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Comparison of levels of genetic diversity detected with AFLP and microsatellite
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markers within and among mixed Q. petraea (Matt.) Liebl. and Q. robur L. stands
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Envoyé à Silvae Genetica le 30/05/01
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Stéphanie Mariette1*, Joan Cottrell2, Ulrike M. Csaikl3, Pablo Goikoechea4, Armin
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König5, Andrew J. Lowe6, Barbara C. Van Dam7, Teresa Barreneche1, Catherine
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Bodénès1, Réjane Streiff1, Kornel Burg3, Katrin Groppe5, Robert C. Munro6, Helen
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Tabbener2 and Antoine Kremer1
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INRA, Equipe de Génétique et Amélioration des Arbres Forestiers, BP45, F-33610
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Cestas, France.
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United Kingdom.
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3
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Austria.
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01080 Vitoria Gasteiz, Spain.
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5
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22927 Grosshandorf, Germany.
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Midlothian, EH26 0QB, Scotland, United Kingdom.
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The Netherlands.
Forest Research, Northern Research Station, Roslin, Midlothian, Scotland, EH25 9SY,
Österreichisches Forschungszentrum, Seibersdorf Ges. m. b. H., A-2444 Seiberdorf,
NEIKER, Dpto Producción y Protección Vegetal, Granja Modelo-Arkaute, Apdo 46,
BFH, Institut für Forstgenetik und Forstpflanzenzuechtung, Sieker Landstrasse 2,
Centre for Ecology and Hydrology, CEH, Edinburgh Bush Estate, Penicuik,
Alterra Green World Research, POB 47, Droevendaalsesteeg 3, 6700 AA Wageningen,
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*Corresponding author :
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Stéphanie Mariette
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INRA, Laboratoire de Génétique et Amélioration des Arbres
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Forestiers, BP45, 33610 Cestas, France.
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Tel: +33-5-57-97-90-83
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Fax: +33-5-57-97-90-88
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e-mail: stephanie.mariette@pierroton.inra.fr
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Running title: AFLP markers, microsatellites and Quercus spp.
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1
Summary
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In this study, we compare the genetic diversity within and among Quercus spp.
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populations assessed with two contrasting types of molecular markers: a limited number
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of highly polymorphic microsatellite markers and numerous less informative AFLP
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markers. Seven mixed stands of Quercus petraea and Quercus robur were analysed
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with six microsatellite markers and 155 AFLP loci. Genetic differentiation and genetic
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diversity within each population was assessed. The intra- and inter-locus variances were
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calculated and the results were used to compare the genetic diversity between
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populations. The rankings of populations provided by the two types of markers were
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compared. The results obtained with the two types of markers revealed the same general
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trend. The genetic diversity within population and the genetic differentiation among
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populations were greater in Q. petraea than in Q. robur. The genetic differentiation was
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generally higher when AFLP markers were used as compared to microsatellites, and it
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was also the case when only polymorphic AFLP fragments were used. For AFLP, the
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inter-locus variance was always much higher than the intra-locus variance, and explains
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why it was not possible to distinguish populations based on the level of diversity for this
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marker system. Finally, no significant positive correlation was found between the level
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of within-population assessed with the two markers.
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Keywords: microsatellite, AFLP, genetic diversity, genetic differentiation, Quercus
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robur, Quercus petraea.
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3
1
Introduction
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The assessment of genetic diversity with molecular markers in natural populations
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follows a two-stage sampling: (i) sampling of populations and individuals and (ii)
4
sampling of loci within the genome. The associated components of sampling variance
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have been termed “intra-locus variance” and “inter-locus variance” respectively and in
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theory the inter-locus variance should be much higher than the intra-locus component
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(Nei 1987). On the basis of allozymic data, Nei concluded that “a large number of loci
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should be examined even if the number of individuals per locus is small”. The larger
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inter-locus variance is likely to be the result of large differences in the mutation rates
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across the loci within the genome. There have been major advances in molecular
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techniques in recent years and as a result there is currently a wide range of markers
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available (Karp et al. 1997). Markers such as microsatellites are based on sequence data
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and this makes their development expensive. However, they have the advantage that
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they are codominant markers. In contrast markers such as Random Amplified
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Polymorphic DNA (RAPD) and Amplified Fragment Length Polymorphism (AFLP) are
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cheap to develop as no knowledge of DNA sequence is required for their development.
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They provide information on many loci which are randomly distributed throughout the
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genome; however they are usually dominant markers (Breyne et al. 1997).
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Consequently, for a given investment of time and money they provide information on a
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wider range of loci than microsatellites, but the information at a given locus is less
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specific. As a result, for a given amount of resources, two contrasting sampling
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strategies can be adopted to assess genetic diversity using molecular markers: (i)
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selection of highly informative markers at a few loci (microsatellites), (ii) sampling of
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numerous less informative markers randomly distributed within the genome (RAPD or
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AFLP). It is not yet clear that these two extreme strategies will produce similar results
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1
when used to measure within- and among-populations diversity. Most of studies that
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compare different types of markers focused on allozymes and RAPD markers: Baruffi et
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al. (1995), Cagigas et al. (1999), Isabel et al. (1995), Lannér-Herrera et al. (1996) or Le
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Corre et al. (1997) all gave allozyme and RAPD diversity data sets that do not provide
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congruent results. Still few comparative studies involve AFLP though they provide a
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high number of markers. We report here on the comparison of the levels of genetic
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diversity within and among populations of two closely related white oak species Q.
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petraea (Matt.) Liebl. and Q. robur L. using these two contrasted types of nuclear
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molecular markers: microsatellite and AFLP.
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The two species are sympatric and generally occupy different but proximal ecological
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niches. However, leaf and fruit interspecific differences are clearly recognized (Dupouey
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& Badeau 1993). Genetic variation in Q. petraea and Q. robur populations has
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previously been analysed in several studies using allozymes (Müller-Starck & Ziehe
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1991, Kremer et al. 1991, Müller-Starck et al. 1993; Kremer & Petit 1993).
