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

Biochemical Systematics and Ecology 38 (2010) 73–82

Contents lists available at ScienceDirect

Biochemical Systematics and Ecology journal homepage: www.elsevier.com/locate/biochemsyseco

Genetic structure of Pinus pinaster Ait. populations in Morocco revealed by 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 Ge´omatique et de Ge´ographie, De´partement des sciences du bois et de la foreˆt, Pavillon Abitibi-Price, bureau 3171, 2405 rue de la Terrasse, Que´bec (Qc), Canada G1V 0A6 b Laboratoire d’analyse et de valorisation des ressources environnementales, De´partement de Biologie, Universite´ Cadi Ayyad, Faculte´ des Sciences et Techniques de Be´ni Mellal, BP 523, Be´ni Mellal, Morocco c Laboratoire d’e´cologie, De´partement de Biologie, Universite´ Mohammed Premier, Faculte´ des Sciences, B.P. 524, Oujda 60000, Morocco d Chaire CRSNG-UQAT-UQAM en ame´nagement forestier durable, Universite´ du Que´bec en Abitibi-Te´miscamingue, 445 boul. de l’universite´, Rouyn-Noranda, QC, Canada J9X 5E4

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 May 2009 Accepted 12 December 2009

Pinus pinaster is one of the most popular conifers used for reforestation in Morocco and represents an economically and ecologically important species for the region. In this study, nuclear microsatellites (ncSSRs) are used to compare genetic structure and diversity estimates of natural populations of Moroccan maritime pine. Samples were collected among 10 natural populations distributed in three biogeographically different regions, the Rif Mountain, the Middle and the High Atlas. Forty-five nuclear alleles at seven variable loci 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. pinaster showed a lower genetic diversity than in other parts of its geographic range. Significant departures from Hardy–Weinberg equilibrium with excess homozygosity are observed indicating a high level of mating inside populations. Genetic diversity was structured with high variability among populations (Fst ¼ 12%). Results show a correlation between genetic and geographic distances with an R-squared of 0.436. Two clusters were found using STRUCTURE, whereas three main clusters can be distinguished based on genetic distances of phylogenetic tree. Genetic relationships among maritime pine populations in Morocco appear to be related to historical, ecological as well as anthropogenic factors, suggesting the need for conservation strategies at the population level. Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved.

Keywords: Nuclear microsatellites markers Plant conservation Geographic variation Phylogeography

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

* Corresponding author at: Universite´ Laval, Faculte´ de Foresterie, de Ge´omatique et de Ge´ographie, De´partement des sciences du bois et de la foreˆt, Pavillon Abitibi-Price, bureau 3171, 2405 rue de la Terrasse, Que´bec (Qc), Canada G1V 0A6. Tel.: þ1 418 656 2131x4964; fax: þ1 418 656 3351. E-mail address: [email protected] (N. Wahid). 0305-1978/$ – see front matter Crown Copyright Ó 2009 Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.bse.2009.12.008

74

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

scales must be considered for two reasons (Hamrick and Godt, 1990). First, the knowledge of intraspecific genetic variation and structure should allow a more representative sample of the species genetic variation to be included in conservation planning. This is important to minimise the genetic impact of any management strategy and, to account for locally adaptive variation. 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 and the Atlantic coastal region of southern Europe (Critchfield and Little, 1966; Destremau et al., 1976; Barbe´ro et al., 1998). Maritime pine is important in the context of ecosystem conservation for the protection of sand dunes and economically for a source of wood and pulp for the paper industry. In Morocco, this species is threatened by recurrent wildfire, overexploitation and the absence of natural regeneration (Cauvin et al., 1997; M’Hirit et al., 1997). Recent changes in forestry policy require the introduction of alternative reforestation systems enabling greater structural diversity and enhancing aesthetic values as well as environmental benefits. The development and implementation of the New Forest Management (NFM) plan implies the consideration of within-species genetic variability and adaptability. Genetic studies carried out on the Moroccan maritime pine provenances have primarily addressed biogeography distribution (Benabid, 1982) and provenance performance (Sbay et al., 1997). These studies revealed a higher phenotypical variability among populations growing in different regions rather than among populations from the same geographical area. But recent studies (Wahid et al., 2004, 2006) using allozyme and morphological markers indicate that genetic variation of maritime pine in Morocco is highly structured. However, to date, the genetic diversity of P. pinaster using molecular genetic marker (MGM) has not been established for whole area of its natural distribution in Morocco. The objective of this investigation is to use nuclear microsatellite markers (ncSSRs) to investigate the relative impacts of colonization history and geographical distance as determinants to the genetic diversity and population structure of natural populations of P. pinaster in Morocco. 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, coefficient of differentiation and effective population size, and 3) determine the genetic structure of the species and draw recommendations 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 and low lands environments. Nowadays, this species covers approximately 12,000 ha and, although it is not widespread, it is found 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, the Middle 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 avoid sampling 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 extracted from germinated embryos following the mini preparation kit technical bulletin of Sigma (product Code: G2N350). DNA was extracted from 36 embryos per population (one to two embryos per tree depending on population area).

