX461816

JX461470

5010

5010a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462065

JX462203

JX461629

JX461983

JX485199

JX461799

JX461453

 

5010b

      

JX461630

JX461984

JX485200

JX461800

JX461454

5126

5126

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462066

JX462197

5086

5086a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462056

JX462198

JX461649

JX462009

JX485197

JX461825

JX461479

 

5086b

      

JX461650

JX462010

JX485198

JX461826

JX461480

5103

5103a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462055

JX462202

JX461655

JX462015

JX485207

JX461831

JX461483

 

5103b

      

JX461656

JX462016

JX485208

JX461832

JX461484

5104

5104a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462063

JX462199

JX461657

JX462017

JX485209

JX461833

JX461485

 

5104b

      

JX461658

JX462018

JX485210

JX461834

JX461486

5015

5015a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462057

JX462200

JX461633

JX461987

JX485203

JX461803

JX46147

 

5015b

      

JX461634

JX461988

JX485204

JX461804

JX46148

5130

5130a

Tizin Tichka (Tiz)

31.30077

−7.40984

2800

JX462067

JX462201

JX461667

JX462031

JX485201

JX461847

JX461497

 

5130b

      

JX461668

JX462032

JX485202

JX461848

JX461498

1040

1040a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462083

JX462153

JX461519

JX461871

JX485240

JX461685

JX461345

 

1040b

      

JX461520

JX461872

JX485241

JX461686

JX461346

1349

1349a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462084

JX462150

JX461557

JX461911

JX485141

JX461725

JX461383

 

1349b

      

JX461558

JX461912

JX485142

JX461726

JX461384

1394

1394a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462085

JX462147

JX461559

JX461913

JX485244

JX461726

JX461384

 

1394b

      

JX461560

JX461914

JX485245

JX461727

JX461385

1489

1489a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462086

JX462152

JX461561

JX461915

JX485246

JX461729

JX461387

 

1489b

      

JX461562

JX461916

JX485247

JX461730

JX461388

1498

1498a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462087

JX462151

JX461563

JX461917

JX485248

JX461732

JX461389

 

1498b

      

JX461564

JX461918

JX485249

JX461733

JX461390

1598

1598a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462158

JX462158

JX461573

JX461927

JX485256

JX461741

JX461399

 

1598b

      

JX461574

JX461928

JX485257

JX461742

JX461340

1633

1633a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462096

JX462148

JX461583

JX461937

JX485264

JX461751

JX461409

 

1633b

      

JX461584

JX461938

JX485265

JX461752

JX461410

1638

1638

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462097

JX462159

1638

1588a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX461567

JX461921

JX485250

JX461735

JX461393

 

1588b

      

JX461568

JX461922

JX485251

JX461736

JX461394

1626

1626a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462160

JX462160

JX461579

JX461933

JX485260

JX461747

JX461405

 

1626b

      

JX461580

JX461934

JX485261

JX461748

JX461406

1616

1616a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462154

JX462154

JX461577

JX461931

JX485258

JX461745

JX461403

 

1616b

      

JX461578

JX461932

JX485259

JX461746

JX461404

1609

1609

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462155

JX462155

1589

1589a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462090

JX462156

JX461569

JX461923

JX485252

JX461737

JX461395

 

1589b

      

JX461570

JX461924

JX485253

JX461738

JX461396

1630

1630a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462088

JX462149

JX461581

JX461935

JX485262

JX461749

JX461407

 

1630b

      

JX461582

JX461936

JX485263

JX461750

JX461408

1591

1591a

Jebel Sirwa (JSi)

30.77671

−7.65299

2561

JX462157

JX462157

JX461571

JX461925

JX485254

JX461739

JX461397

 

1591b

      

JX461572

JX461926

JX485255

JX461740

JX461398

1158

1158a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462069

JX462162

JX461531

JX461883

JX485211

JX461697

JX461355

 

1158b

      

JX461532

JX461884

JX485212

JX461698

JX461356

1154

1154a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462068

JX462161

JX461529

JX461881

JX485214

JX461695

JX461353

 

1154b

      

JX461530

JX461882

JX485215

JX461696

JX461354

2534

2534a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462070

JX462163

JX461587

JX461941

JX485220

JX461755

JX461411

 

2534b

      

JX461588

JX461942

JX485221

JX461756

JX461412

2553

2553a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462071

JX462164

JX461591

JX461945

JX485218

JX461759

JX461415

 

2553b

      

JX461592

JX461946

JX485219

JX461760

JX461416

2619

2619a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX461621

JX461975

JX485238

JX461789

JX461443

 

2619b

      

JX461622

JX461976

JX485239

JX461790

JX461444

2620

2620a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX461623

JX461977

JX485236

JX461791

JX461445

 

2620b

      

JX461624

JX461978

JX485237

JX461792

JX461446

2577

2577a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462074

JX462167

JX461603

JX461957

JX485216

JX461771

JX461427

 

2577b

      

JX461604

JX461958

JX485217

JX461772

JX461428

2567

2567a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462072

JX462165

JX461599

JX461953

JX485222

JX461767

JX461423

 

2567b

      

JX461600

JX461954

JX485223

JX461768

JX461424

2569

2569a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462073

JX462166

JX461601

JX461955

JX485234

JX461769

JX461425

 

2569b

      

JX461602

JX461956

JX485235

JX461770

JX461426

2602

2602a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462075

JX462168

JX461607

JX461961

JX485232

JX461775

JX461429

 

2602b

      

JX461608

JX461962

JX485233

JX461776

JX461430

2604

2604a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462076

JX462169

JX461609

JX461963

JX485230

JX461777

JX461430

 

2604b

      

JX461610

JX461964

JX485231

JX461778

JX461431

2612

2612a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462077

JX462170

JX461613

JX461967

JX485224

JX461781

JX461435

 

2612b

      

JX461614

JX461968

JX485225

JX461782

JX461436

2615

2615a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462078

JX462171

JX461615

JX461969

JX485228

JX461783

JX461437

 

2615b

      

JX461616

JX461970

JX485229

JX461784

JX461438

2616

2615a

Oukaimeden (Ouk)

31.20426

−7.86705

2600

JX462079

JX462172

JX461617

JX461971

JX485226

JX461785

JX461439

 

2615b

      

JX461618

JX461972

JX485227

JX461786

JX461440

1579

1579a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462098

JX462178

JX461565

JX461919

JX485276

JX461733

JX461391

 

1579b

      