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Nevertheless, due to different population sampling strategies, results were not congruent
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and could not be directly compared. Zanetto et al. (1994) showed that the level of
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diversity within Q. petraea populations is slightly higher than within Q. robur and that
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the proportion of variation partitioned among populations within species is low for both
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species. In addition, Q. petraea populations are more differentiated than those of Q.
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robur. Studies with allozymes also showed that both species share the same alleles and
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exhibit only small differences in allele frequencies, the two species exhibit extremely
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low genetic intraspecific differentiation. Analysis of total proteins confirmed results
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found with allozymes that is a low level of genetic differentiation between Q. petraea
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and Q. robur (Barreneche et al. 1996). Bodénès et al. (1997b) investigated the
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geographic variation of the species differentiation throughout their natural range. After
5
1
screening 2800 PCR amplification products using random primers, they found only two
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per cent of the amplified fragments that exhibited significant frequency differences
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between the two species and none of them was species specific. Finally, Bacilieri et al.
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(1994) and Streiff et al. (1998) studied the spatial genetic structure of the two species in
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the same oak mixed stand (“La Petite Charnie”) respectively with allozymes and
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microsatellites: only slight differences in the levels of genetic diversity were found
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between the two species.
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Within the framework of a research project supported by the European Union, seven
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mixed Q. petraea and Q. robur stands were selected in six different countries. Within
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each stand, every tree was analysed with six microsatellite markers by each laboratory
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within the project. In addition, approximately 45 samples of each species from each
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stand were screened with 155 AFLP loci by one of the laboratory (INRA). These data
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sets were used to compare the levels of diversity within and between the two species.
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The objectives of this study were twofold: (i) to compare contrasting marker systems for
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the assessment of gene diversity in oaks. The main purpose was to verify whether these
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markers would provide the same ranking when populations (within each species) were
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ordered by their level of diversity and (ii) to compare the level of diversity among
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populations (within each species) by considering the whole genome. A method was
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developed here to assess the inter-locus sampling variance and the intra-locus sampling
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variance of gene diversity and was used to compare the level of diversity between
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populations.
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Material and Methods
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Sampling of stands
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Seven stands from six countries were selected. The criteria for selection were as
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follows: (i) the stand should be mixed and comprise Q. petraea and Q. robur in
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approximately equal proportions. The stands should consist of three zones: two
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monospecific zones and one zone where the two species were mixed tree by tree, (ii) the
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stand should be of natural origin, (iii) the stand should consist of adult trees (more than
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120 years old), (iv) the population size for each species should be close to 200.
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Although these were the properties of an ideal site, it was not possible to fulfill all these
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criteria at every site. The sampling within the stand was exhaustive, since all trees on a
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given area were analysed. A standard protocol based on leaf morphology characters was
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established so that the distinction between the two species was based on the same
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criteria in the different countries (Dupouey & Badeau 1993). A principal component
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analysis (PCA) of 14 leaf characters enabled each tree to be assigned to a species. Trees
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exhibiting intermediate morphology were excluded from the analysis.
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The location of stands is given on Figure 1. The list of stands and their composition are
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given in Table 1.
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Microsatellite scoring
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Beforehand, a technical workshop was organized to standardize the methods. All trees
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in the study were genotyped using six microsatellite loci: ssrQpZAG9, ssrQpZAG36,
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ssrQpZAG104 and ssrQpZAG1/5 (Steinkellner et al. 1997), MSQ4 and MSQ13 (Dow et
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al. 1995) by each laboratory. The extraction, amplification and detection protocols for
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these microsatellites are described in Streiff et al. (1998). A test cross was further
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implemented in order to compare the scoring procedure between laboratories. Each
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participant sent 10 DNA extracts to the INRA laboratory who performed the comparison
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between his own scoring and the scoring procedure of the different participants. The test
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cross indicated five different scoring discrepancies among laboratories resulting from:
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(Error 1) a systematic shift in allele size. For example, the allele that was scored as 202
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by the INRA laboratory was actually scored as 205 by the Austrian laboratory. The
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difference in allele size was constant across the range of allele sizes.
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(Error 2) a systematic shift in allele size. However the difference in allele size was not
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constant across the range of allele sizes. The difference amounted to a certain value
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when the allele size was lower than a given threshold, and then changed above this
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threshold.
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(Error 3) a random variation of allele sizes. There were occasionally discrepancies
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between allele sizes. For example, one allele was for example scored as 203 by one of
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laboratory and 204 by the INRA one.
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(Error 4) differences in genotype identification, especially inconsistencies in
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differentiating heterozygotes and homozygotes. In some cases a tree bearing for example
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the alleles 203 and 205 was scored as heterozygote by the INRA laboratory and as
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homozygote (205-205) by another laboratory. This mainly occurred when the two alleles
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exhibited a small difference in size.
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(Error 5) a miscoring of rare, high molecular weight alleles. For a few loci, there were
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alleles of unusually extreme size corresponding to either a deletion or insertion in
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flanking regions. In general, there were small discrepancies in the assessment of the size
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of these alleles scored across the laboratories.
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AFLP scoring
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Within each stand, approximately 45 randomly selected trees of each species were
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screened in the INRA laboratory with four AFLP Primer-Enzyme Combinations
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following the protocol described in Gerber et al. (2000): PstI+CAG / MseI+CAA,
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1
PstI+CAG / MseI+GCA, PstI+CAG / MseI+GGA and PstI+CCA / MseI+CAA. The
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RFLPscan version 3.0 (Scanalytics) software was used to score the AFLP fragments.
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The STR marker (purchased by LI-COR, Biotechnology Division) was used to
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determine accurately the sizes of individual fragments.
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The four AFLP Primer-Enzyme combinations provided 155 scorable loci of which
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seventy per cent were polymorphic in at least one population (Table 2).