Table 1 Geographic 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 Cere`s Koudiat Erramla Jbel Bouhachem Madisouka Adeldal Tadouine Talaghine Tamjout Zaouia Ifrane Sidi Meskour

Pc Kr Jb Mad Adl Tad Tal Tamj Zi Sm

Rif Occidental Rif Occidental Rif Central Rif Central Rif Central Rif Central Middle Atlas Middle Atlas Oriental Middle Atlas High Atlas

24 23 30 19 20 31 16 26 16 35

35 550 35 280 35 140 35 100 35 080 34 560 32 270 33 50 33 130 31 280

5 280 5 230 5 250 5 090 5 010 4 320 5 140 3 590 5 360 6 50

40 400 1094 1880 1450 1520 1840 1550 1510 1910

125 140 95 168 168 172 20 130 250 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) were carried out in a Perkin–Elmer 9700 thermocycler with HotMaster Taq DNA Polymerase of Eppendorf AG in 10 ml reaction volume, following Naydenov et al. (2006). PCR products fluorescent dye labelled (0.65 ml) were mixed with 12 ml Hi-Di Formamide (Applied Biosystems) and internal size standard 0.36 ml (50–800 pb (Peter and Feldhim, 2005)) (Applied Biosystems). The mix of PCR product was denatured for 5 min at 95  C and separated by capillary electrophoresis on an Applied Biosystems Prism 310 Genetic Analyser. Automatic sizing of the amplified fragments was performed using GenscanÔ software (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), the effective number of alleles (Ne ¼ 1/(1  He)), the number of private alleles by population (Npa) and the number of locally common alleles (Nlca) (frequency > 50%). Genetic diversity is assessed for each sample, by calculating expected and observed frequency heterozygotes (He, Ho), respectively. The statistical analysis of the genetic diversity data is carried out using the software GenALEx (Peakall and Smouse, 2006) and the heterozygote deficiency for each locus and population is tested using the 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 covariance components due to intra-population and inter-populations variations and test the covariance components significance using permutation tests (1000 permutations) at different levels. Only P-values lower than 0.01 are considered significant. The AMOVA analysis and significance tests are performed using GenALEx version 6.1 (Peakall and Smouse, 2006). Genetic differentiation among samples is quantified using Fst values calculated in GenePop (Raymond and Rousset, 1995) and Rst values (Michalakis and Excoffier, 1996) calculated in GenALEx. The analogue Rst accounts for the stepwise mode of mutation that characterizes microsatellite loci and thus a comparison of corresponding Fst and Rst values can shed light on the relative importance of drift and 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. Correspondence between estimates of genetic distance and geographic distance populations is assessed using Mantel tests for matrix correlation with 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 among populations based on polymorphism, several empirical methods were developed and a Principal Component Analysis (PCA) is conducted on multilocus microsatellite genotype data to predefined groupings, by using programs GenALEx. The first two principal components (eigenvalue > 1), explaining 90.10% of the total variance in our data set, were retained. The first principal component, PCA1, explains 78.15% of the total variance, while PCA2 explains 11.95%. We use Bayesian analysis implemented in the program STRUCTURE 2.0 (Pritchard et al., 2000) to infer population structure and evaluate groupings. The objective is to identify the optimum number of partitions among groups of samples, corresponding to the proportion of its genome estimated to have ancestry in the cluster. Many replicate runs of the STRUCTURE correlated allele frequency model were performed using 50,000 iterations following a burn-in period of 250,000. To explore the population structure, we let the number of populations (K) vary between 1 and 50 and estimation of the number of populations cluster is performed using a model with the best log-likelihood score (ln (L(K))). Then, we analyze our results according to method described in STRUCTURE program, in which the number of populations (K) is plotted against DK ¼ mjL00 (K)j/sjL(K)j in which the estimated number of populations cluster identified by the largest change in log-likelihood (L(K)) values between estimated number of populations. 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 the percentage of 1000 bootstraps, which performed re-sampling of loci within samples (Felsenstein, 1993). We use AMOVA in GenALEx 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 of 6.4 alleles per locus were detected in ten populations combined (Table 2).