JX461566

JX461920

JX485277

JX461734

JX461392

2552

2552a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462099

JX462177

JX461589

JX461943

JX485278

JX461757

JX461413

 

2552b

      

JX461590

JX461944

JX485279

JX461758

JX461414

2564

2694a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462101

JX462176

JX461597

JX461951

JX485272

JX461765

JX461421

 

2694b

      

JX461598

JX461952

JX485273

JX461766

JX461422

2608

2608a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462102

JX462175

JX461611

JX461965

JX485270

JX461779

JX461433

 

2608b

      

JX461612

JX461966

JX485271

JX461780

JX461434

2618

2618a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462103

JX462174

JX461619

JX461973

JX485268

JX461787

JX461441

 

2618b

      

JX461620

JX461974

JX485269

JX461788

JX461442

9189

9189a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX461675

JX462043

JX485282

JX461859

JX461509

 

9189b

      

JX461676

JX462044

JX485283

JX461860

JX461510

9199

9199

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462108

JX462183

9255

9255a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462110

JX462179

JX461681

JX462051

JX485290

JX461867

JX461515

 

9255b

      

JX461682

JX462052

JX485291

JX461868

JX461516

9209

9209

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462109

JX462184

9191

9191a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462106

JX462181

JX461677

JX462045

JX485292

JX461861

JX461511

 

9191b

      

JX461678

JX462046

JX485293

JX461862

JX461512

9336

9336

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462111

JX462182

9193

9193a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462107

JX462188

JX461679

JX462047

JX485288

JX461863

JX461513

 

9193b

      

JX461680

JX462048

JX485289

JX461864

JX461514

2557

2557a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX461595

JX461949

JX485274

JX461763

JX461419

 

2557b

      

JX461596

JX461950

JX485275

JX461764

JX461420

9145

9145a

Jebel Ayache (Jay)

32.53671

−4.79110

3043

JX462104

JX462185

JX461673

JX462041

JX485286

JX461857

JX461507

 

9145b

      

JX461674

JX462042

JX485287

JX461858

JX461508

5076

5076a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462120

JX462206

JX461647

JX462005

JX485296

JX461821

JX461475

 

5076b

      

JX461648

JX462006

JX485297

JX461822

JX461476

5128

5128a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462126

JX462207

JX461665

JX462029

JX485298

JX461845

JX461495

 

5128b

      

JX461667

JX462030

JX485299

JX461846

JX461496

5091

5091

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462122

JX462208

5017

5017a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462113

JX462209

JX461635

JX461989

JX485308

JX461805

JX461459

 

5071b

      

JX461636

JX461990

JX485309

JX461806

JX461460

5122

5122a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462125

JX462210

JX461661

JX462023

JX485300

JX461839

JX461491

 

5122b

      

JX461662

JX462024

JX485301

JX461840

JX461492

5105

5105a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462123

JX462211

JX461659

JX462019

JX485302

JX461835

JX461487

 

5105b

      

JX461660

JX462020

JX485303

JX461836

JX461488

5072

5072a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462118

JX462221

JX461645

JX462001

JX485304

JX461817

JX461471

 

5072b

      

JX461646

JX462002

JX485305

JX461818

JX461472

5037

5037a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462116

JX462213

JX461639

JX461995

JX485322

JX461811

JX461465

 

5037b

      

JX461640

JX461996

JX485323

JX461812

JX461466

5011

5011a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462112

JX462204

JX461631

JX461985

JX485312

JX461801

JX461455

 

5011b

      

JX461632

JX461986

JX485313

JX461802

JX461456

5034

5034

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462115

JX462205

5080

5080

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462121

JX462216

5025

5025a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462114

JX462214

JX461637

JX461991

JX485314

JX461807

JX461461

 

5025b

      

JX461638

JX461992

JX485315

JX461808

JX461462

5043

5043a

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462117

JX462218

JX461641

JX461997

JX485324

JX461813

JX461467

 

5034b

      

JX461641

JX461997

JX485324

JX461813

JX461467

5073

5073

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462119

JX462215

5111

5111

Jebel Azourki (Jaz)

31.75847

−6.28826

2789

JX462124

JX462217

6016

6816a

Outabati (Out)

32.17714

−5.33214

2441

JX462128

JX462221

JX461671

JX462037

JX485330

JX461853

JX461503

 

6816b

      

JX461672

JX462038

JX485331

JX461854

JX461504

11754

11754a

Outabati (Out)

32.17714

−5.33214

2441

JX462140

JX462230

JX461551

JX461903

JX485350

JX461717

JX461375

 

11754b

      

JX461552

JX461904

JX485351

JX461718

JX461376

11746

11746a

Outabati (Out)

32.17714

−5.33214

2441

JX462137

JX462228

JX461547

JX461899

JX485346

JX461713

JX461371

 

11746b

      

JX461548

JX461900

JX485347

JX461713

JX461371

11743

11743a

Outabati (Out)

32.17714

−5.33214

2441

JX462135

JX462226

JX461543

JX461895

JX485342

JX461709

JX461367

 

11743b

      

JX461544

JX461896

JX485343

JX461710

JX461368

11717

11717a

Outabati (Out)

32.17714

−5.33214

2441

JX462130

JX462222

JX461533

JX461885

JX485332

JX461699

JX461357

 

11717b

      

JX461534

JX461886

JX485333

JX461700

JX461358

11755

11755a

Outabati (Out)

32.17714

−5.33214

2441

JX462139

JX462231

JX461553

JX461905

JX485352

JX461719

JX461377

 

11755b

      

JX461554

JX461906

JX485353

JX461720

JX461378

11727

11727a

Outabati (Out)

32.17714

−5.33214

2441

JX462131

JX462232

JX461535

JX461887

JX485334

JX461701

JX461359

 

11727b

      

JX461536

JX461888

JX485335

JX461702

JX461360

11752

11752a

Outabati (Out)

32.17714

−5.33214

2441

JX462138

JX462229

JX461549

JX461901

JX485348

JX461715

JX461373

 

11752b

      

JX461550

JX461902

JX485349

JX461716

JX461374

6643

6643

Outabati (Out)

32.17714

−5.33214

2441

JX462129

JX462220

11741

11741a

Outabati (Out)

32.17714

−5.33214

2441

JX462134

JX462225

JX461541

JX461893

JX485340

JX461707

JX461365

 

11741b

      

JX461542

JX461894

JX485341

JX461708

JX461366

11734

11734a

Outabati (Out)

32.17714

−5.33214

2441

JX462133

JX462224

JX461539

JX461891

JX485338

JX461705

JX461363

 