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Data analysis of microsatellite markers
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The following standard genetic parameters were estimated for each species and each
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stand (Brown & Weir 1983): allelic richness (A), effective number of alleles (AE=1/(1-
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HE)), observed heterozygosity (HO), expected heterozygosity (HE) and fixation index
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(FIS). In addition, the within-population gene diversity (Hi), the mean within-population
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gene diversity (HS), the total diversity (HT) and the genetic differentiation (GST) were
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calculated following Nei’s procedure (1987). Parameter estimates were made as the
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mean value across the different loci. The coefficient of gene differentiation among
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populations (GST) was computed between both species in each stand and among
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populations of each species. Those parameters were computed using the DIPLOIDE
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program (Antoine Kremer, Equipe de Génétique et Amélioration des Arbres Forestiers,
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Cestas, France).
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Because of the discrepancies with the scoring of microsatellites between the different
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laboratories the estimation of genetic diversity was performed in two different ways:
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(1st analysis)
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done separately within each stand, by using the scoring procedure developed by the
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laboratory in charge of the given stand.
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(2nd analysis)
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across sites was performed after transforming the original data by taking into account
the comparison of diversity between Q. petraea and Q. robur was
the comparison of diversity of Q. petraea/Q. robur populations
9
1
the discrepancies. The transformation of data could be done when systematic
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discrepancies were identified (systematic shift of allele size, constant or not across the
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range size of alleles). Corrections were made according to the results obtained by the
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test cross and allele sizes were shifted accordingly. Furthermore, alleles differing by one
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base pair and present in low frequencies were merged into common allelic classes in
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order to correct for random variation of allele sizes. In this case, the comparison of
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diversity was restricted only to the expected heterozygosity (HE) and to the within-
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population gene diversity (Hi) which are known to be less sensitive to small changes in
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allele frequencies than the other gene diversity statistics as the allelic richness A.
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Data analysis of AFLP markers
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The analysis of the AFLP markers was based on the assumption that each AFLP
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amplification product, regardless of its relative intensity, corresponded to a dominant
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allele at a unique locus. Polymorphic amplified loci were scored as “1” for the presence
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and “0” for the absence of a locus. Only major well-resolved amplified loci were used
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for data analysis.
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Both phenotypic and genotypic types of analysis were performed on the AFLP data set.
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Phenotypic analysis considered two types of variants: the individuals exhibiting a band
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(frequency P) and those without the band (frequency Q). P and Q were deduced directly
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from the DNA electrophoretic profiles and used to compute Hi, HS, HT, and GST at the
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phenotypic level (parameters with the same definition as described above).
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Genotypic analysis considered the frequencies, p and q, of alleles responsible for the
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presence or the absence of bands respectively. A hypothesis of genetic structure allows p
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and q to be deduced from Q: if the deficiency of heterozygotes (estimated by FIS) is
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known, then Q  q 2 (1  FIS )  qFIS . Assuming that the true value FIS is known, an
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asymptotically unbiased estimate of q is obtained by the use of a second order Taylor
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expression (Kendall & Stuart 1977):
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qˆ 
1
2
  Q(1  Q) 
 FIS


 2(1  FIS ) 2 
2(1 - FIS ) 2(1  FIS )
 N 
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(1)
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with   FIS  4(1  FIS )Q and N the number of trees sampled per population.
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Genotypic analysis was performed using the FIS value that was estimated with the
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average of the six microsatellite loci. We performed the genotypic analysis over all loci
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and we also restricted the analysis to loci that showed an observed frequency smaller
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than (1-(3/N)), where N is the population sample size, as recommended by Lynch &
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Milligan (1994). Any fragment that exhibited a higher frequency than (1-(3/N)) in a
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single population was removed from the whole data set. Lynch & Milligan (1994)
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showed that the bias introduced to the estimation of q due to a small sample size was
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substantial when the null allele was rare. Gene diversity statistics were computed by
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using the allelic frequencies as estimated by formula (1). The two analyses are
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respectively denoted G1 and G2 analysis in the following text.
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The phenotypic and genotypic analysis were performed by respectively using the
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HAPLOID and the HAPDOM programs (Antoine Kremer, Equipe de Génétique et
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Amélioration des Arbres Forestiers, Cestas, France).
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Intra- and inter-locus sampling variances
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The total sampling variance of gene diversity statistics (A, AE, HO, HE, Hi, HS, HT, FIS
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and GST) is due to a two step sampling procedure: sampling of individuals within
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populations (Vintra-locus) and sampling of loci within the genome (Vinter-locus). The total
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sampling variance is Vtotal = Vintra-locus + Vinter-locus.
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We estimated these two components by using resampling methods (bootstrap). All
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resampling procedures were done with replacement. One thousand bootstrap samples
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were made each time for estimating the sampling variances.
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Vintra-locus was estimated by resampling individuals within populations. For GST, Vintra-locus
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was estimated by resampling populations as suggested by Petit & Pons (1998).
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Vinter-locus was estimated by resampling loci across individuals.
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Statistical test of differences between populations
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The distributions of the diversity statistics estimated by bootstrapping were used to test
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for the difference between two populations a and b (a and b being the two species
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populations from the same stand or being populations of the same species from two
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different stands). For example, in the case of Hi, values of Hia and Hib were calculated
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for each bootstrap sample in each population as well as the difference of Hi between the
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two populations (Hia – Hib). The distribution of (Hia – Hib) was then compared with the
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null hypothesis (Hia – Hib = 0) and the associated probability p was calculated.
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Statistical test of AFLP allelic frequencies differences between species
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At the species level and within each stand, the frequencies of AFLP markers were
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compared between the two species by performing Fisher’s exact tests.
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Comparison of diversity statistics between markers
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The Q. petraea and Q. robur populations were ranked according to the parameters A,
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AE, HO and Hi for microsatellites and according to Hi for AFLPs. The value of these
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parameters were then compared by computing Spearman’s rank coefficient correlation:
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rS (Sokal & Rohlf 1995).
