Biochemical Systematics and Ecology 38 (2010) 73–82

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Biochemical Systematics and Ecology

journal homepage: www.elsevier .com/locate/biochemsyseco

Genetic structure of Pinus pinaster Ait. populations in Morocco revealedby nuclear microsatellites

Nadya Wahid a,b,*, Krassimir D. Naydenov d, Salim Kamari c, Abdelali Boulli b,Francine Tremblay d

a Universite Laval, Faculte de Foresterie, de Geomatique et de Geographie, Departement des sciences du bois et de la foret, Pavillon Abitibi-Price, bureau 3171,2405 rue de la Terrasse, Quebec (Qc), Canada G1V 0A6b Laboratoire d’analyse et de valorisation des ressources environnementales, Departement de Biologie, Universite Cadi Ayyad, Faculte des Sciences et Techniques deBeni Mellal, BP 523, Beni Mellal, Moroccoc Laboratoire d’ecologie, Departement de Biologie, Universite Mohammed Premier, Faculte des Sciences, B.P. 524, Oujda 60000, Moroccod Chaire CRSNG-UQAT-UQAM en amenagement forestier durable, Universite du Quebec en Abitibi-Temiscamingue, 445 boul. de l’universite, Rouyn-Noranda,QC, Canada J9X 5E4

a r t i c l e i n f o

Article history:Received 12 May 2009Accepted 12 December 2009

Keywords:Nuclear microsatellites markersPlant conservationGeographic variationPhylogeography

* Corresponding author at: Universite Laval, FacuPavillon Abitibi-Price, bureau 3171, 2405 rue de la T

E-mail address: wahid2na@yahoo.fr (N. Wahid).

0305-1978/$ – see front matter Crown Copyright �doi:10.1016/j.bse.2009.12.008

a b s t r a c t

Pinus pinaster is one of the most popular conifers used for reforestation in Morocco andrepresents an economically and ecologically important species for the region. In this study,nuclear microsatellites (ncSSRs) are used to compare genetic structure and diversityestimates of natural populations of Moroccan maritime pine. Samples were collectedamong 10 natural populations distributed in three biogeographically different regions, theRif Mountain, the Middle and the High Atlas. Forty-five nuclear alleles at seven variableloci were found with a mean of 6.4 alleles per locus. A number of private alleles (17.1%)were shown in populations from Rif and Middle Atlas. Moreover, in Morocco, P. pinastershowed a lower genetic diversity than in other parts of its geographic range. Significantdepartures from Hardy–Weinberg equilibrium with excess homozygosity are observedindicating a high level of mating inside populations. Genetic diversity was structured withhigh variability among populations (Fst ¼ 12%). Results show a correlation between geneticand geographic distances with an R-squared of 0.436. Two clusters were found usingSTRUCTURE, whereas three main clusters can be distinguished based on genetic distancesof phylogenetic tree. Genetic relationships among maritime pine populations in Moroccoappear to be related to historical, ecological as well as anthropogenic factors, suggestingthe need for conservation strategies at the population level.

Crown Copyright � 2009 Published by Elsevier Ltd. All rights reserved.

1. Introduction

Intraspecific patterns of genetic variation can often be used to identify biogeographic divisions which can be especiallyuseful in the design of conservation strategies (Petit et al., 1998). If conservation strategies for tree species are to addressevolutionary dynamics, data such as these suggest that both the amount of genetic variation and genetic structure at different

lte de Foresterie, de Geomatique et de Geographie, Departement des sciences du bois et de la foret,errasse, Quebec (Qc), Canada G1V 0A6. Tel.: þ1 418 656 2131x4964; fax: þ1 418 656 3351.

2009 Published by Elsevier Ltd. All rights reserved.

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–8274

scales must be considered for two reasons (Hamrick and Godt, 1990). First, the knowledge of intraspecific genetic variationand structure should allow a more representative sample of the species genetic variation to be included in conservationplanning. This is important to minimise the genetic impact of any management strategy and, to account for locally adaptivevariation. Second, the distribution of genetic variation must be considered because genetic structure reflects, at least in part,the action of population genetic processes.