11734b

      

JX461540

JX461892

JX485339

JX461706

JX461364

11745

11745a

Outabati (Out)

32.17714

−5.33214

2441

JX462136

JX462227

JX461545

JX461897

JX485344

HX461711

JX461369

 

11745b

      

JX461546

JX461898

JX485345

HX461712

JX461370

11733

11733a

Outabati (Out)

32.17714

−5.33214

2441

JX462132

JX462223

JX461537

JX461889

JX485336

JX461703

JX461361

 

11733b

      

JX461538

JX461890

JX485337

JX461704

JX461361

6639

6639

Outabati (Out)

32.17714

−5.33214

2441

JX462127

JX462219

3865

3865a

Toubkal (Tou)

31.09415

−7.91367

2600

JX462142

JX462236

JX461627

JX461981

JX485360

JX461797

JX4614513

 

3865b

      

JX461628

JX461982

JX485361

JX461798

JX4614514

13276

13276a

Toubkal (Tou)

31.09415

−7.91367

2600

JX462143

JX462237

JX461909

JX485362

JX461723

JX461381

 

13276b

      

JX461910

JX485363

JX461724

JX461382

5090

5090a

Jebel Awlime (JAw)

30.81708

−8.86298

2967

JX46244

JX462234

JX461651

JX462011

JX485354

JX461827

JX461481

 

5090b

      

JX461652

JX462012

JX485355

JX4618288

JX461482

13179

13179a

Jebel Awlime (JAw)

30.81708

−8.86298

2967

JX462146

JX462235

JX461555

JX461907

JX485358

JX461721

JX461379

 

13179b

      

JX461556

JX461908

JX485359

JX461722

JX461380

5123

5123a

Jebel Awlime (JAw)

30.81708

−8.86298

2967

JX462145

JX462233

JX461663

JX462025

JX485356

JX461841

JX461493

 

5123b

      

JX461664

JX462026

JX485357

JX461842

JX461494

Genomic DNA was extracted from ethanol-preserved tissue samples using standard high-salt protocols [80]. A total of 89 specimens of Atlantolacerta andreanskyi plus three outgroups (Podarcis hispanica, Podarcis carbonelli and Podarcis bocagei) were sequenced for two mitochondrial regions: partial 12S rRNA (12S) and partial NADH dehydrogenase 4 (ND4) and flanking tRNA (tRNA-His) and 77 specimens for five nuclear gene fragments, recombination-activating gene 1 (RAG1), acetylcholinergic receptor M4 (ACM4), melanocortin receptor 1 (MC1R), oocyte maturation factor Mos (C-MOS) and phosducin (PDC). Primers used for both amplification and sequencing were: 12Sa and 12Sb [81] for the 12S following the PCR conditions described in Harris and Arnold [82], ND4 and Leu for ND4 + tRNA-His, PCR conditions described in Arévalo et al.[83]; L2408 and H2920 for RAG1 following the PCR conditions from Vidal and Hedges [84]; tg-F and tg-R [85] for ACM4 with PCR conditions following Gamble et al.[86]; MC1RF and MC1RR for MC1R following PCR conditions described in Pinho et al.[87]; Lsc1 and Lsc2 for C-MOS following the PCR conditions from Godinho et al.[88]; and PHOF2 and PHOF1 for PDC, following PCR conditions described in Bauer et al.[89]. PCRs were carried out in 25 μl volumes, containing 5.0 μl of 10 reaction Buffer, 2.0 mM of MgCl2, 0.5 mM each dNTP, 0.2 μM each primer, 1 U of Taq DNA polymerase (Invitrogen), and approximately 100 ng of template DNA. Finally, PCR products were purified using exosap IT and the resulting amplified fragments were sequenced on an Applied Biosystem DNA Sequencing Apparatus. Chromatographs were checked manually, assembled and edited using Bioedit 7.0.1 [90]. Sequences were aligned for each gene independently using the online version of MAFFT v.6 [91] with default parameters (gap opening penalty = 1.53, gap extension = 0.0) and FFT-NS-1 algorithm. Coding gene fragments (ND4, C-MOS, ACM4, RAG1, PDC and MC1R) were translated into amino acids and no stop codons were observed, suggesting that the sequences were all functional. Heterozygous individuals were identified based on the presence of two peaks of approximately equal height at a single nucleotide site. SEQPHASE [92] was used to convert the input files, and the software PHASE v2.1.1 to resolve phased haplotypes [93]. Default settings of PHASE were used except for phase probabilities that were set as ≥ 0.7 [94]. All polymorphic sites with a probability of < 0.7 were coded in both alleles with the appropriate IUPAC ambiguity code. Phased nuclear sequences were used for the structure analysis; networks and species tree analysis, and the unphased sequences for the phylogenetic analyses (see below). DnaSP [95] was used to calculate the number of haplotypes (h) and mutations (η). Mega v.3.0 [96] was used to estimate uncorrected p-distances and to obtain the number of variable and parsimony informative sites.

Phylogenetic analyses

Phylogenetic analyses were performed using maximum likelihood (ML) and Bayesian (BI) methods. JModelTest [97] was used to select the most appropriate model of sequence evolution under the Akaike Information Criterion [98]. ML analyses were performed with RAxML v.7.0.4 [99] with 100 random addition replicates. A GTR + I + G model was used and parameters were estimated independently for each partition (by gene). Reliability of the ML tree was assessed by bootstrap analysis [100] including 1000 replications. Bayesian analyses were performed with MrBayes v.3.1.2 [101] with best fitting models applied to each partition by gene and all parameters unlinked across partitions. The models selected for the different partitions were: 12S, GTR + I + G; ND4, GTR + G; tRNA-His, GTR + I + G; ACM4, HKY + I; C-MOS, GTR + I + G; MC1R, HKY + I + G; PDC, GTR + I + G; and RAG1, GTR + I. Two independent runs of 5x106 generations were carried out, sampling at intervals of 1000 generations producing 5000 trees. Convergence and appropriate sampling were confirmed examining the standard deviation of the split frequencies between the two simultaneous runs and the Potential Scale Reduction Factor (PSRF) diagnostic. Burn-in was performed discarding the first 1250 trees of each run (25%) and a majority-rule consensus tree was generated from the remaining trees. In both ML and BI alignment gaps were treated as missing data and the nuclear gene sequences were not phased.