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Results
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1-Difference of gene diversity between Q. petraea and Q. robur
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1a-Microsatellites
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For every stand, the genetic diversity as measured by microsatellites was higher within
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the Q. petraea population than within Q. robur. Results shown in Table 3 were obtained
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by keeping the scoring procedure developed by the laboratory responsible for a given
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stand (1st analysis). Since the two species within a stand were scored the same way, the
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comparison of species was not affected by between laboratory discrepancies of scoring.
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There was at least one measure of diversity (among A, AE, HE, Hi and HO) per stand that
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showed higher values in Q. petraea than in Q. robur. The only site in which Q. robur
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ever demonstrated a higher measure of genetic diversity was Dalkeith Old Wood but
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this was probably due to the very low numbers of Q. petraea at this site (Table 1). The
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differences in the levels of diversity were usually small but significant when the intra-
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locus standard deviation was used. In three stands, the fixation index was significantly
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higher in Q. robur than in Q. petraea, indicating a higher excess of homozygotes in Q.
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robur than in Q. petraea in those stands.
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1b-AFLPs
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The phenotypic analysis and the G1 analysis (with all bands) based on AFLP data
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indicated a higher genetic diversity within Q. petraea for five out of the seven stands
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(Table 4). None of the results was significant when the total standard deviation was used
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to compare between the species within each stand. However, when the comparison was
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based only on the intra-locus standard deviation, the phenotypic analysis indicated
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significantly higher genetic diversity for Q. petraea in Petite Charnie, Escherode and
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Meinweg (data not shown). The G2 analysis (reducing the number of analysed bands)
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inverted results for Petite Charnie, Escherode, Dalkeith Old Wood and Meinweg but
13
1
results were not significant. The standard deviation due to sampling different loci within
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the genome was always higher than the standard deviation due to the sampling of
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individuals, whatever the adopted analysis. There was also a higher sampling variance
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associated with the G2 analysis for both components (intra and inter-locus).
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1c-Microsatellite and AFLP analysis at the species level
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When the data were pooled from the seven populations on a species basis, both
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microsatellite and AFLP markers indicated a higher genetic diversity within Q. petraea
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for all parameters and analyses (Table 5). The only exception was the G2 analysis of
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AFLPs that gave similar levels of diversity in the two species (0.252 and 0.256 for Q.
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petraea and Q. robur respectively). The genetic diversity in Q. petraea was only
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significantly higher when the intra-locus variance was considered and was never found
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to be significantly greater when the total sampling variance was used.
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2-Genetic differentiation between Q. petraea and Q. robur
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The genetic differentiation (GST) between Q. petraea and Q. robur as measured by
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microsatellites was low (Table 6a), ranging from 0.005 (Roudsea Wood) to 0.024
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(Sigmundsherberg).
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The genetic differentiation detected using AFLP markers was higher when the
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phenotypic analysis was applied as compared to the genotypic analyses (Table 6a).
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There was also an important difference between the G1 and G2 methods. In comparison
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to microsatellites, the genetic differentiation found by AFLP markers was higher, even
21
using the G2 method. Furthermore, there was no correlation between genetic
22
differentiation among markers, except between the phenotypic and the G1 analysis
23
(rS=0.883, p=0.034).
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1
3-Genetic differentiation among populations within species
2
The genetic differentiation among Q. petraea populations markers (0.023) was higher
3
than among Q. robur populations (0.020) when microsatellites were used but the
4
difference was not statistically significant (Table 6b).
5
AFLP markers did not show a significantly higher genetic differentiation among Q.
6
petraea populations. The genetic differentiation assessed using microsatellites among
7
Quercus robur populations or among Quercus petraea were not significantly different
8
from the differentiation found with AFLP with the G2 method. However, when the
9
phenotypic and G1 methods were applied to the AFLP data, the differentiation was
10
found to be significantly higher than that found using microsatellites.
11
4-Distribution curves of genetic differentiation for AFLPs
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As a higher differentiation was found using AFLP analysis compared to microsatellites,
13
the variation of GST values among loci for these markers was further analysed. The
14
distribution of GST values is given in Figure 2, 3 and 4. The three curves resemble an L-
15
shaped distribution where more extreme values occurred among populations rather than
16
between species. For example, only four loci exhibited a differentiation greater than
17
10% between Q. petraea and Q. robur populations whereas 36 loci among Q. petraea
18
populations and 23 loci among Q. robur populations demonstrated this level of
19
differentiation. At the species level, the null hypothesis was rejected for independence
20
between the observed frequencies and the species for 39% of the loci. This percentage
21
of loci ranged between 9% (Dalkeith Old Wood) and 22% (Escherode).
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5-Comparison of levels of diversity across stands
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The comparison of HE or Hi values across stands for the two species and the two
24
markers is given in Table 7. For microsatellites, differences between stands were
25
statistically significant and the overall ranking of stands was very similar when the Q.
15
1
petraea and Q. robur portions of the stands were considered independently (rS=0.769,
2
p=0.043) and the two extreme values were the same for both species. Roudsea Wood
3
exhibited the highest value of diversity and Sigmundsherberg the lowest.
4
For AFLP markers, no significant difference was detected among stands when the total
5
standard deviation was used. For each analysis, the correlation between the ranking for
6
Q. petraea and the ranking for Q. robur was positive but never significant.
7
6-Comparison of levels of diversity assessed with different measures or with different
8
markers
9
No significant correlation was found when rankings given by A and Hi for the
10
microsatellite data were compared (Table 8). For AFLP, rankings given by the G2
11
analysis for Q. petraea tended to be different from the rankings given by the two other
12
methods of analysis but the result was not significant. For Q. robur, the three analyses
13
were congruent and the correlations were significant.
14
When Hi for the microsatellite data was compared with Hi1, Hi2 or Hi3 for the AFLP
15
data, no significant correlation was found.