Maritime pine (Pinus pinaster Ait.) is one of the most important forest species of the Mediterranean occidental basin andthe Atlantic coastal region of southern Europe (Critchfield and Little, 1966; Destremau et al., 1976; Barbero et al., 1998).Maritime pine is important in the context of ecosystem conservation for the protection of sand dunes and economically fora source of wood and pulp for the paper industry. In Morocco, this species is threatened by recurrent wildfire, over-exploitation and the absence of natural regeneration (Cauvin et al., 1997; M’Hirit et al., 1997). Recent changes in forestry policyrequire the introduction of alternative reforestation systems enabling greater structural diversity and enhancing aestheticvalues as well as environmental benefits. The development and implementation of the New Forest Management (NFM) planimplies the consideration of within-species genetic variability and adaptability.

Genetic studies carried out on the Moroccan maritime pine provenances have primarily addressed biogeography distri-bution (Benabid, 1982) and provenance performance (Sbay et al., 1997). These studies revealed a higher phenotypical vari-ability among populations growing in different regions rather than among populations from the same geographical area. Butrecent studies (Wahid et al., 2004, 2006) using allozyme and morphological markers indicate that genetic variation ofmaritime pine in Morocco is highly structured. However, to date, the genetic diversity of P. pinaster using molecular geneticmarker (MGM) has not been established for whole area of its natural distribution in Morocco. The objective of this investi-gation is to use nuclear microsatellite markers (ncSSRs) to investigate the relative impacts of colonization history andgeographical distance as determinants to the genetic diversity and population structure of natural populations of P. pinaster inMorocco. For this goal we 1) compare the levels of within- and among-population diversity assessed by nuclear markers, 2)analyze the genetic diversity of P. pinaster populations in order to provide valuable information through gene flux, coefficientof differentiation and effective population size, and 3) determine the genetic structure of the species and draw recom-mendations for conservation purposes.

2. Materials and methods

2.1. Geographical information and sample collection

Moroccan maritime pine grows in natural stands on a variety of soil types and climatic conditions, both in mountains andlow lands environments. Nowadays, this species covers approximately 12,000 ha and, although it is not widespread, it isfound in most forested regions: the High Atlas, the Middle Atlas, the Rif and the Mediterranean coastal region (Wahid, 2007).It is the second species used for reforestation.

In this analysis, samples were collected from 10 natural populations of P. pinaster corresponding to the Rif, theMiddle Atlas and the High Atlas regions (Table 1, Fig. 1). A total of 240 trees, about 72 years old, and an average of 16–35 trees per population were sampled: 147 from the Rif region, 58 from the Middle Atlas, and 35 from the High Atlas.Trees were chosen randomly, with no phenotypical selection, and were at least 50 m away from each other to avoidsampling bias from related individuals.

2.2. DNA extraction

Extracted seeds were placed in Petri dishes on top of filter paper disks moistened with distilled water. DNA was extractedfrom germinated embryos following the mini preparation kit technical bulletin of Sigma (product Code: G2N350). DNA wasextracted from 36 embryos per population (one to two embryos per tree depending on population area).

Table 1Geographic location of Morocco maritime pine populations analysed in this study.

Populations Code Provenance region Number of trees Latitude North Longitude West Altitude Size (ha)

Punta Ceres Pc Rif Occidental 24 35�550 5�280 40 125Koudiat Erramla Kr Rif Occidental 23 35�280 5�230 400 140Jbel Bouhachem Jb Rif Central 30 35�140 5�250 1094 95Madisouka Mad Rif Central 19 35�100 5�090 1880 168Adeldal Adl Rif Central 20 35�080 5�010 1450 168Tadouine Tad Rif Central 31 34�560 4�320 1520 172Talaghine Tal Middle Atlas 16 32�270 5�140 1840 20Tamjout Tamj Middle Atlas Oriental 26 33�50 3�590 1550 130Zaouia Ifrane Zi Middle Atlas 16 33�130 5�360 1510 250Sidi Meskour Sm High Atlas 35 31�280 6�50 1910 96