Nuclear Networks

The genealogical relationships between the populations were assessed with haplotype networks for all the individual nuclear genes, constructed using statistical parsimony [102] implemented in the program TCS v 1.21 [103] with a connection limit of 95%. This analysis was made with the phased sequences. Haplotypes were colored taking into account the population of origin.

Population structure – Clustering analyses

A model-based Bayesian clustering method was applied to all haplotypes using STRUCTURE v.2.3.2 [60, 104, 105]. In this analysis, individuals are probabilistically assigned to either a single cluster (the population of origin), or more than one cluster (if there is admixture). STRUCTURE was run with haplotype information from the nuclear fragments independently. We ran our data with the all parameters combinations between the Ancestry Model and the Allele Frequency Model to compare the results. The genetic structure was forced to vary from K = 2 to K = 10 clusters, the latter corresponding to the number of geographic populations sampled plus two. STRUCTURE ran for 550 000 steps, of which the first 50 000 were discarded as burn-in. For each value of K ten independent replicates of the Markov Chain Monte Carlo (MCMC) were conducted. To detect the true number of clusters (K) we followed the graphical methods and algorithms outlined in Evanno et al.[61], with the comparison of the average posterior probability values for K (log likelihood; ln L) using the online version, STRUCTURE HARVESTER v0.6.5 (available at: http://taylor0.biology.ucla.edu/struct_ harvest/, April 2011).

Species tree, and divergence time estimates

Here we applied the coalescent-based species-tree approach implemented in STARBEAST [106] an extension of BEAST v1.6.1 [107] to test the origin and diversification patterns in Atlantolacerta, and to compare these results to those obtained from the ML and BI analyses of the concatenated dataset. This analysis needs a priori information regarding the species/populations delimitation and the species/populations assignation of the individuals in order to reconstruct the topology of the species tree. For this approach, we used the results obtained from previous clustering analyses to define the groups of individuals to be used as “species” (populations) in STARBEAST [106]. The clustering analysis supported the existence of six lineages, as Oukaimeden, Toubkal and J. Awlime were included in the same lineage.

All five nuclear gene fragments, 12S and the fragment consistent of the ND4 and flanking tRNA-His were included in the analyses as 7 independent partitions. The phased dataset was used for the nuclear loci.

The input file was formatted with the BEAUti utility included in the software package. We performed two independent runs of 1.5 x 108 generations, sampling every 15 000 generations, from which 10% were discarded as burn-in. Models and prior specifications applied were as follows (otherwise by default): 12S - GTR + G; ND4 and tRNA-His - HKY + G; MC1R - HKY + I; ACM4 - HKY + I; C-MOS - GTR + I + G; RAG1 - HKY + I; PDC - GTR + I; Relaxed Uncorrelated Lognormal Clock (estimate); Yule process of speciation; random starting tree; alpha Uniform (0, 10).

For all analyses implemented in BEAST, convergence for all model parameters was assessed by examining trace plots and histograms in Tracer v1.5 [108] after obtaining an effective sample size (ESS) > 200. The initial 10% of samples were discarded as burn-in. Runs were combined using LogCombiner, and maximum credibility trees with divergence time means and 95% highest probability densities (HPDs) were produced using Tree Annotator (both part of the BEAST package). Trees were visualized using the software FigTree v1.3.1 [109].

Several studies have already calculated divergence rates for reptiles, and particularly for lacertids [2, 15, 49]. Pinho et al. [15] used well-known and dated independent geological events in the Aegean [110] to estimate a maximum and minimum mutation rate for the ND4 mitochondrial fragment (and flanking tRNA-His) for the lacertid lizards of the genus Podarcis (0.0278 and 0.0174 mutation/site/million years, respectively). However, this was the only information available for our data, since we did not have any fossils or calibrations for nuclear markers. It is important to bear in mind that, in the absence of accurate calibration points in the phylogeny from external and independent data (fossil records, known biogeographic events, or paleoclimatic reconstructions) or as a result of the heterogeneity in the evolutionary rate between the calibrated and uncalibrated taxa, temporal estimates by means of molecular data could be a potential source of inference error, and, therefore, they should be treated with caution [111]. Despite the limitations of molecular clocks [111, 112], divergence time estimates can still provide a proxy for the temporal window of evolutionary diversification in species groups of interest. Therefore and taking into account our data limitations and availability, we used BEAST v.1.6.1 [107] to estimate dates of the cladogenetic events using only ND4 and flanking tRNA-His. We used a phylogeny pruned arbitrarily to include one representative from each of the major lineages uncovered with the concatenated analysis (6 specimens in total, we excluded J. Awlime population, because of the lack of support of the branch in previous analyses). This method excludes closely related terminal taxa because the Yule tree prior (see below) does not include a model of coalescence, which can complicate rate estimation for closely related sequences [113]. Analyses were run four times for 5x107 generations with a sampling frequency of 10 000. Models and prior specifications applied were as follows (otherwise by default): GTR + G for 12S; HKY + G for ND4 and tRNA-His; HKY + I for MC1R; HKY + I for ACM4; GTR + G + I for C-MOS; HKY + I for RAG1; GTR + I for PDC; Relaxed Uncorrelated Lognormal Clock (estimate); Yule process of speciation; random starting tree; alpha Uniform (0, 10); ucld.mean of ND4 Normal (initial value: 0.0226, mean: 0.0226, Stdev: 0.0031).

Declarations

Acknowledgements

MB is supported by the FCT grant SFRH/BD/41488/2007. This work was funded by FCT grant PTDC/BIA-BDE/74349/2006 and by grant CGL2009-11663 from the Ministerio de Educación y Ciencia, Spain to SC. Fieldwork in Morocco in 2008 and 2009 was conducted under permit decision 84° issued by Haut Commissariat aux Eaux et Forêts et à la Lutte Contre la Désertification, issued to David Donaire plus other permits issued to the latter along a 10 year period.

Thanks to all colleagues from CIBIO who assisted during the hard fieldwork, especially to Anna Perera, Daniele Salvi, Fatima Jorge and Fernando Martinez-Freiria. We also want to thank to the anonymous reviewers that helped to improve this manuscript.