16
Discussion
17
Intra- and inter locus variances of gene diversities
18
Our experimental results confirm Nei’s prediction that a larger variance is attributable to
19
the effect of sampling different loci within a genome than to the sampling of individuals
20
within a population. The AFLP data demonstrate that the former source of variance can
21
be up to 15 times greater than the latter. Interestingly, both microsatellites and AFLPs
22
provided similar estimates of intra-locus variance, which suggests that the sampling
23
variance is independent of the number of alleles (Table 3 and Table 4). If differences in
24
the levels of diversity have to be assessed on the whole genome, the number of loci
25
rather than the number of individuals should be as great as possible. This is also likely
16
1
to be the case when monitoring of gene diversity is done for conservation purposes.
2
When the attributes for which diversity is assessed are unknown, diversity should be
3
measured at the whole genome level, by using a random set of markers distributed
4
throughout the genome. However, a larger sampling variance would be expected, which
5
would lead to a reduction in the power of any statistical test applied to measure diversity
6
differences among populations. This is clearly reflected in our results. There is a trend
7
towards higher genetic diversity in Q. petraea, although the difference between the two
8
species is not significant. More AFLP markers would have been necessary to reveal
9
significant differences between the two species.
10
Comparison of AFLPs and microsatellites for measuring gene diversity and
11
differentiation
12
When examining genetic diversity within the two species, the two markers show similar
13
trends and indicate that Q. petraea is more variable than Q. robur (Table 3 and Table 4).
14
Comparable results are only obtained as long as the analysis is done at the phenotypic
15
level and at the genotypic level with all the markers for AFLP data. When the analysis is
16
restricted to the subset of polymorphic markers only, following the recommendation by
17
Lynch & Milligan (1994), there is an increase in the sampling variance at the intra- and
18
inter-locus level. This increase in variance is likely to be due to the reduction in the
19
number of loci (from 155 loci to 61 loci). For comparative analysis of levels of diversity
20
between populations, it would therefore be preferable to use all markers, polymorphic
21
and monomorphic.
22
For genetic differentiation, contrasting results are obtained between microsatellite and
23
AFLP markers. In general, AFLP markers exhibit higher levels of differentiation than
24
microsatellites. There may be two explanations for these observations. First, mutation
25
rates are higher in microsatellites and cannot be ignored when compared to migration
17
1
rates. Both mutation and migration tend to decrease population differentiation (Jin &
2
Chakraborty 1995, Rousset 1996, Slatkin 1995). Second, there are more AFLP loci than
3
microsatellites and the likelihood that some of the AFLP markers are linked to
4
adaptative traits cannot be excluded. Oak populations are known to be highly
5
differentiated for growth and phenological traits (Ducousso 1996). As a result, we might
6
expect a high heterogeneity of GST values for different AFLP fragments. This is found to
7
be the case and is shown by Figures 1, 2 and 3 where the distribution of GST values
8
follows a L-shaped curve. A few markers exhibit unusually high GST values. As a result
9
the overall GST value for AFLP markers is higher than for microsatellites, most probably
10
because there is a higher likelihood that some loci are linked to an adaptive trait than for
11
microsatellites.
12
There is an important discrepancy between the GST values obtained from the two
13
methods of genotypic analyses for the AFLP data. Differentiation is much lower when
14
the analysis is restricted to polymorphic markers. This result was also demonstrated by
15
Isabel et al. (1999) who used RAPD markers and several differentiation parameters.
16
Again, unexpected effects can be induced by restricting the analysis to polymorphic
17
markers. For example, an AFLP fragment that is present and fixed in population A, but
18
absent from population B and fixed in population B, would be excluded by this method
19
of analysis. However, this fragment would have a GST value of 1.
20
Other comparative studies of different marker systems conducted in oaks provide more
21
congruent results. For example, in a genetic study on 21 populations of Q. petraea, Le
22
Corre et al. (1997) compared the level of differentiation between 31 RAPD markers and
23
8 allozyme loci and found that the levels were similar (2.7% for allozymes and 2.4% for
24
RAPDs) and comparable with the results we obtained here with microsatellites (2.3%,
25
Table 6b). The congruence between these results can again be interpreted by the
18
1
sampling effect within the genome. The low number of loci that are sampled by each of
2
this method results in preferential selection of loci that are neutral and located within the
3
tail of the L-shaped curve of GST values.
4
Comparison of levels of genetic diversity and differentiation among species
5
The results obtained here using microsatellites and AFLP markers confirm earlier
6
studies based on other markers, characters and Quercus populations. Gene diversity
7
surveys based on isozymes (Müller-Starck et al. 1993, Kremer et al. 1991, Zanetto et al.
8
1994) indicated that heterozygosity values are higher in Q. petraea than in Q. robur.
9
Data from DNA analyses conducted on both species (Moreau et al. 1994, Bodénès et al.
10
1997a) also reached similar conclusions. These differences in diversity may be related to
11
the social status of the two species. Stands of Q. petraea are usually pure and of larger
12
size than those of Q. robur that are more commonly found inter-mixed with other
13
species. Furthermore the co-evolution of the two species, the so called “regeneration” of
14
Q. petraea from successive unidirectional hybridisation with Q. robur (Petit et al. 1998)
15
can also be considered as a mechanism contributing to the enrichment of genetic
16
diversity within Q. petraea. This enrichment results from the additional diversity arising
17
following hybridisation with Q. robur and augments the diversity already existing in Q.
18
petraea per se. Lastly, results from mating system studies (Bacilieri et al. 1996) have
19
shown that the outcrossing rate is higher in Q. petraea than in Q. robur. The difference
20
in outcrossing rates is also likely to contribute as well to the observed difference in
21
fixation index (FIS).
22
Microsatellites and AFLPs show a slightly higher level of genetic differentiation among
23
populations of Q. petraea and Q. robur than between the two species. In addition, a
24
higher genetic differentiation is observed among Q. petraea populations than among Q.
19
1
robur populations but results are not significant. This observation was also true when
2
allozyme markers were used in earlier studies (Zanetto et al. 1994).