76

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

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

Allele

Tad

Jb

Adl

Kr

Mad

Tamj

Pc

Sm

Zi

Tal

3025

260 261 265 266 267

– – – – 1.000

– – – – 1.000

0.029 – – – 0.971

– – – – 1.000

0.139 – 0.014 0.014 0.833

– – – – 1.000

– 0.167 – – 0.833

– – – – 1.000

– – – – 1.000

– – – 0.015 0.985

2090

186 274 290 299 312 314 337 338 339

0.625 – – 0.139 – – 0.236 – –

0.583 – – 0.014 – – 0.389 0.014 –

0.597 – 0.028 0.097 – – 0.278 – –

0.792 – – – – – 0.208 – –

0.528 0.014 0.097 0.111 – 0.028 0.222 – –

0.278 – – 0.306 0.042 0.014 0.361 – –

0.444 – – 0.069 0.014 0.014 0.292 0.167 –

0.375 – – 0.250 – 0.028 0.194 – 0.153

0.500 – – 0.056 – – 0.264 0.042 0.139

0.236 – – 0.569 – – 0.097 – 0.097

3030

186 187 188 189 197 200

0.162 0.221 0.544 0.059 0.015 –

0.222 0.167 0.611 – – –

0.194 0.056 0.722 0.028 – –

0.181 0.111 0.708 – – –

0.181 0.028 0.792 – – –

0.043 0.229 0.729 – – –

– 0.111 0.764 0.125 – –

0.014 0.292 0.542 0.097 0.056 –

– 0.071 0.700 0.171 0.043 0.014

– 0.181 0.431 0.111 0.278 –

3020

190 191 193 197 198 199 200 201

– 0.014 – – – 0.417 0.542 0.028

– – 0.129 0.014 – 0.743 0.114 –

– 0.014 – – – 0.629 0.214 0.143

– – – – 0.015 0.529 0.294 0.162

– – – – – 0.576 0.364 0.061

– – – – 0.014 0.528 0.347 0.111

– – – – – 0.417 0.528 0.056

– – – – – 0.139 0.750 0.111

– 0.014 – – – 0.250 0.458 0.278

0.069 0.167 – – – 0.125 0.500 0.139

2123

161 164 166 168 178 180 182 187

– 0.125 0.125 – 0.278 0.153 0.306 0.014

– 0.292 – – 0.653 0.042 0.014 –

0.014 0.278 0.097 0.014 0.333 0.222 0.042 –

– 0.111 0.028 0.014 0.417 0.389 0.042 –

– 0.286 0.114 – 0.286 0.271 0.043 –

– 0.153 0.208 – 0.153 0.333 0.153 –

– 0.028 0.014 – 0.319 0.639 – –

– 0.042 – – 0.111 0.458 0.389 –

– 0.014 – – 0.278 0.431 0.278 –

– 0.056 0.111 0.028 0.083 0.292 0.431 –

J-7

132 134 135 136

– 1.000 – –

0.200 0.771 0.029 –

0.014 0.986 – –

0.329 0.671 – –

– 1.000 – –

– 1.000 – –

– 0.729 – 0.271

– 0.971 0.029 –

– 1.000 – –

0.056 0.944 – –

3118

108 186 196 205 208

– 0.222 0.139 0.597 0.042

– 0.069 0.306 0.611 0.014

– 0.229 0.029 0.629 0.114

– 0.014 0.042 0.903 0.042

– 0.264 0.028 0.667 0.042

0.014 0.194 – 0.431 0.361

– 0.044 0.132 0.794 0.029

– 0.029 0.457 0.414 0.100

– 0.181 – 0.264 0.556

– 0.197 0.076 0.167 0.561

Genetic diversity parameters based on numbers of alleles are shown in Table 3. An average 3.471 alleles per locus is found in native populations of maritime pine. The populations from Middle Atlas (in particular Tamj) showed high number of alleles and effective number of alleles (mean Na ¼ 3.857, Ne ¼ 2.435) than populations from Rif or High Atlas. Adeldal (Adl) and Koudiat Erramla (Kr) populations had, respectively, the highest and the lowest number of allele (3.857, 3.143) and slightly effective number allele (2.066, 1.831), both located in Rif region. A strong difference in number of variants between populations 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 populations from the other two regions. A number of private alleles were found in the Rif and Middle Atlas populations but with high 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 private allele was found in population form High Atlas (Sm), remarkably with a high fixation index (F ¼ 0.559). On the other hand, the genetic diversity observed and estimated in each population ranged from Ho ¼ 0.215 and He ¼ 0.382 in Kr (Rif Occidental) to Ho ¼ 0.414 and He ¼ 0.490 in Tamj (Middle Atlas). In fact, genetic diversity estimates (He) were highest for Middle Atlas populations. In this case, Tamjout (Tamj) showed the highest value (0.490). The lowest values of diversity were found in Koudiat Erramla (Kr, 0.382) and Jbel Bouhachem (Jb, 0.408), both in the Rif region. Genetic diversity was intermediate at Sidi Meskour (Sm, 0.434), the only population analysed from High Atlas.

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

77

Table 3 Genetic diversity in Moroccan maritime pine. Number of individual analysed (N), number of alleles (Na), effective number of alleles (Ne), number of private alleles 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