Authors’ Affiliations

(1)
CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos. Campus Agrário de Vairão
(2)
Departamento de Biologia, Faculdade de Ciências da Universidade do Porto
(3)
Institute of Evolutionary Biology (CSIC-UPF)

References

  1. Arribas O, Carranza S: Morphological and genetic evidence of the full species status of Iberolacerta cyreni martinezricai (Arribas, 1996). Zootaxa. 2004, 634: 1-24.Google Scholar
  2. Carretero MA: An integrated assessment of a group with complex systematics: the Iberomaghrebian lizard genus Podarcis (Squamata, Lacertidae). Integr Zool. 2008, 3 (4): 247-266.PubMedGoogle Scholar
  3. Duggen S, Hoernle K, van den Bogaard P, Rupke L, Morgan JP: Deep roots of the Messinian salinity crisis. Nature. 2003, 422 (6932): 602-606.PubMedGoogle Scholar
  4. Krijgsman W, Hilgen FJ, Raffi I, Sierro FJ, Wilson DS: Chronology, causes and progression of the Messinian salinity crisis. Nature. 1999, 400 (6745): 652-655.Google Scholar
  5. Pinho C, Ferrand N, Harris DJ: Reexamination of the Iberian and North African Podarcis (Squamata: Lacertidae) phylogeny based on increased mitochondrial DNA sequencing. Molecular Phylogenetics and Evolution. 2006, 38 (1): 266-273.PubMedGoogle Scholar
  6. Santos X, Roca J, Pleguezuelos JM, Donaire D, Carranza S: Biogeography and evolution of the Smooth snake Coronella austriaca (Serpentes: Colubridae) in the Iberian Peninsula: evidence for Messinian refuges and Pleistocenic range expansions. Amphibia-Reptilia. 2008, 29 (1): 35-47.Google Scholar
  7. Schmitt T: Molecular biogeography of Europe: Pleistocene cycles and postglacial trends. Frontiers in Zoology. 2007, 4 (1): 11-PubMedPubMed CentralGoogle Scholar
  8. Tolley KA, Chase BM, Forest F: Speciation and radiations track climate transitions since the Miocene Climatic Optimum: a case study of southern African chameleons. J Biogeogr. 2008, 35 (8): 1402-1414.Google Scholar
  9. Camargo A, Sinervo B, Sites JW: Lizards as model organisms for linking phylogeographic and speciation studies. Mol Ecol. 2010, 19 (16): 3250-3270.PubMedGoogle Scholar
  10. Carranza S, Arnold EN, Geniez P, Roca J, Mateo JA: Radiation, multiple dispersal and parallelism in the skinks, Chalcides and Sphenops (Squamata: Scincidae), with comments on Scincus and Scincopus and the age of the Sahara Desert. Molecular Phylogenetics and Evolution. 2008, 46 (3): 1071-1094.PubMedGoogle Scholar
  11. Fonseca MM, Brito JC, Paulo OS, Carretero MA, Harris DJ: Systematic and phylogeographical assessment of the Acanthodactylus erythrurus group (Reptilia: Lacertidae) based on phylogenetic analyses of mitochondrial and nuclear DNA. Molecular Phylogenetics and Evolution. 2009, 51 (2): 131-142.PubMedGoogle Scholar
  12. Fonseca MM, Brito JC, Rebelo H, Kalboussi M, Larbes S, Carretero MA, Harris DJ: Genetic variation among spiny-footed lizards in the Acanthodactylus pardalis group from North Africa. African Zoology. 2008, 43 (1): 8-15.Google Scholar
  13. Harris DJ, Batista V, Carretero MA: Assessment of genetic diversity within Acanthodactylus erythrurus (Reptilia: Lacertidae) in Morocco and the Iberian Peninsula using mitochondrial DNA sequence data. Amphibia-Reptilia. 2004, 25 (2): 227-232.Google Scholar
  14. Kaliontzopoulou A, Pinho C, Harris DJ, Carretero MA: When cryptic diversity blurs the picture: a cautionary tale from Iberian and North African Podarcis wall lizards. Biol J Linn Soc. 2011, 103 (4): 779-800.Google Scholar
  15. Pinho C, Harris DJ, Ferrand N: Contrasting patterns of population subdivision and historical demography in three western Mediterranean lizard species inferred from mitochondrial DNA variation. Mol Ecol. 2007, 16 (6): 1191-1205.PubMedGoogle Scholar
  16. Rato C, Harris DJ: Genetic variation within Saurodactylus and its phylogenetic relationships within the Gekkonoidea estimated from mitochondrial and nuclear DNA sequences. Amphibia-Reptilia. 2008, 29 (1): 25-34.Google Scholar
  17. Perera A, Harris DJ: Genetic variability within the Oudri’s fan-footed gecko Ptyodactylus oudrii in North Africa assessed using mitochondrial and nuclear DNA sequences. Molecular Phylogenetics and Evolution. 2010, 54: 634-639.PubMedGoogle Scholar
  18. Carranza S, Romano A, Arnold EN, Sotgiu G: Biogeography and evolution of European cave salamanders, Hydromantes (Urodela: Plethodontidae), inferred from mtDNA sequences. J Biogeogr. 2008, 35: 724-738.Google Scholar
  19. Carranza S, Arnold EN: History of West Mediterranean newts, Pleurodeles (Amphibia: Salamandridae), inferred from old and recent DNA sequences. Syst Biodivers. 2004, 1 (3): 327-337.Google Scholar
  20. Rato C, Carranza S, Harris DJ: When selection deceives phylogeographic interpretation: The case of the Mediterranean house gecko, Hemidactylus turcicus (Linnaeus, 1758). Molecular Phylogenetics and Evolution. 2011, 58: 365-373.PubMedGoogle Scholar
  21. Rato C, Carranza S, Perera A, Carretero MA, Harris DJ: Conflicting patterns of nucleotide diversity between mtDNA and nDNA in the Moorish gecko, Tarentola mauritanica. Molecular Phylogenetics and Evolution. 2010, 56 (3): 962-971.PubMedGoogle Scholar
  22. Degnan JH, Rosenberg NA: Gene tree discordance, phylogenetic inference and the multispecies coalescent. Trends in ecology & evolution. 2009, 24 (6): 332-340.Google Scholar
  23. Edwards SV: Is a New and General Theory of Molecular Systematics Emerging?. Evolution. 2009, 63 (1): 1-19.PubMedGoogle Scholar
  24. Maddison WP: Gene trees in species trees. Syst Biol. 1997, 46 (3): 523-536.Google Scholar