3
Comparison of levels of genetic diversity among populations of the same species
4
Despite the fact that microsatellites and AFLPs provide congruent results, although not
5
significant, in levels of diversity among the two species, they do not agree in levels of
6
intra-population genetic diversity within each species. As shown by the correlation
7
matrix of diversity statistics (Table 8), there is a positive trend among H values,
8
especially in Q. robur, but the correlation is never significant. This lack of congruence
9
may be due to a contribution of different factors. Firstly, the level of diversity may be of
10
similar magnitude in the different populations, as indicated in Table 7. Oaks live in
11
large populations and exhibit high migration rates (Streiff et al. 1999). As a result, seed
12
and pollen flow may contribute to the high homogeneity of diversity between
13
populations. Second, the diversity statistics are estimated with an important sampling
14
variance (Table 4). Again a larger number of loci would be necessary to increase the
15
power of the statistical test to compare the level of diversity among populations.
16
17
Overall, microsatellite and AFLP markers analysed in this study confirm earlier results
18
obtained for Q. petraea and Q. robur. A higher level of genetic diversity was found
19
within Q. petraea species and population in each stand. In addition, Q. petraea
20
exhibited a higher genetic differentiation than did Q. robur. However, the high inter-
21
locus variance for AFLP markers did not allow us to significantly distinguish
22
populations. It also appeared from our analysis that a restriction of loci analysed, as
23
recommended by Lynch & Milligan (1994), leads to different rankings of populations.
24
Even if the evolutionary forces are the same for the two types of markers, the lack of
20
1
information for AFLP markers and the limited number of microsatellite loci that were
2
analysed could explain the absence of significant positive correlation.
3
4
Acknowledgements
5
This project was supported by the European Union (FAIR1 PL95-0297) and by a grant
6
by BRG (Bureau des Ressources Génétiques). Ian Forrest was responsible for the
7
morphological assessment of the samples from Dalkeith Old Wood. Jan Bovenschen
8
was in charge of analysing samples from De Meinweg stand.
9
10
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18
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20
25
1
FIGURE LEGENDS
2
Figure 1: Location of stands.
3
Figure 2: Distribution curve of GST per locus between Q. petraea and Q. robur
4
populations
5
Figure 3: Distribution curve of GST per locus among Q. petraea populations
6
Figure 3: Distribution curve of GST per locus among Q. robur populations
7
In each figure, loci were numbered in order of their GST values. The dotted line
8
separated the loci showing a differentiation superior to 10% from the other loci.
9
26
1
Figure 1
Dalkeith Old Wood
Roudsea Wood
Meinweg
Escherode
Sigmundsherberg
Petite Charnie
Salinasco Mendia
2
27
1
Figure 2
155
144
133
122
111
100
89
78
67
56
45
34
23
12
0,6
0,5
0,4
0,3
G ST
0,2
0,1
0
-0,1
1
Gst between Quercus robur and Quercus petraea
Locus
2
28
1
Figure 3
Genetic differentiation among Quercus petraea
populations
0,8
0,6
0,4
Gst
0,2
151
141
131
121
111
101
91
81
71
61
51
41
31
21
-0,2
11
1
0
Locus
2
29
1
Figure 4
155
144
133
122
111
100
89
78
67
56
45
34
23
12
1
0,8
0,6
Gst 0,4
0,2
0
-0,2
1
Genetic differentiation among Quercus
robur populations
Locus
2
30
1
Table 1: List of the stands and their composition
Country
Name of the location
Number of trees
Number of trees
Number of trees of
Q. petraea
Q. robur
intermediate morphology
France
Petite Charnie
199
215
8
Germany
Escherode
110
206
5
United Kingdom
Dalkeith Old Wood
21
351
27
The Netherlands
Meinweg
181
184
15
Austria
Sigmundsherberg
228
159
8
United Kingdom
Roudsea Wood
205
56
11
Spain
Salinasco Mendia
233
45
-
2
31
1
Table 2: Polymorphism of AFLP markers
Primer-Enzyme Combination
PstI+CAG
PstI+CAG
PstI+CAG
PstI+CCA
4 PECs
MseI+CAA MseI+GCA MseI+GGA MseI+CAA
Number of analysed loci
48
34
30
43
155
Number of polymorphic loci*
33
19
23
33
108
% of polymorphic loci
69
59
77
77
70
2
A locus is polymorphic as soon as the two phenotypes (presence and absence of the
3
fragment) existed in at least one population.
4
32
1
Table 3: Microsatellites diversity statistics in Q. petraea and Q. robur populations
2
3
4
5
Petite Charnie
Q. petraea
sd 1
Q. robur
sd 1
p
0,877
0,004
0,866
0,004
Hi
0,036
0,819
0,014
0,804
0,013
0,209
HO
18,67
0,39
18.00
0,44
0,077
A
8,14
0,26
7,46
0,22
AE
0,046
0,063
0,016
0,068
0,015
0,616
FIS
Escherode
Q. petraea
sd 1
Q. robur
sd 1
p
0,878
0,005
0,835
0,004
Hi
0,001
0,884
0,011
0,832
0,010
HO
0,001
19,50
0,41
18.00
0,39
A
0,024
8,23
0,29
6,04
0,16
AE
0,001
-0,011
0,013
0.000
0,012
0,671
FIS
Dalkeith Old Wood Q. petraea
sd 1
Q. robur
sd 1
p
0,869
NC
0,868
0,002
NC
Hi
0,881
NC
0,815
0,009
NC
HO
12,67
NC
19,50
0,36
NC
A
7,63
NC
7,57
0,12
NC
AE
-0,038
NC
0,058
0,010
NC
FIS
Meinweg
Q. petraea
sd 1
Q. robur
sd 1
p
0,867
0,003
0,860
0,004
0,079
Hi
0,790
0,011
0,748
0,014
HO
0,007
18,17
0,37
17,83
0,42
0,278
A
7,50
0,18
7,12
0,20
0,108
AE
0,086
0,012
0,128
0,015
FIS
0,016
Sigmundsherberg Q. petraea
sd 1
Q. robur
sd 1
p
0,883
0,004
0,882
0,004
0,306
Hi
0,819
0,010
0,757
0,014
HO
0,001
26,67
0,44
24,67
0,51
A
0,005
8,54
0,17
8,45
0,14
0,351
AE
0,071
0,012
0,142
0,017
FIS
0,000
Roudsea Wood
Q. petraea
sd 1
Q. robur
sd 1
p
0,908
0,002
0,899
0,005
Hi
0,003
0,781
0,013
0,775
0,022
0,434
HO
26,50
0,45
19,50
0,56
A
0,004
10,85
0,24
9,90
0,43
AE
0,003
0,136
0,014
0,131
0,026
0,501
FIS
Salinasco Mendia Q. petraea
sd 1
Q. robur
sd 1
p
0,862
0,004
0,866
0,007
0,354
Hi
0,840
0,009
0,812
0,020
0,107
HO
19,33
0,34
14,50
0,55
A
0,003
7,22
0,20
7,44
0,33
0,349
AE
0,023
0,010
0,052
0,023
0,928
FIS
sd 1 is the standard deviation associated to the intra-locus variance; p values are the
associated probabilities; significant values at 5% are in bold numbers; NC: not
calculated, because bootstrap mean values differed markedly from the observed values,
indicating that the bootstrap procedure is not adequate here since the sample size is low.