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–82 75

2.3. DNA analysis

Genotyping of individuals was performed by screening seven nuclear primer pairs: PtTX-3025, PtTX-2090, PtTX-3030,PtTX-3020, PtTX-2123, PtTX-3118 and P-7 that were originally developed for other Pinus species (Lian et al., 2000; Zhou et al.,2002). ncSSR primers were obtained from Sigma Genesy. The DNA amplifications by polymerase chain reaction (PCR) werecarried out in a Perkin–Elmer 9700 thermocycler with HotMaster Taq DNA Polymerase of Eppendorf AG in 10 ml reactionvolume, following Naydenov et al. (2006). PCR products fluorescent dye labelled (0.65 ml) were mixed with 12 ml Hi-DiFormamide (Applied Biosystems) and internal size standard 0.36 ml (50–800 pb (Peter and Feldhim, 2005)) (Applied Bio-systems). The mix of PCR product was denatured for 5 min at 95 �C and separated by capillary electrophoresis on an AppliedBiosystems Prism 310 Genetic Analyser. Automatic sizing of the amplified fragments was performed using Genscan� soft-ware (Applied Biosystems).

2.4. Genetic diversity estimates

The genetic diversity was determined by their overall allelic frequency (pk), the number of alleles per locus (Na), theeffective number of alleles (Ne ¼ 1/(1 � He)), the number of private alleles by population (Npa) and the number of locallycommon alleles (Nlca) (frequency> 50%). Genetic diversity is assessed for each sample, by calculating expected and observedfrequency heterozygotes (He, Ho), respectively. The statistical analysis of the genetic diversity data is carried out using thesoftware GenALEx (Peakall and Smouse, 2006) and the heterozygote deficiency for each locus and population is tested usingthe GenePop software (Raymond and Rousset, 1995).

2.5. Population genetic structure

The population genetic structure of Moroccan maritime pine is investigated using an analysis of molecular variance(AMOVA) (e.g., Excoffier et al.,1992) based on the sum of the squared number of repeat differences between two heterozygotes(Michalakis and Excoffier, 1996). We use a hierarchical analysis of variance to partition the total variance into covariancecomponents due to intra-population and inter-populations variations and test the covariance components significance usingpermutation tests (1000 permutations) at different levels. Only P-values lower than 0.01 are considered significant. The AMOVAanalysis and significance tests are performed using GenALEx version 6.1 (Peakall and Smouse, 2006). Genetic differentiationamong samples is quantified using Fst values calculated in GenePop (Raymond and Rousset, 1995) and Rst values (Michalakisand Excoffier, 1996) calculated in GenALEx. The analogue Rst accounts for the stepwise mode of mutation that characterizesmicrosatellite loci and thus a comparison of corresponding Fst and Rst values can shed light on the relative importance of driftand mutation underpinning genetic differentiation. These estimators are expected to be similar when drift is most important,while Rst should increase relative to Fst as the contribution of stepwise mutation to differentiation increases. Correspondencebetween estimates of genetic distance and geographic distance populations is assessed using Mantel tests for matrix correlationwith a test for a significant relationship by random permutation, following Smouse and Peakall (1999).

Because of uncertainty regarding what constitutes the most appropriate method for quantifying genetic distances amongpopulations based on polymorphism, several empirical methods were developed and a Principal Component Analysis (PCA) isconducted on multilocus microsatellite genotype data to predefined groupings, by using programs GenALEx. The first twoprincipal components (eigenvalue > 1), explaining 90.10% of the total variance in our data set, were retained. The firstprincipal component, PCA1, explains 78.15% of the total variance, while PCA2 explains 11.95%. We use Bayesian analysisimplemented in the program STRUCTURE 2.0 (Pritchard et al., 2000) to infer population structure and evaluate groupings. Theobjective is to identify the optimum number of partitions among groups of samples, corresponding to the proportion of itsgenome estimated to have ancestry in the cluster. Many replicate runs of the STRUCTURE correlated allele frequency modelwere performed using 50,000 iterations following a burn-in period of 250,000. To explore the population structure, we let thenumber of populations (K) vary between 1 and 50 and estimation of the number of populations cluster is performed usinga model with the best log-likelihood score (ln (L(K))). Then, we analyze our results according to method described inSTRUCTURE program, in which the number of populations (K) is plotted against DK ¼mjL00(K)j/sjL(K)j in which the estimatednumber of populations cluster identified by the largest change in log-likelihood (L(K)) values between estimated number ofpopulations. We also make a phylogenetic tree with UPGMA algorithm by using the program Phylip version 3.5 (Felsenstein,1993) to calculate Nei’s standard genetic distance (Nei, 1973). Confidence levels on tree topology are estimated from thepercentage of 1000 bootstraps, which performed re-sampling of loci within samples (Felsenstein, 1993). We use AMOVA inGenALEx to analyze the groups identified by results from the STRUCTURE and UPGMA analysis.

3. Results

3.1. Genetic variability within populations

The Moroccan P. pinaster population is genetically diverse. A total of 45 nuclear alleles at seven variable loci with a mean of6.4 alleles per locus were detected in ten populations combined (Table 2).

Table 2Allele frequencies of nuclear microsatellite alleles found by populations.

Locus Allele Tad Jb Adl Kr Mad Tamj Pc Sm Zi Tal

3025 260 – – 0.029 – 0.139 – – – – –261 – – – – – – 0.167 – – –265 – – – – 0.014 – – – – –266 – – – – 0.014 – – – – 0.015267 1.000 1.000 0.971 1.000 0.833 1.000 0.833 1.000 1.000 0.985

2090 186 0.625 0.583 0.597 0.792 0.528 0.278 0.444 0.375 0.500 0.236274 – – – – 0.014 – – – – –290 – – 0.028 – 0.097 – – – – –299 0.139 0.014 0.097 – 0.111 0.306 0.069 0.250 0.056 0.569312 – – – – – 0.042 0.014 – – –314 – – – – 0.028 0.014 0.014 0.028 – –337 0.236 0.389 0.278 0.208 0.222 0.361 0.292 0.194 0.264 0.097338 – 0.014 – – – – 0.167 – 0.042 –339 – – – – – – – 0.153 0.139 0.097

3030 186 0.162 0.222 0.194 0.181 0.181 0.043 – 0.014 – –187 0.221 0.167 0.056 0.111 0.028 0.229 0.111 0.292 0.071 0.181188 0.544 0.611 0.722 0.708 0.792 0.729 0.764 0.542 0.700 0.431189 0.059 – 0.028 – – – 0.125 0.097 0.171 0.111197 0.015 – – – – – – 0.056 0.043 0.278200 – – – – – – – – 0.014 –

3020 190 – – – – – – – – – 0.069191 0.014 – 0.014 – – – – – 0.014 0.167193 – 0.129 – – – – – – – –197 – 0.014 – – – – – – – –198 – – – 0.015 – 0.014 – – – –199 0.417 0.743 0.629 0.529 0.576 0.528 0.417 0.139 0.250 0.125200 0.542 0.114 0.214 0.294 0.364 0.347 0.528 0.750 0.458 0.500201 0.028 – 0.143 0.162 0.061 0.111 0.056 0.111 0.278 0.139

2123 161 – – 0.014 – – – – – – –164 0.125 0.292 0.278 0.111 0.286 0.153 0.028 0.042 0.014 0.056166 0.125 – 0.097 0.028 0.114 0.208 0.014 – – 0.111168 – – 0.014 0.014 – – – – – 0.028178 0.278 0.653 0.333 0.417 0.286 0.153 0.319 0.111 0.278 0.083180 0.153 0.042 0.222 0.389 0.271 0.333 0.639 0.458 0.431 0.292182 0.306 0.014 0.042 0.042 0.043 0.153 – 0.389 0.278 0.431187 0.014 – – – – – – – – –

J-7 132 – 0.200 0.014 0.329 – – – – – 0.056134 1.000 0.771 0.986 0.671 1.000 1.000 0.729 0.971 1.000 0.944135 – 0.029 – – – – – 0.029 – –136 – – – – – – 0.271 – – –

3118 108 – – – – – 0.014 – – – –186 0.222 0.069 0.229 0.014 0.264 0.194 0.044 0.029 0.181 0.197196 0.139 0.306 0.029 0.042 0.028 – 0.132 0.457 – 0.076205 0.597 0.611 0.629 0.903 0.667 0.431 0.794 0.414 0.264 0.167208 0.042 0.014 0.114 0.042 0.042 0.361 0.029 0.100 0.556 0.561

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Genetic diversity parameters based on numbers of alleles are shown in Table 3. An average 3.471 alleles per locus is foundin native populations of maritime pine. The populations from Middle Atlas (in particular Tamj) showed high number of allelesand effective number of alleles (mean Na ¼ 3.857, Ne ¼ 2.435) than populations from Rif or High Atlas. Adeldal (Adl) andKoudiat Erramla (Kr) populations had, respectively, the highest and the lowest number of allele (3.857, 3.143) and slightlyeffective number allele (2.066, 1.831), both located in Rif region. A strong difference in number of variants between pop-ulations in the same region (Rif) was reflected in the first pattern of genetic diversity. Population from the High Atlas (Sm)presents medium values of number of allele (Na ¼ 3.429, Ne ¼ 2.184) and effective number allele in comparison to pop-ulations from the other two regions. A number of private alleles were found in the Rif and Middle Atlas populations but withhigh standard deviation among the Rif populations. In particular in Pc, Jb, Mad and Zi populations which contribute by 28.6%of genetic diversification/diversity. The highest value of number of locally common alleles (Nlca ¼ 1.00) without a privateallele was found in population form High Atlas (Sm), remarkably with a high fixation index (F¼ 0.559). On the other hand, thegenetic diversity observed and estimated in each population ranged from Ho ¼ 0.215 and He ¼ 0.382 in Kr (Rif Occidental) toHo ¼ 0.414 and He ¼ 0.490 in Tamj (Middle Atlas). In fact, genetic diversity estimates (He) were highest for Middle Atlaspopulations. In this case, Tamjout (Tamj) showed the highest value (0.490). The lowest values of diversity were found inKoudiat Erramla (Kr, 0.382) and Jbel Bouhachem (Jb, 0.408), both in the Rif region. Genetic diversity was intermediate at SidiMeskour (Sm, 0.434), the only population analysed from High Atlas.

Table 3Genetic diversity in Moroccan maritime pine. Number of individual analysed (N), number of alleles (Na), effective number of alleles (Ne), number of privatealleles by population (Npa) and number of locally common alleles (frequency > 50%) (Nlca), observed heterozygosity (Ho), expected heterozygosity (He),fixation index (F).

Code N Na Ne Npa Nlca Ho He F

RifPc 36 3.429 1.933 0.286 0.571 0.273 0.447 0.432Kr 36 3.143 1.831 0.008 0.875 0.215 0.382 0.354Jb 36 3.286 1.802 0.286 0.714 0.290 0.408 0.331Mad 36 3.714 2.116 0.286 0.714 0.400 0.434 0.070Adl 36 3.857 2.066 0.143 0.998 0.368 0.415 0.088Tad 36 3.429 2.248 0.143 0.998 0.366 0.434 0.158Mean 36 3.476 � 0.590 1.999 � 0.309 0.190 � 0.116 0.810 � 0.173 0.318 � 0.112 0.420 � 0.088 0.038 � 0.175

Middle AtlasTamj 36 3.857 2.435 0.143 0.671 0.414 0.490 0.231Tal 36 3.286 2.389 0.143 0.987 0.346 0.446 0.291Zi 36 3.286 2.149 0.286 0.714 0.280 0.433 0.324Mean 36 3.486 � 0.614 2.324 � 0.393 0.190 � 0.082 0.790 � 0.297 0.346 � 0.131 0.456 � 0.116 0.282 � 0.191

High AtlasSm 36 3.429 2.184 0.000 1.000 0.244 0.434 0.559Mean 36 3.429 � 0.571 2.184 � 0.376 0.000 1.000 � 0.436 0.244 � 0.093 0.434 � 0.111 0.559 � 0.140Average 36 3.471 � 0.179 2.115 � 0.107 0.171 � 0.127 0.829 � 0.351 0.320 � 0.036 0.432 � 0.03 0.284 � 0.054

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–82 77

3.2. Population genetic structure

Significant departure from Hardy–Weinberg equilibrium was observed in most populations at loci 3020 (Fis¼ 0.889, P< 0.01)and 3118 (Fis¼0.659, P<0.01) (Table 4). In contrast, locus 2090 reveals a significant efficiency in heterozygote in most populations(Fis¼�0.045, P< 0.05). Considering all populations, a slight heterozygote deficiency was evident in all loci indicated by positivevalues of Fit. No linkage disequilibrium was observed, indicating all seven loci segregate independently of each other.

Genetic divergence among the seven ncSSRs was also tested by AMOVA, using estimator’s parameters Phi (F). The AMOVAanalysis revealed that 17% of the variation was found among population with 83% of the diversity being expressed withinpopulations (Table 5). Moreover, genetic differentiation among populations across all regions in Morocco, was high (Fst¼ 12.1,P < 0.001) (Table 4). To assess the relative importance of drift and mutation in the observed genetic subdivision, we repeatedthe analysis of genetic differentiation using Rst. The calculate Rst among population was smaller than its Fst equivalent(Rst¼ 0.052, P< 0.001). The comparison between the three parameters Rst, Fst and AMOVA reveals a difference in valuebetween Rst and both Fst and AMOVA, but Fst differed little from the AMOVA’s results. The Mantel test results indicateda quite significant correlation between pair wise estimates of Fst and Rst (r ¼ 0.391, P ¼ 0.03). Furthermore, genetic andgeographic distances are correlated in the maritime pine populations from Morocco (Fig. 2). This correlation is marginallysignificant as shown by a Mantel test (r ¼ 0.45, p ¼ 0.06).

Analysis of principal component based in allele frequencies for all populations indicates that 90.10% of variation is explainedby the first two components (78.15% and 11.95%, respectively). Fig. 3 illustrates the projection of the populations compared tothese first two axes. It seems that most populations gather around the first component, but the second component differen-tiates the populations in only one clear group of Mediterranean coastal populations (Pc and Kr) can be easy distinguished, whileother ones are dispersed and composed by populations from Rif Central, Middle Atlas and High Atlas.

Models of population structure among the 360 genotypes were evaluated using STRUCTURE program. A Bayesian MCMCclustering approach supported the differentiation result by partitioning populations of the P. pinaster into two clusters (Fig. 4).In fact, we observed one peak indicating sub-structure with two sub-populations. Accordingly, there is also a large plateau inlikelihood values, indicating a most likely numbers of populations. However, cluster 1 comprising samples from Jb (91%), Kr(56%), Pc (60%), Adl (63%) and Mad (57%) was identified for the region of the Rif Occidental and Central. Cluster 2 wasconstituted by Sm from High Atlas (85%), Tamj (51%), Zi (73%) and Tal (82%) from Middle Atlas, and by the only population Tad

Table 4Inbreeding (F) coefficients based in seven polymorphic nuclear microsatellite loci of Pinus pinaster Ait. Significant values are represented by: *P < 0.05 and**P < 0.01, computed using bootstrap re-sampling over loci (1000 bootstraps).

Locus Fis Fit Fst

3025 0.238 0.323 0.1112090 �0.045* 0.057 0.0983030 0.235 0.281 0.0613020 0.889** 0.902 0.1152123 0.278* 0.361 0.1163118 0.659** 0.117 0.164J-7 �0.095 0.725 0.163

Mean 0.272 0.361 0.121

Table 5Analysis of molecular variance (AMOVA) F performed by sum of squared size difference overall population and among groups identified by STRUCTURE andUPGMA methods.

Source of variation Degree free Sum of squares Variance components Percent of variation

Overall populationsAmong populations 9 309.892 0.840 17%Within populations 350 1470.056 4.200 83%

Total 359 1779.947 5.040 100%

Structure clusterAmong groups 1 116.447 0.513 10%Among populations within groups 8 193.444 0.555 11%Within populations 350 1470.056 4.2 79%

Total 359 1779.947 5.268 100%

UPGMA clusterAmong groups 2 105.123 0.240 5%Among populations within groups 7 204.769 0.696 14%Within populations 350 1470.056 4.200 81%

Total 359 1779.947 5.136 100%

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–8278

(55%) from Rif Oriental. Detecting only 2 clusters is remarkable given the large biogeographic repartition between pop-ulations. The fraction of variation estimated by AMOVA among two clusters was 10% (Table 5). Fig. 5 shows an UPGMAphylogenetic tree based on Nei’s unbiased genetic distance. Three groups can be differentiated significantly with 5% ofcoefficient of variation (Table 5). One subdivided cluster differentiating further High Atlas (Sidi Meskour) population fromcombined Middle Atlas (Zaouia Ifrane and Talaghine). The second cluster includes all populations from Rif (Adeldhal, Mad-isouka, Tadouine, Jbel Bouhachem and Koudiat Erramla). Only population from Middle Atlas (Tamjout) was clearly integratedin these groups and not corresponding with their geographic location. Finally, the third cluster including one populationlocated in Occidental Rif (Punta Ceres). Mainly to PCA, Bayesian MCMC and UPGMA methods, the same clusters formed bypopulations from Mountain Atlas (Zi, Tal and Sm) and in other hand populations from Rif Central (Jb, Mad and Adl) wereshowed. Tad and Tamj, using the three methods, didn’t share the same group all time. The population of Pc forms a separategroup of the Rif Occidental with the PCA and UPGMA methods but it forms a single cluster with the other populations fromthe same geographic region (Rif) when assessed by the Bayesian MCMC method.

4. Discussion

4.1. Genetic diversity

To our knowledge, just a few populations located in the Middle Atlas, such as Tamjout were previously studied withmolecular markers (Gonzalez-Martınez et al., 2004; Bucci et al., 2007 and references therein). In the present study theestimates of genetic diversity in Moroccan P. pinaster are significantly lower, about half of those from other Mediterranean

Fig. 1. Location geographical of 10 native maritime pine populations sampled in this study.

Fig. 2. Correlation between Nei’s unbiased genetic distance and geographic distance. A linear trend line is also shown.

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–82 79

populations (Ne ¼ 4.62, He ¼ 0.786 in Aquitaine P. pinaster and Ne ¼ 3.52, He ¼ 0.717 in Corsica P. pinaster (Mariette et al.,2001) and He ¼ 0.88 in Iberian populations (Derory et al., 2002)). In line with this result, three from seven loci, populations ofMorocco P. Pinaster showed significant heterozygote deficiency. Moreover, Bucci et al. (2007) have reported in their studybased on interpolated haplotypes frequencies that the highest level of genetic diversity was present in central and southernSpain population of P. pinaster (Ne ¼ 18.4 and He ¼ 0.95), whereas the lower chloroplast gene diversity was observed inMorocco (Ne ¼ 4.74 and He ¼ 0.78). Allozyme analysis had previously revealed similar levels of low genetic variation andpolymorphism in the same Moroccan population (Wahid et al., 2004). Despite lower levels of diversity and polymorphic locifound in this study, were comparable to genetic studies of other pines, such as White pine (Marquardt et al., 2007; Jones et al.,2006), Red pine (Boys et al., 2005), Aleppo pine (Gomez et al., 2005). Polymorphism has been reported to largely vary fromtaxon to taxon (Ellegren et al., 1995; Feldmann et al., 1997). Maritime pine, like other conifers, can suffer from sever reducedyields, lower seed germination rates, lower survival rates, and slower seedling growth (Goldstein et al., 1995; Cauvin et al.,1997; Ledig, 1998). An alternative hypothesis to explain the low level of genetic diversity of Morocco maritime pine would beits small distribution range population fragmentation, such as the population in Talaghine from the Middle Atlas presentsonly 20 ha (M’Hirit et al., 1997). Moreover, populations of maritime pine in Morocco are threatened by overexploitation andfrequent fires (M’Hirit, 1999). Genetic drift has been widely reported as the main cause in reduction of genetic variability inmarginal or isolated populations of conifers (Ledig and Conkle, 1983; Hamrick et al., 1992; Fallour et al., 1997; Senneville et al.,2001).

The presence of unique or private alleles can be considered a measure of genetic distinctiveness. In this study, P. pinasterreveals 17.1% of private alleles. These private alleles were found in several populations from Rif and Middle Atlas. This indicatesthe low exchange of the pollen gene flow within P. pinaster populations. Hence, we speculate that historical genes interchangeamong populations region is difficult due to physical barrier. From a conservation perspective, the distribution pattern of alleles,the presence of private alleles in several populations examined, and the distribution of genetic variation in maritime pine couldbe used as a genetic criterion to protect as many distinct populations as possible throughout their range.

4.1.1. Population genetic structureThe overall Fis and Fit values were higher than zero and suggest a departure from Hardy–Weinberg equilibrium with

deficiency of heterozygotes. The increased levels of homozygosity and the departure from Hardy–Weinberg equilibrium could

Fig. 3. Principal Component Analysis of allele frequencies in 7 loci of all 10 populations.

Fig. 4. Graphical presentation of clusters for P. pinaster population using Bayesian MCMC model.

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–8280

be attributable to mating between closely related individuals or small populations and the evolutionary history of the speciesand its scattered distribution (Durel et al., 1996; Boys et al., 2005; Degen et al., 2006). Gene flow among population is esti-mated to be z1.96 individuals per generation. Genetic drift along with restricted gene flow might also explain the highdifferentiation found in Moroccan populations of maritime pine (Fst ¼ 12.1%) at nuclear loci. The presence of clear geneticdifference between may be the result of the scattered distribution due to ecological difference and habitat fragmentation(Boulli, 2001). Comparisons of genetic differentiation (Fst and Rst) between the most distant populations showed no evidenceof stepwise mutation has contributed to divergence.

Our results demonstrate that population structure does exist in Moroccan P. pinaster. Results from comparison of geneticstructure among populations with three methods, (PCA, Bayesian MCMC and Tree-UPGMA) were slightly different. PCAanalysis revealed several discontinuities in nuclear gene frequencies within species forming one clear separate group. But therobust analysis based on Bayesian MCMC reveals the presence of two main clusters distinguished with 10% of fractiondifferentiation: i) all populations from Rif except Tadouin (Tad) and ii) the Middle and High Atlas. The detection of clusterscomprising distant groups suggests that populations share a common history and thus likely originate from the same wave ofcolonization (Rossiter et al., 2007). However, the UPGMA phylogenetic trees were slightly distinct than the STRUCTURE MCMC

Fig. 5. UPGMA phylogenetic tree based on Nei’s genetic distance for 10 native populations of maritime pine in Morocco.

N. Wahid et al. / Biochemical Systematics and Ecology 38 (2010) 73–82 81

results and three clusters were obtained with 5% of differentiation. Similar structure with three groups with clear separatePunta Ceres population from other range of species has been reported by previous studies (Wahid et al., 2004, 2006). Mainly,differentiation among major clusters identified in this study reflects the discontinuous species range and habitat fragmen-tation. Three principal causes could explain the present-day distribution of genetic diversity: (1) the distribution of geneticvariability before the last glaciation, (2) the location of the glacial refuge (Burban and Petit, 2003), and (3) the migration routesfollowed by the species during the expansion subsequent to the climate warming (Bucci et al., 2007). Highest parts ofmountains and different topographic elements may have separated the populations into different parts. In conclusion,nuclear markers indicated that populations are strongly differentiated and determined the phylogeography relationship,supporting the current taxonomy of the species. This result provides a number of useful insights for conservation strategiesand genetic reserves of all natural populations which could be used by forestry management.

Acknowledgements

This study was funded by training grant from Chair AFD (Amenagement Forestier Durable) project, grant to F. Tremblay.

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