  25. Agapow PM, Bininda-Emonds ORP, Crandall KA, Gittleman JL, Mace GM, Marshall JC, Purvis A: The impact of species concept on biodiversity studies. Q Rev Biol. 2004, 79 (2): 161-179.PubMedGoogle Scholar
  26. Mayden RL: A hierarchy of species concepts: the denouement in the saga of the species problem. Species: The units of diversity. Edited by: Claridge MF, Dawah HA, Wilson MR. 1997, London: Chapman and Hall, 381-423.Google Scholar
  27. Sites JW, Marshall JC: Delimiting species: a Renaissance issue in systematic biology. Trends in Ecology & Evolution. 2003, 18 (9): 462-470.Google Scholar
  28. Agapow M: Species: demarcation and diversity. Phylogeny and Conservation. Edited by: Purvis A, Gittleman JL, Brooks T. 2005, Cambridge, UK: Cambridge University, 57-75.Google Scholar
  29. Sattler T, Bontadina F, Hirzel AH, Arlettaz R: Ecological niche modelling of two cryptic bat species calls for a reassessment of their conservation status. J Appl Ecol. 2007, 44 (6): 1188-1199.Google Scholar
  30. de Queiroz K, Donoghue MJ: Phylogenetic Systematics and the Species Problem. Cladistics. 1988, 4 (4): 317-338.Google Scholar
  31. de Queiroz K: Species Concepts and Species Delimitation. Syst Biol. 2007, 56 (6): 879-886.PubMedGoogle Scholar
  32. Dayrat B: Towards integrative taxonomy. Biol J Linn Soc. 2005, 85: 407-415.Google Scholar
  33. Padial JM, Miralles A, De la Riva I, Vences M: The integrative future of taxonomy. Front Zool. 2010, 7: 16-PubMedPubMed CentralGoogle Scholar
  34. Schlick-Steiner BC, Steiner FM, Seifert B, Stauffer C, Christian E, Crozier RH: Integrative taxonomy: a multisource approach to exploring biodiversity. Annu Rev Entomol. 2010, 55: 421-438.PubMedGoogle Scholar
  35. Miralles A, Vasconcelos R, Perera A, Harris DJ, Carranza S: An integrative taxonomic revision of the Cape Verdean skinks (Squamata, Scincidae). Zoologica Scripta. 2010, 40: 16-44.Google Scholar
  36. Vasconcelos R, Carranza S, Harris DJ: Insight into an island radiation: the Tarentola geckos of the Cape Verde archipelago. J Biogeogr. 2010, 37 (6): 1047-1060.Google Scholar
  37. Galbreath KE, Hafner DJ, Zamudio KR: When cold is better: Climate-driven elevation shifts yield complex patterns of diversification and demography in an Alpine Specialist (American Pika, Ochotona Princeps). Evolution. 2009, 63 (11): 2848-2863.PubMedGoogle Scholar
  38. Hewitt GM: Genetic consequences of climatic oscilations in the quaternary. Philos Trans R Soc Lond. 2004, 359: 183-195.Google Scholar
  39. Knowles LL: Did the Pleistocene glaciations promote divergence? Tests of explicit refugial models in montane grasshopprers. Mol Ecol. 2001, 10 (3): 691-701.PubMedGoogle Scholar
  40. Hughes L: Climate change and Australia: Trends, projections and impacts. Austral Ecol. 2003, 28 (4): 423-443.Google Scholar
  41. Mouret V, Guillaumet A, Cheylan M, Pottier G, Ferchaud AL, Crochet PA: The legacy of ice ages in mountain species: post-glacial colonization of mountain tops rather than current range fragmentation determines mitochondrial genetic diversity in an endemic Pyrenean rock lizard. J Biogeogr. 2011, 38 (9): 1717-1731.Google Scholar
  42. Bons J, Geniez P: Amphibiens et reptiles du Maroc (Sahara Occidental compris) Atlas Biogéographique. 1996, Barcelone: Asociación Herpetologica EspanolaGoogle Scholar
  43. Schleich HH, Kastle W, Kabisch K: Amphibians and Reptiles from North Africa. 1996, Königstein, Germany: Koeltz Scientific PublicationsGoogle Scholar
  44. Arnold EN: Relationships of the Palaearctic lizards assigned to the genera Lacerta, Algyroides and Psammodromus (Reptila, Lacertidae). 1973, London: British Museum (Natural History)Google Scholar
  45. Arnold EN: Towards a phylogeny and biogeography of the Lacertidae: relationships within an Old-World family of lizards derived from morphology. 1989, London: British Museum (Natural History)Google Scholar
  46. Harris DJ: Molecular systematics and evolution of lacertid lizards. Natura Croatica. 1999, 83 (3): 161-180.Google Scholar
  47. Mayer W, Bischoff W: Beiträge zur taxonomischen Revision der Gattung Lacerta (Reptilia: Lacertidae) Teil 1: Zootoca, Omanosaura, TimonundTeira als eigenstandige Gattungen. Salamandra. 1996, 32 (3): 163-170.Google Scholar
  48. Oliverio M, Bologna MA, Mariottin P: Molecular biogeography of the Mediterranean lizards Podarcis Wagler, 1830 and Teira Gray, 1838 (Reptilia, Lacertidae). J Biogeogr. 2000, 27: 1403-1420.Google Scholar
  49. Arnold EN, Arribas O, Carranza S: Systematics of the Palaearctic and Oriental lizard tribe Lacertini (Squamata: Lacertidae: Lacertinae), with descriptions of eight new genera. Zootaxa. 2007, 1430: 1-86.Google Scholar
  50. Pavlicev M, Mayer W: Fast radiation of the subfamily Lacertinae (Reptilia: Lacertidae): History or methodical artefact?. Molecular Phylogenetics and Evolution. 2009, 52 (3): 727-734.PubMedGoogle Scholar
  51. Busack SD: Notes on the biology of Lacerta andreanszkyi (Reptilia: Lacertidae). Amphibia-Reptilia. 1987, 8: 231-236.Google Scholar
  52. Carretero MA, Perera A, Harris DJ, Batista V, Pinho C: Spring diet and trophic partitioning in an alpine lizard community from Morocco. African Zoology. 2006, 41 (1): 113-122.Google Scholar
  53. Crochet PA, Chaline O, Surget-Groba Y, Debain C, Cheylan M: Speciation in mountains: phylogeography and phylogeny of the rock lizards genus Iberolacerta (Reptilia: Lacertidae). Molecular Phylogenetics and Evolution. 2004, 30 (3): 860-866.PubMedGoogle Scholar
  54. Carranza S, Arnold EN, Amat F: DNA phylogeny of Lacerta (Iberolacerta) and other lacertine lizards (Reptilia: Lacertidae): did competition cause long-term mountain restriction?. Syst Biodivers. 2004, 2 (01): 57-77.Google Scholar