Rif Pc Kr Jb Mad Adl Tad Mean

36 36 36 36 36 36 36

3.429 3.143 3.286 3.714 3.857 3.429 3.476  0.590

1.933 1.831 1.802 2.116 2.066 2.248 1.999  0.309

0.286 0.008 0.286 0.286 0.143 0.143 0.190  0.116

0.571 0.875 0.714 0.714 0.998 0.998 0.810  0.173

0.273 0.215 0.290 0.400 0.368 0.366 0.318  0.112

0.447 0.382 0.408 0.434 0.415 0.434 0.420  0.088

0.432 0.354 0.331 0.070 0.088 0.158 0.038  0.175

Middle Atlas Tamj 36 Tal 36 Zi 36 Mean 36

3.857 3.286 3.286 3.486  0.614

2.435 2.389 2.149 2.324  0.393

0.143 0.143 0.286 0.190  0.082

0.671 0.987 0.714 0.790  0.297

0.414 0.346 0.280 0.346  0.131

0.490 0.446 0.433 0.456  0.116

0.231 0.291 0.324 0.282  0.191

High Atlas Sm Mean Average

3.429 3.429  0.571 3.471  0.179

2.184 2.184  0.376 2.115  0.107

0.000 0.000 0.171  0.127

1.000 1.000  0.436 0.829  0.351

0.244 0.244  0.093 0.320  0.036

0.434 0.434  0.111 0.432  0.03

0.559 0.559  0.140 0.284  0.054

36 36 36

3.2. Population genetic structure Significant departure from Hardy–Weinberg equilibrium was observed in most populations at loci 3020 (Fis ¼ 0.889, P < 0.01) and 3118 (Fis ¼ 0.659, P < 0.01) (Table 4). In contrast, locus 2090 reveals a significant efficiency in heterozygote in most populations (Fis ¼ 0.045, P < 0.05). Considering all populations, a slight heterozygote deficiency was evident in all loci indicated by positive values 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 AMOVA analysis revealed that 17% of the variation was found among population with 83% of the diversity being expressed within populations (Table 5). Moreover, genetic differentiation among populations across all regions in Morocco, was high (Fst ¼ 12.1, P < 0.001) (Table 4). To assess the relative importance of drift and mutation in the observed genetic subdivision, we repeated the analysis of genetic differentiation using Rst. The calculate Rst among population was smaller than its Fst equivalent (Rst ¼ 0.052, P < 0.001). The comparison between the three parameters Rst, Fst and AMOVA reveals a difference in value between Rst and both Fst and AMOVA, but Fst differed little from the AMOVA’s results. The Mantel test results indicated a quite significant correlation between pair wise estimates of Fst and Rst (r ¼ 0.391, P ¼ 0.03). Furthermore, genetic and geographic distances are correlated in the maritime pine populations from Morocco (Fig. 2). This correlation is marginally significant 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 explained by the first two components (78.15% and 11.95%, respectively). Fig. 3 illustrates the projection of the populations compared to these first two axes. It seems that most populations gather around the first component, but the second component differentiates the populations in only one clear group of Mediterranean coastal populations (Pc and Kr) can be easy distinguished, while other 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 MCMC clustering 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 in likelihood 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 was constituted by Sm from High Atlas (85%), Tamj (51%), Zi (73%) and Tal (82%) from Middle Atlas, and by the only population Tad Table 4 Inbreeding (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 2090 3030 3020 2123 3118 J-7

0.238 0.045* 0.235 0.889** 0.278* 0.659** 0.095

0.323 0.057 0.281 0.902 0.361 0.117 0.725

0.111 0.098 0.061 0.115 0.116 0.164 0.163

Mean

0.272

0.361

0.121

78

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

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

Degree free

Sum of squares

Variance components

Percent of variation

Overall populations Among populations Within populations

9 350

309.892 1470.056

0.840 4.200

17% 83%

Total

359

1779.947

5.040

100%

Structure cluster Among groups Among populations within groups Within populations

1 8 350

116.447 193.444 1470.056

0.513 0.555 4.2

10% 11% 79%

Total

359

1779.947

5.268

100%

UPGMA cluster Among groups Among populations within groups Within populations

2 7 350

105.123 204.769 1470.056

0.240 0.696 4.200

5% 14% 81%

Total

359

1779.947

5.136

100%

(55%) from Rif Oriental. Detecting only 2 clusters is remarkable given the large biogeographic repartition between populations. The fraction of variation estimated by AMOVA among two clusters was 10% (Table 5). Fig. 5 shows an UPGMA phylogenetic tree based on Nei’s unbiased genetic distance. Three groups can be differentiated significantly with 5% of coefficient of variation (Table 5). One subdivided cluster differentiating further High Atlas (Sidi Meskour) population from combined Middle Atlas (Zaouia Ifrane and Talaghine). The second cluster includes all populations from Rif (Adeldhal, Madisouka, Tadouine, Jbel Bouhachem and Koudiat Erramla). Only population from Middle Atlas (Tamjout) was clearly integrated in these groups and not corresponding with their geographic location. Finally, the third cluster including one population located in Occidental Rif (Punta Ceres). Mainly to PCA, Bayesian MCMC and UPGMA methods, the same clusters formed by populations from Mountain Atlas (Zi, Tal and Sm) and in other hand populations from Rif Central (Jb, Mad and Adl) were showed. Tad and Tamj, using the three methods, didn’t share the same group all time. The population of Pc forms a separate group of the Rif Occidental with the PCA and UPGMA methods but it forms a single cluster with the other populations from the 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 with molecular markers (Gonza´lez-Martı´nez et al., 2004; Bucci et al., 2007 and references therein). In the present study the estimates 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.

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

79

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

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 of Morocco P. Pinaster showed significant heterozygote deficiency. Moreover, Bucci et al. (2007) have reported in their study based on interpolated haplotypes frequencies that the highest level of genetic diversity was present in central and southern Spain population of P. pinaster (Ne ¼ 18.4 and He ¼ 0.95), whereas the lower chloroplast gene diversity was observed in Morocco (Ne ¼ 4.74 and He ¼ 0.78). Allozyme analysis had previously revealed similar levels of low genetic variation and polymorphism in the same Moroccan population (Wahid et al., 2004). Despite lower levels of diversity and polymorphic loci found 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 (Go`mez et al., 2005). Polymorphism has been reported to largely vary from taxon to taxon (Ellegren et al., 1995; Feldmann et al., 1997). Maritime pine, like other conifers, can suffer from sever reduced yields, 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 be its small distribution range population fragmentation, such as the population in Talaghine from the Middle Atlas presents only 20 ha (M’Hirit et al., 1997). Moreover, populations of maritime pine in Morocco are threatened by overexploitation and frequent fires (M’Hirit, 1999). Genetic drift has been widely reported as the main cause in reduction of genetic variability in marginal 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. pinaster reveals 17.1% of private alleles. These private alleles were found in several populations from Rif and Middle Atlas. This indicates the low exchange of the pollen gene flow within P. pinaster populations. Hence, we speculate that historical genes interchange among 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 could be used as a genetic criterion to protect as many distinct populations as possible throughout their range. 4.1.1. Population genetic structure The 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.

80

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

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

be attributable to mating between closely related individuals or small populations and the evolutionary history of the species and its scattered distribution (Durel et al., 1996; Boys et al., 2005; Degen et al., 2006). Gene flow among population is estimated to be z1.96 individuals per generation. Genetic drift along with restricted gene flow might also explain the high differentiation found in Moroccan populations of maritime pine (Fst ¼ 12.1%) at nuclear loci. The presence of clear genetic difference 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 evidence of stepwise mutation has contributed to divergence. Our results demonstrate that population structure does exist in Moroccan P. pinaster. Results from comparison of genetic structure among populations with three methods, (PCA, Bayesian MCMC and Tree-UPGMA) were slightly different. PCA analysis revealed several discontinuities in nuclear gene frequencies within species forming one clear separate group. But the robust analysis based on Bayesian MCMC reveals the presence of two main clusters distinguished with 10% of fraction differentiation: i) all populations from Rif except Tadouin (Tad) and ii) the Middle and High Atlas. The detection of clusters comprising distant groups suggests that populations share a common history and thus likely originate from the same wave of colonization (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 separate Punta Cere`s 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 fragmentation. Three principal causes could explain the present-day distribution of genetic diversity: (1) the distribution of genetic variability before the last glaciation, (2) the location of the glacial refuge (Burban and Petit, 2003), and (3) the migration routes followed by the species during the expansion subsequent to the climate warming (Bucci et al., 2007). Highest parts of mountains 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 strategies and genetic reserves of all natural populations which could be used by forestry management. Acknowledgements This study was funded by training grant from Chair AFD (Ame´nagement Forestier Durable) project, grant to F. Tremblay. References Barbe´ro, M., Loisel, R., Que´zel, P., Richardson, D.M., Romane, F., 1998. Pines of the Mediterranean basin. In: Dans Richardson, D.M. (Ed.), Ecology and Biogeography of Pinus. Cambridge University Press, Cambridge, pp. 153–170. Benabid, A., 1982. Eude phyto-e´cologie, bioge´ographique et dynamique des principales associations et se´ries sylvatiques du Rif occidental: Caracte´ristiques structurales des peuplements forestiers et proble`mes pose´s par leur ame´nagement (nord ouest du Maroc). The`se e`s Sciences. Fac. St. Je´roˆme, Marseille, 199 pp. Boulli, A., 2001. Ecologie et variabilite´ ge´ne´tique du pin d’Alep (Pinus halepensis Mill.) au Maroc: Introduction a` l’utilisation des marqueurs ge´ne´tiques. The`se de doctorat d’e´tat des sciences. Univ. Cady Ayyad, Marrakech, 165 pp. Boys, J., Cherry, M., Dayanandan, S., 2005. Microsatellite analysis reveals genetically distinct populations of red pine (Pinus resinosa, Pinaceae). American Journal Botany 92, 833–841. Bucci, G., Gonza´lez-Martı´nez, S.C., Le Provost, G., Polomion, C., Ribiero, M., Sebastiani, F., Alı`a, R., Vendramin, G.G., 2007. Rang-wide phylogeography and gene zones in Pinus pinaster Ait. revealed by chloroplast microsatellite markers. Molecular Ecology 16, 2137–2153. Burban, C., Petit, R.J., 2003. Phylogeography of maritime pine inferred with organelle markers having contrasted inheritance. Molecular Ecology 12, 1487–1495. Cauvin, B., Marien, J.N., El-Yousfi, S.M., 1997. Protection, conservation et gestion de ressources ge´ne´tiques forestie`res au Maroc. Annale Recherche Forestiere Maroc, 178–195. Critchfield, W.B., Little, E.L., 1966. Geographic Distribution of the Pines of the World. U.S.D.A. Forest Service Miscellaneous Publication 991. Degen, B., Blanc, L., Caron, H., Maggia, L., Kremer, A., 2006. Impact of selective logging on genetic composition and demographic structure of four tropical tree species. Biological Conservation 131, 