33
1
Table 4: AFLPs diversity statistics in Q. petraea and Q. robur populations
Q. petraea sd 1
Petite Charnie
sd 2
0.194
0.006 0.018
Hi(P)
0.191
0.006 0.018
Hi(G1)
0.234
0.012 0.022
Hi(G2)
Q. petraea sd 1
Escherode
sd 2
(P)
0.207
0.006 0.016
Hi
0.189
0.005 0.016
Hi(G1)
(G2)
0.211
0.007 0.017
Hi
Dalkeith Old Wood Q. petraea sd 1
sd 2
0.214
NC
NC
Hi(P)
(G1)
0.204
NC
NC
Hi
(G2)
0.234
NC
NC
Hi
Q. petraea sd 1
Meinweg
sd 2
0.200
0.006 0.016
Hi(P)
0.194
0.008 0.015
Hi(G1)
0.247
0.008 0.019
Hi(G2)
Sigmundsherberg Q. petraea sd 1
sd 2
(P)
0.189
0.006 0.016
Hi
0.189
0.006 0.016
Hi(G1)
(G2)
0.262
0.010 0.020
Hi
Q. petraea sd 1
Roudsea Wood
sd 2
0.214
0.007 0.016
Hi(P)
0.202
0.007 0.015
Hi(G1)
(G2)
0.241
0.010 0.018
Hi
Salinasco Mendia Q. petraea sd 1
sd 2
(P)
0.195
0.005 0.016
Hi
(G1)
0.197
0.006 0.015
Hi
0.264
0.008 0.019
Hi(G2)
(P)
2
Hi is the phenotypic diversity; Hi(G1) is the
total sd Q. robur sd 1 sd 2 total sd
0.019
0.179
0.005 0.018 0.019
0.019
0.172
0.004 0.018 0.018
0.025
0.252
0.010 0.022 0.024
total sd Q. robur sd 1 sd 2 total sd
0.017
0.192
0.005 0.015 0.016
0.017
0.185
0.006 0.015 0.016
0.018
0.233
0.008 0.020 0.022
total sd Q. robur sd 1 sd 2 total sd
NC
0.189
0.004 0.015 0.016
NC
0.180
0.005 0.014 0.015
NC
0.236
0.006 0.017 0.018
total sd Q. robur sd 1 sd 2 total sd
0.017
0.186
0.005 0.015 0.016
0.017
0.189
0.005 0.015 0.016
0.021
0.261
0.008 0.020 0.022
total sd Q. robur sd 1 sd 2 total sd
0.017
0.196
0.006 0.015 0.016
0.017
0.199
0.008 0.015 0.017
0.022
0.262
0.008 0.020 0.022
total sd Q. robur sd 1 sd 2 total sd
0.017
0.227
0.005 0.015 0.016
0.017
0.217
0.005 0.016 0.017
0.021
0.279
0.010 0.018 0.021
total sd Q. robur sd 1 sd 2 total sd
0.017
0.194
0.006 0.015 0.016
0.016
0.189
0.005 0.015 0.016
0.021
0.260
0.008 0.018 0.020
(G2)
G1 gene diversity; Hi
is the G2 gene
3
diversity; sd 1 is the standard deviation associated to the intra-locus variance; sd 2 is the
4
standard deviation associated to the inter-locus variance; total sd is the total standard
5
deviation associated to the total variance; p is the associated probability; NC: not
6
calculated, because bootstrap mean values differed markedly from the observed values,
7
indicating that the bootstrap procedure is not adequate here since the sample size is low.
8
34
p
0.575
0.465
0.610
p
0.522
0.865
0.441
p
NC
NC
NC
p
0.549
0.834
0.646
p
0.764
0.682
1.000
p
0.582
0.535
0.184
p
0.968
0.734
0.881
1
Table 5: Microsatellites and AFLPs diversity statistics at the species level
Quercus petraea
Quercus robur
Microsatellites
sd 1
sd 1
p1
Hi
0.896
0,001
0.878
0,002
0,000
HO
0.820
0,005
0.799
0,005
0,000
A
28.83
0,461
26.83
0,492
0,000
AE
9.59
0,113
8.20
0,088
0,000
FIS
0.084
0,005
0.089
0,006
0,249
AFLPs
Hi(P)
Hi(G1)
Hi(G2)
sd 2
total sd
p1
p
sd 1
sd 2
total sd
sd 1
p2
0.228
0.002
0.015
0.015
0.220 0.002 0.014
0.014
0.001* 0.370 0.697
0.225
0.002
0.015
0.015
0.219 0.002 0.014
0.014
0.008* 0.385 0.772
0.252
0.004
0.015
0.016
0.256 0.003 0.016
0.016
0.202
0.380 0.857
2
Hi(P) is the phenotypic diversity; Hi(G1) is the G1 gene diversity; Hi(G2) is the G2 gene
3
diversity; sd 1 is the standard deviation associated to the intra-locus variance and p1 the
4
associated probability; sd 2 is the standard deviation associated to the inter-locus
5
variance and p2 the associated probability; total sd is the total standard deviation
6
associated to the total variance and p is the associated probability; * indicates a
7
significant difference between Q. petraea and Q. robur.
8
35
1
Table 6a: Genetic differentiation between species
GST (microsatellites)
Q. petraea /
Q. robur
Q. petraea / Q. robur
Petite Charnie
Q. petraea / Q. robur
Escherode
Q. petraea / Q. robur
Dalkeith Old Wood
Q. petraea / Q. robur
Meinweg
Q. petraea / Q. robur
Sigmundsherberg
Q. petraea / Q. robur
Roudsea Wood
Q. petraea / Q. robur
Salinasco Mendia
GST(P) (AFLPs) GST(G1) (AFLPs) GST(G2) (AFLPs)
0.0132
0.037
0.030
0.016
0.0181
0.068
0.053
0.038
0.0191
0.093
0.096
0.028
0.0101
0.031
0.031
-0.0003
0.0181
0.076
0.071
0.038
0.0241
0.060
0.056
0.023
0.0051
0.063
0.053
0.034
0.0151
0.051
0.040
0.021
2
3
Table 6b: Genetic differentiation among populations within species
GST (microsatellites)
Q. petraea
populations
sd
Q. robur
populations
sd
p value
GST(P) (AFLPs) GST(G1) (AFLPs) GST(G2) (AFLPs)
0.0232
0.118
0.111
0.044
0.0072
0.016
0.015
0.009
0.0202
0.114
0.111
0.030
0.0052
0.3642
0.022
0.482
0.019
0.592
0.005
0.126
4
1
1st analysis of
5
In Tables 6a and 6b, p values were obtained with bootstrap samples;
6
microsatellites; 2 2nd analysis of microsatellites; GST(P) is the phenotypic differentiation;
7
GST(G1) is the G1 genetic differentiation; GST(G2) is the G2 genetic differentiation.
8
36
1
Table 7: Comparisons of levels of diversity across stands
AFLPs (Hi(P))
AFLPs (Hi(G1))
AFLPs (Hi(G2))
Quercus petraea
0.905 A
Roudsea Wood
Dalkeith Old Wood 0.214 NC Dalkeith Old Wood 0,204 NC Salinasco Mendia 0,264
0.881
B
0.214 A
0,202 A
Escherode
Roudsea Wood
Roudsea Wood
Sigmundsherberg 0,262
0.877
B C
0.207 A
0,247
Petite Charnie
Escherode
Salinasco Mendia 0,197 A
Meinweg
0.868
C
0.200 A
0,194 A
0,241
Meinweg
Meinweg
Meinweg
Roudsea Wood
NC
0,191 A
0,234
Dalkeith Old Wood 0.866
Salinasco Mendia 0.195 A
Petite Charnie
Petite Charnie
D
0.194 A
0,189 A Dalkeith Old Wood 0,234
Salinasco Mendia 0.859
Petite Charnie
Escherode
0.853
D
0.189
A
0,211
Sigmundsherberg
Sigmundsherberg
Sigmundsherberg 0,189 A
Escherode
Quercus robur
0.898 A
0.229 A
0,217 A
0,279
Roudsea Wood
Roudsea Wood
Roudsea Wood
Roudsea Wood
0.867
B
0.196
A
0,199
A
Dalkeith Old Wood
Sigmundsherberg
Sigmundsherberg
Sigmundsherberg 0,262
B C
0,189 A
0,261
Salinasco Mendia 0.867
Salinasco Mendia 0.194 A
Meinweg
Meinweg
0.866
B C
0.192 A
Petite Charnie
Escherode
Salinasco Mendia 0,189 A
Salinasco Mendia 0,260
0.862
C
0,185 A
0,252
Meinweg
Dalkeith Old Wood 0.189 A
Escherode
Petite Charnie
0.851
D
0.186 A Dalkeith Old Wood 0,180 A Dalkeith Old Wood 0,236
Escherode
Meinweg
E
0.179 A
0,172 A
0,233
Sigmundsherberg 0.832
Petite Charnie
Petite Charnie
Escherode
(P)
(G1)
(G2)
2
Hi is the phenotypic diversity; Hi
is the G1 gene diversity; Hi
is the G2 gene diversity. For microsatellites, rankings were performed with
Microsatellites (Hi)
3
the intra-locus standard deviation. For AFLPs, rankings were based on the total standard deviation. Populations having the same letter do not
4
show a significant difference in their level of diversity.
5
37
A
A
A
A
A
NC
A
A
A
A
A
A
A
A
1
Table 8: Spearman’s rank correlation analysis among diversity values obtained with different
2
markers
Hi(P)
Hi(G1)
Quercus petraea
Hi (microsatellites)
A (microsatellites)
0.4142
Hi(P)
0.577
Hi(G1)
0.090
0.673
Hi(G2)
-0.649
-0.464
0.091
Quercus robur
Hi (microsatellites)
Hi(P)
Hi(G1)
A (microsatellites)
0.4292
Hi(P)
0.180
Hi(G1)
0.082
0.847*
Hi(G2)
0.180
0.571
0.847*
3
Each Spearman’s rank correlation is followed by the associated probability; * indicates a
4
significant positive correlation at the 5% level; Hi: microsatellites within-population diversity;
5
2
6
diversity; Hi(G2) is the G2 gene diversity.
2nd analysis of microsatellites; Hi(P) is the phenotypic diversity; Hi(G1) is the G1 gene
38