  55. Funk DJ, Omland KE: Species-level paraphyly and polyphyly: Frequency, causes, and consequences, with insights from animal mitochondrial DNA. Annu Rev Ecol Evol S. 2003, 34: 397-423.Google Scholar
  56. Knowles LL, Carstens BC: Delimiting species without monophyletic gene trees. Syst Biol. 2007, 56 (6): 887-895.PubMedGoogle Scholar
  57. Hudson RR, Coyne JA: Mathematical consequences of the genealogical species concept. Evolution. 2002, 56 (8): 1557-1565.PubMedGoogle Scholar
  58. Hudson RR, Turelli M: Stochasticity overrules the "three-times rule": Genetic drift, genetic draft, and coalescence times for nuclear loci versus mitochondrial DNA. Evolution. 2003, 57 (1): 182-190.PubMedGoogle Scholar
  59. Pinho C, Harris DJ, Ferrand N: Non-equilibrium estimates of gene flow inferred from nuclear genealogies suggest that Iberian and North African wall lizards (Podarcis spp.) are an assemblage of incipient species. BMC Evol Biol. 2008, 8: 63-(http://www.biomedcentral.com/1471-2148/8/63)PubMedPubMed CentralGoogle Scholar
  60. Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.PubMedPubMed CentralGoogle Scholar
  61. Evanno G, Regnaut S, Goudet J: Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study Molecular ecology. 2005, 14: 2611-2620.PubMedGoogle Scholar
  62. Banks MA, Eichert W: WHICHRUN (version 3.2): A computer program for population assignment of individuals based on multilocus genotype data. J Hered. 2000, 91 (1): 87-89.PubMedGoogle Scholar
  63. Corander J, Waldmann P, Sillanpaa MJ: Bayesian analysis of genetic differentiation between populations. Genetics. 2003, 163 (1): 367-374.PubMedPubMed CentralGoogle Scholar
  64. Dawson KJ, Belkhir K: A Bayesian approach to the identification of panmictic populations and the assignment of individuals. Genet Res. 2001, 78 (1): 59-77.PubMedGoogle Scholar
  65. Manel S, Berthier P, Luikart G: Detecting wildlife poaching: Identifying the origin of individuals with Bayesian assignment tests and multilocus genotypes. Conserv Biol. 2002, 16 (3): 650-659.Google Scholar
  66. Pritchard JK, Donnelly P: Case–control studies of association in structured or admixed populations. Theor Popul Biol. 2001, 60 (3): 227-237.PubMedGoogle Scholar
  67. Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, Feldman MW: Genetic structure of human populations. Science. 2002, 298 (5602): 2381-2385.PubMedGoogle Scholar
  68. Turakulov R, Easteal S: Number of SNPS loci needed to detect population structure. Hum Hered. 2003, 55 (1): 37-45.PubMedGoogle Scholar
  69. Babault J, Teixell A, Arboleya ML, Charroud M: A late cenozoic age for long-wavelength surface uplift of the atlas mountains of Morocco. Terra Nova. 2008, 20 (2): 102-107.Google Scholar
  70. Gomez F, Beauchamp W, Barazangi M: Role of the Atlas Mountains (northwest Africa) within the African-Eurasian plate-boundary zone. Geology. 2000, 28 (9): 775-778.Google Scholar
  71. Brown JW, Rest JS, Garcia-Moreno J, Sorenson MD, Mindell DP: Strong mitochondrial DNA support for a Cretaceous origin of modern avian lineages. BMC Biol. 2008, 6: 6-(http://www.biomedcentral.com/1741-7007/6/6/)PubMedPubMed CentralGoogle Scholar
  72. Sousa P, Froufe E, Harris DJ, Alves PC, van der Meijden A: Genetic diversity of Maghrebian Hottentotta (Scorpiones: Buthidae) scorpions based on CO1: new insights on the genus phylogeny and distribution. Afr Invertebr. 2011, 52 (1): 135-143.Google Scholar
  73. Masembe C, Muwanika VB, Nyakaana S, Arctander P, Siegismund HR: Three genetically divergent lineages of the Oryx in eastern Africa: Evidence for an ancient introgressive hybridization. Conserv Genet. 2006, 7 (4): 551-562.Google Scholar
  74. Perera A, Vasconcelos R, Harris DJ, Brown RP, Carretero MA, Perez-Mellado V: Complex patterns of morphological and mtDNA variation in Lacerta perspicillata (Reptilia; Lacertidae). Biol J Linn Soc. 2007, 90 (3): 479-490.Google Scholar
  75. de Pous P, Beukema W, Weterings M, Dummer I, Geniez P: Area prioritization and performance evaluation of the conservation area network for the Moroccan herpetofauna: a preliminary assessment. Biodivers Conserv. 2011, 20 (1): 89-118.Google Scholar
  76. Barata M, Perera A, Harris DJ, Van Der Meijden A, Carranza S, Ceacero F, García-Muñoz E, Gonçalves D, Henriques S, Jorge F, et al: New observations of amphibians and reptiles in Morocco, with a special emphasis on the eastern region. Herpetological Bulletin. 2011, 116: 4-14.Google Scholar
  77. Pounds JA, Fogden MPL, Campbell JH: Biological responses to climate change on a tropical mountain. Nature. 1999, 398: 611-615.Google Scholar
  78. Vasconcelos R, Perera A, Geniez P, Harris DJ, Carranza S: An integrative taxonomic revision of the Tarentola geckos (Squamata, Phyllodactylidae) of the Cape Verde Islands. Zool J Linn Soc-Lond. 2012, 164: 328-360.Google Scholar
  79. Carranza S, Arnold EN: A review of the geckos of the genus Hemidactylus (Squamata: Gekkonidae) from Oman based on morphology, mitochondrial and nuclear data, with descriptions of eight new species. Zootaxa. 2012, 3378: 1-95.Google Scholar
  80. Sambrook J, Fritsch EF, Maniatis T: Molecular cloning: a laboratory manual, 3nd edt edn. 1989, New York: Cold Sring Harbor Laboratory PressGoogle Scholar
  81. Kocher TD, Thomas WK, Meyer A, Edwards SV, Pääbo S, Villablanca FX, Wilson AC: Dynamics of mitochondrial-DNA evolution in animals - Amplification and sequencing with conserved primers. Proc Natl Acad Sci U S A. 1989, 86 (16): 6196-6200.PubMedPubMed CentralGoogle Scholar