386–401. Derory, J., Mariette, S., Gonzale´z-Martı´nez, S.C., Chagne´, D., Madur, D., Gerber, S., Brach, J., Persyn, F., Ribeiro, M.M., Plomion, C., 2002. What can nuclear microsatellites tell us about maritime pine genetic resources conservation and provenance certification strategies? Annals of Forest Science 59, 699–708. Destremau, D., Jolly, H., Tafri, T., 1976. Contribution a` la connaissance des provenances de Pinus pinaster. Annale Recherche Forestiere Maroc 16, 101–153. Durel, C.E., Bertin, P., Kremer, A., 1996. Relationship between inbreeding depression and inbreeding coefficient in maritime pine (Pinus pinaster Ait.). Theoretical and Applied Genetics 92, 347–356. Ellegren, H., Primmer, C.R., Sheldon, B.C., 1995. Microsatellite evolution: directionality or bias. Nature Genetic 11, 360–362. Excoffier, L., Smouse, P.E., Quattro, J.M., 1992. Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction sites. Genetics 131, 479–491. Fallour, D., Fady, B., Lefe`vre, F., 1997. Study on isoenzyme variation in Pinus pinea L.: evidence for low polymorphism. Silvae Genetica 46, 201–207. Feldmann, M.W., Bergman, A., Pollock, D.D., Goldestein, D.B., 1997. Microsatellite genetic distances with rang constraints: analytic description and problems of estimation. Genetics 145, 207–216. Felsenstein, J., 1993. PHYLIP 3.5 (Phylogeny Inference Package). University of Washington, Seatle, Washington, USA. Goldstein, D.B., Linares, A.R., Cavalli-Sforza, L.L., Feldman, M.W., 1995. An evaluation of genetic distances for use with microsatellite loci. Genetics 139, 463–471. Go`mez, A., Vendramin, G.G., Gonza´lez-Martı´nez, S.C., Alı`a, R., 2005. Genetic diversity and differentiation of two Mediterranean pines (Pinus halepensis Mill. and Pinus pinaster Ait.) along latitudinal cline using chloroplast microsatellite markers. Diversity and Distributions 11, 257–263. Gonza´lez-Martı´nez, S.C., Mariette, S., Ribeiro, M.M., Burban, C., Raffin, A., Chambel, M.R., Ribeiro, C.A.M., Aguiar, A., Plomion, C., Alı´a, R., Gil, L., Vendramin, G.G., Kremer, A., 2004. Genetic resources in maritime pine (Pinus pinaster Ait.): molecular and quantitative measures of genetic variation and differentiation among maternal lineages. Forest Ecology and Management 197, 103–115. Hamrick, J.L., Godt, M.J., Sherman-Broyles, S.L., 1992. Factors influencing levels of genetic diversity in woody plant species. New Forests 6, 95–124. Hamrick, J.L., Godt, M.J., 1990. Allozyme diversity in plant species. In: Brown, A.H.D., Clegg, M.T., Kahler, A.L., Weir, B.S. (Eds.), Plant Population Genetics, Breeding and Genetic Resources. Sinauer, Sunderland, Mass., pp. 43–63. Jones, F.A., Hamrick, J.L., Peterson, C.J., Squires, L.E., 2006. Inferring colonization history from analyses of spatial genetic structure within populations of Pinus strobus and Quercus rubra. Molecular Ecology 15, 851–861. Ledig, F.T., Conkle, M.T., 1983. Gene diversity and genetic structure in a narrow endemic, Torrey pine. Evolution 37, 79–85. Ledig, F.T., 1998. Genetic variation in Pinus. In: Richardson, M.D. (Ed.), Ecology and Biogeography of Pinus. Cambridge University Press, NewYork, pp. 251–280. Lian, C., Miwa, M., Hogetsu, T., 2000. Isolation and characterisation of microsatellite loci from the Japanese red pine (Pinus densiflora). Molecular Ecology 9, 1171–1193. Mariette, S., Chagneaˆ, D., Lea Zier, C., Pastuszka, P., Raffin, A., Plomion, P., Kremer, A., 2001. Genetic diversity within and among Pinus pinaster populations: comparison between AFLP and microsatellite markers. Heredity 86, 469–479. Marquardt, P.E., Craig, S.E., Eperson, B.K., Pubanz, D.M., 2007. Genetic structure, diversity, and inbreeding of eastern white pine under different management conditions. Canadian Journal of Forest Research 37, 2652–2662. M’Hirit, O., Sbay, H., El-Alami, S.L., Kerrouani, H., 1997. Bilan de la recherche en matie`re de ge´ne´tique forestie`re au Maroc. Annale Recherche Forestiere Maroc, 1–29. M’Hirit, O., 1999. Mediterranean forest: ecological space and economic and community wealth. Unasylva 197, 3–15. Michalakis, Y., Excoffier, L., 1996. A generic estimation of population subdivision using distances between alleles with special reference for microsatellite loci. Genetics 142, 1061–1064.

82

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

Naydenov, K.D., Tremblay, F.M., Fenton, N.J., Alexandrov, A., 2006. Structure of Pinus nigra Arn. population in Bulgaria revealed by chloroplast microsatellites and terpenes analysis: provenance tests. Biochemical Systematics and Ecology 34, 562–574. Nei, M., 1973. Analysis of gene diversity in subdivided populations. Proceedings of the National Academy of Science of the United States of America 70, 3321–3323. Peakall, R., Smouse, P.E., 2006. GenALEx: genetic analysis in Excel, population genetic software teaching and research. Molecular Ecology Notes 6, 288–295. Peter, G.A., Feldhim, A.K., 2005. A method to size DNA fragments from 50 to 800 bp on a DNA analyser. Molecular Ecology Notes 5, 969–970. Petit, R.J., El-Mousadik, A., Pons, O., 1998. Identifying populations for conservation on the basis of genetic markers. Conservation Biology 12, 844–855. Pritchard, J.K., Stephens, M., Donnelly, P., 2000. Inference of population structure using multilocus genotype data. Genetics 155, 945–959. Raymond, M., Rousset, F., 1995. GENEPOP (version 1.2): population genetics software for exact tests and ecumenicism. Heredity 86, 248–249. Rossiter, S.J., Benda, P., Dietz, C., Zhang, S., Jones, G., 2007. Range-wide phylogeography in the greater horseshoe bat inferred from microsatellites: implications for population history, taxonomy and conservation. Molecular Ecology 16, 4699–4714. Sbay, H., Arbez, M., Pastuszka, P., El-Alami, S.L., Rachidai, A., Ouassou, A., 1997. Analyse de la variabilite´ adaptative du Pin maritime au Maroc. Annale Recherche Forestiere Maroc, 64–78. Senneville, S., Beaulieu, J., Daoust, G., Deslauriers, M., Bousquet, J., 2001. Evidence for low genetic diversity and metapopulation structure in Canada yew (Taxus canadensis): considerations for conservation. Canadian Journal of Forest Research 31, 110–116. Smouse, P.E., Peakall, R., 1999. Spatial autocorrelation analysis of individual multi-allele and multi-locus genetic structure. Heredity 82, 561–573. Wahid, N., Gonza´lez-Martı´nez Santiago, C., El Hadrami, I., Boulli, A., 2004. Genetic structure and variability of the Moroccan natural populations of maritime pine (Pinus pinaster Aiton). Silvae Genetica 53, 93–99. Wahid, N., Gonza´lez-Martı´nez, Santiago C., El Hadrami, I., Boulli, A., 2006. Variation in morphological traits in natural populations of maritime pine (Pinus pinaster Ait.) in Morocco. Annals of Forest Science 63, 83–92. Wahid, N., 2007. Ecophysiologie et diversite´ ge´ne´tique du Pin maritime (Pinus pinaster Ait.) au Maroc. The`se nationale. Universite´ Cadi Ayyad, Faculte´ des Sciences Semlalia, Maroc. Zhou, Y., Bui, T., Auckland, L.D., Williams, C.G., 2002. Undermethylated DNA as source of microsatellites from a conifer genome. Genome 45, 91–99.