  82. Harris DJ, Arnold EN: Relationships of wall lizards, Podarcis (Reptilia: Lacertidae) based on mitochondrial DNA sequences. Copeia. 1999, 3: 749-754.Google Scholar
  83. Arévalo E, Davis SK, Sites JW: Mitochondrial-DNA Sequence Divergence and Phylogenetic-Relationships among 8 Chromosome Races of the Sceloporus-Grammicus Complex (Phrynosomatidae) in Central Mexico. Syst Biol. 1994, 43 (3): 387-418.Google Scholar
  84. Vidal N, Hedges SB: Molecular evidence for a terrestrial origin of snakes. P R Soc B. 2004, 271: S226-S229.Google Scholar
  85. Gamble T, Bauer AM, Greenbaum E, Jackman TR: Evidence for Gondwanan vicariance in an ancient clade of gecko lizards. J Biogeogr. 2008, 35 (1): 88-104.Google Scholar
  86. Gamble T, Bauer AM, Greenbaum W, Jackman TR: Out of the blue: a novel, trans-Atlantic clade of geckos (Gekkota, Squamata). Zoologica Scripta. 2008, 37 (4): 355-366.Google Scholar
  87. Pinho C, Rocha S, Carvalho BM, Lopes S, Mourao S, Vallinoto M, Brunes TO, Haddad CFB, Goncalves H, Sequeira F, et al: New primers for the amplification and sequencing of nuclear loci in a taxonomically wide set of reptiles and amphibians. Conserv Genet Resour. 2010, 2: 181-185.Google Scholar
  88. Godinho R, Crespo EG, Ferrand N, Harris DJ: Phylogeny and evolution of the green lizards, Lacerta spp. (Squamata: Lacertidae) based on mitochondrial and nuclear DNA sequences. Amphibia-Reptilia. 2005, 26 (3): 271-285.Google Scholar
  89. Bauer AM, de Silva A, Greenbaum E, Jackman T: A new species of day gecko from high elevation in Sri Lanka, with a preliminary phylogeny of Sri Lankan Cnemaspis (Reptilia, Squamata, Gekkonidae). Zoosystematics and Evolution. 2007, 83 (S1): 22-32.Google Scholar
  90. Hall TA: BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symposium Series. 1999, 41: 95-98.Google Scholar
  91. Katoh K, Misawa K, Kuma K, Miyata T: MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002, 30 (14): 3059-3066.PubMedPubMed CentralGoogle Scholar
  92. Flot J-F: SeqPHASE: a web tool for interconverting PHASE input/output files and FASTA sequence alignments. Mol Ecol Resour. 2010, 372: 372-(http://www.biomedcentral.com/1471-2148/10/372/)Google Scholar
  93. Stephens M, Donnelly P: A comparison of Bayesian methods for haplotype reconstruction from population genotype data. Am J Hum Genet. 2003, 73 (5): 1162-1169.PubMedPubMed CentralGoogle Scholar
  94. Harrigan RJ, Mazza ME, Sorenson MD: Computation vs. cloning: evaluation of two methods for haplotype determination. Mol Ecol Resour. 2008, 8 (6): 1239-1248.PubMedGoogle Scholar
  95. Rozas J, Sanchez-DelBarrio JC, Messeguer X, Rozas R: DnaSP, DNA polymorphism analyses by the coalescent and other methods. Bioinformatics. 2003, 19 (18): 2496-2497.PubMedGoogle Scholar
  96. Kumar S, Tamura K, Nei M: MEGA3: Integrated software for molecular evolutionary genetics analysis and sequence alignment. Brief Bioinform. 2004, 5 (2): 150-163.PubMedGoogle Scholar
  97. Posada D: jModelTest: Phylogenetic model averaging. Mol Biol Evol. 2008, 25 (7): 1253-1256.PubMedGoogle Scholar
  98. Akaike H: A new look at the statistical model identification. IEEE Trans Autom Control. 1974, 19 (6): 716-723.Google Scholar
  99. Stamatakis A: RAxML-VI-HPC: Maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics. 2006, 22 (21): 2688-2690.PubMedGoogle Scholar
  100. Felsenstein J: Confidence-Limits on Phylogenies - an Approach Using the Bootstrap. Evolution. 1985, 39 (4): 783-791.Google Scholar
  101. Huelsenbeck JP, Ronquist F: MrBayes: Bayesian inference of phylogeny. Bioinformatics. 2001, 17: 754-755.PubMedGoogle Scholar
  102. Templeton AR, Crandall KA, Sing CF: A Cladistic-Analysis of Phenotypic Associations with Haplotypes Inferred from Restriction Endonuclease Mapping and DNA-Sequence Data.3. Cladogram Estimation. Genetics. 1992, 132 (2): 619-633.PubMedPubMed CentralGoogle Scholar
  103. Clement M, Posada D, Crandall KA: TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000, 9: 1657-1659.PubMedGoogle Scholar
  104. Falush D, Stephens M, Pritchard JK: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003, 164: 1567-1587.PubMedPubMed CentralGoogle Scholar
  105. Documentation for STRUCTURE software. http://pritch.bsd.uchicago.edu, 2,
  106. Heled J, Drummond AJ: Bayesian inference of species trees from multilocus data. Molecular Biology and Evolution. 2010, 27 (3): 570-580.PubMedPubMed CentralGoogle Scholar
  107. Drummond AJ, Rambaut A: BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007, 7 (1): 214-PubMedPubMed CentralGoogle Scholar
  108. Rambaut A, Drummond AJ: Tracer v1.4. 2007, United Kingdom: University of EdinburghGoogle Scholar
  109. Rambaut A: FigTree v1.3.1. 2008, University of Edinburgh, UK: Institute of Evolutionary BiologyGoogle Scholar
  110. Poulakakis N, Lymberakis P, Valakos E, Pafilis P, Zouros E, Mylonas M: Phylogeography of Balkan wall lizard (Podarcis taurica) and its relatives inferred from mitochondrial DNA sequences. Mol Ecol. 2005, 14: 2433-2443.PubMedGoogle Scholar
  111. Heads M: Dating nodes on molecular phylogenies: a critique of molecular biogeography. Cladistics. 2005, 21 (1): 62-78.Google Scholar
  112. Benton MJ, Ayala FJ: Dating the Tree of Life. Science. 2003, 300 (5626): 1698-1700.PubMedGoogle Scholar
  113. Ho SYW, Phillips MJ: Accounting for calibration uncertainty in phylogenetic estimation of evolutionary divergence times. Syst Biol. 2009, 58 (3): 367-380.PubMedGoogle Scholar

Copyright

© Barata et al.; licensee BioMed Central Ltd. 2012

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement