Báo cáo lâm nghiệp: "Delineation of seed zones for European beech (Fagus sylvatica L.) in the Czech Republic based on isozyme gene markers"

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Original article Delineation of seed zones for European beech (Fagus sylvatica L.) in the Czech Republic based on isozyme gene markers Vladimír Hynek Ladislav Paule b a Du&scaron;an a Gömöry a Faculty of Forestry, Technical University in Zvolen, T.G. Masaryka 24, SK-960 53 Zvolen, Slovakia b Forestry and Game Management Research Institute, CZ-156 04 Praha-Zbraslav, Czech Republic (Received 21 March 1997; accepted 2 August 1997) Abstract - Seed zones for European beech (Fagus sylvatica L.) in the Czech Republic were proposed on the basis of isozyme polymorphism. Twenty beech populations distributed over the natural range of beech in the target area were analyzed using 12 isozyme loci. Analysis of genetic distances revealed the existence of geographical differentiation patterns. Allelic fre- quencies were estimated for a square network of 300 points, covering the territory of the Czech Republic, employing kriging as an optimum spatial interpolation method. Cluster analysis based on allelic profiles of the kriging points made it possible to divide the investigated area into eight seed zones. (© Inra/Elsevier, Paris.) Fagus sylvatica / seed zones / isozymes / kriging Résumé - Définition de régions de provenances pour le hêtre européen (Fagus sylvatica République Tchèque sur la base de marqueurs isoenzymatiques. La proposition de L.) en régions de provenances en République Tchèque pour le hêtre commun (Fagus sylvatica L.) a été basée sur l’étude de son polymorphisme isoenzymatique. Pour cela, vingt populations de hêtre, réparties sur l’aire d’extension naturelle dans le territoire examiné ont été analysées pour 12 loci isoenzymatiques. L’analyse des distances génétiques a montré l’existence d’une structuration géographique. Les fréquences alléliques ont été estimées par la méthode de krigeage, méthode d’interpolation spatiale, pour un réseau quadratique de 300 points recouvrant l’ensemble du ter- ritoire tchèque. L’analyse cladistique basée sur les profils alléliques en tout point du krigeage a permis de diviser la zone examinée en huit régions de provenances (© Inra/Elsevier, Paris.) Fagus sylvatica / zone de provenance / isozymes / krigeage * Correspondence and reprints E-mail: gomory@vsld.tuzvo.sk
1. INTRODUCTION ing the areas with the highest proportion of indigenous and valuable beech popula- tions, to which no propagation material In most countries with a developed from other regions can be imported [ 17]. forestry, a concept of seed zones or prove- Allozymes have been considered nance regions is used at least for eco- unsuitable for the development of seed nomically important tree species. These zones referring to the fact that a major part terms are not equivalent, but both are of the genetic variation in allozyme loci based on the assumption that the intraspe- is allocated within, not among popula- cific genetic variation is spatially struc- tions, and that there is no agreement tured due to adaptation to the environment other mechanisms. An uncontrolled between the allozyme loci differentiation to or and the distribution patterns of morpho- transfer of seed or planting material can logical and quantitative traits found in thus lead to a substantial reduction of sur- vival and growth, and to economical provenance experiments [11]. However, several studies have proven that there are losses. clear geographical patterns in several tree Seed could therefore be defined zones species and/or loci [2, 9], indicating adap- genetically more or less homogeneous as tational mechanisms operating on these regions [16]. However, genetic informa- loci. In some cases these mechanisms were tion was usually lacking at the moment described [3]. This indicates a potential when a need for regulation of transfer of usefulness of allozymes for the definition propagation material was recognized; that of the spatial structure of genetic varia- is why seed zones were and are often tion. based on some kind of ecological classi- Unless there is a special project aimed fication. Since the variation of soil prop- the delineation of seed zones on the at erties is mostly too fine-grained to allow basis of allozyme gene markers, one of the delineation of reasonable regions, the the problems of this approach is the den- classification is mostly confined to cli- sity of the network of sample populations. matic data. When experimental data on Generally, only few populations (fre- morphological or physiological traits are quently selected and analyzed for com- available from provenance, ecophysio- pletely different goals) have been included logical or other studies, these preliminary in countrywide studies of most tree seed zones are mostly revised and new species. Even in cases when the geo- zones based on ecological as well as graphical pattern of gene frequencies is experimental data are defined [1, 27]. At clear and the populations are clustered in present, the Czech Republic is divided into well-defined groups, there may arise the 41 natural forest regions (figure 1) corre- problem of how to define the boundaries sponding to the natural geomorphologi- among individual zones. cal division of the country and defined on the basis of environmental conditions, Gene frequency can be considered a which, together with altitudinal vegeta- regionalized variable, i.e. its value depends tion zones, serve as the basis for seed on the geographical position of the sam- transfer regulation. For European beech, a pling location. Regionalized variable the- proposal of new seed zones is being pre- ory assumes that the spatial variation of pared (figure 1). The seed zones were any variable can be expressed as the sum defined on the basis of ecogeography and of three components: a structural compo- the introductory results of provenance nent, associated with a constant mean tests. Within the proposed seed zones, value or a constant trend; a random, spa- ’core regions’ were established, compris- tially correlated component; and a random
of a variable z within a neighbourhood [4]. Based on this assumption, Krige noise ues ex Clark [6]) and Matheron [18] n points. In case of ordinary (1951 V containing developed a method of the optimum inter- kriging, i.e. when no long-range trends polation, providing a best linear unbiased are present, the average of differences of z estimate of a variable at a given point. The between any two places x and x + h sepa- method is known under the name ’krig- rated by a distance vector h, is expected ing.’ Although the method was originally to be zero (E [z (x) - z (x + h)] = 0) and the developed for use in the mining industry, variance of differences depends only on it has recently found wide application in the distance between sites: (E [{z (x) - z soil, groundwater and vegetation mapping, (x + h)} 2 &gam a; (h), where the function ] 2 = as well as in human and plant genetics. y(h) is known as semivariance. If the Piazza et al. [23] provide a detailed above-mentioned conditions are fulfilled, description of the principles of this method the semivariance can be estimated from together with the application to mapping sample data as the gene frequencies in human popula- tions. In its simplest form, kriging is a method of weighted averaging of the observed val-
where is the number of pairs of sample We tried to apply this method for esti- n points separated by distance h. The value mation of allozyme gene frequencies in a of z at the point x can then be estimated as dense network of points by interpolation between analyzed populations and subse- quently to propose seed zones as geneti- cally homogeneous regions comprising points with similar allelic profiles. where &i; is the lambda weight assigned the to i-th point, and 2. MATERIALS AND METHODS For this study, 17 European beech (Fagus sylvatica L.) populations, quite regularly dis- of(x) is The minimum variance tributed the range of beech in the Czech over Republic, were used. To complete the refer- ence population network in areas where no Czech populations were sampled, one Slovak and two Polish populations from neighbour- ing regions were included. The location of the and it is obtained when analyzed populations is given in table I. Only indigenous stands (mostly gene reserves) were sampled. Twigs with dormant buds were col- lected from 50 trees chosen at random in each population. The solution of these equations provides Proteins from buds and cambium were the weights &i; [4, 23]. lambda extracted using the 0.1 M Tris-HCl buffer pH
the range and C is the ’nugget effect’). Ordi- 7.0. The electrophoretic, staining procedures 0 and zymogram interpretations followed nary punctual kriging was performed using the Thiébaut et al. [25], Merzeau et al. [20] and Geo-EAS (Geostatistical Environmental Expo- sure Assessment Software U.S. Environmental Müller-Starck and Starke [21].Eight enzyme systems coded by 12loci were examined: glu- Protection Agency, Las Vegas NV, U.S.A.) tamate-oxaloacetate transaminase (Got-2), isoc- program. The network of estimation points was a grid 27.78 km on a side (15 latitudinal min- itrate dehydrogenase (Idh), leucine aminopep- tidase (Lap-I), malate dehydrogenase (Mdh-1, utes and approximately 23 longitudinal min- utes). For loci with more than two alleles, Mdh-2, Mdh-3), menadione reductase (Mnr), allelic frequencies were subsequently adjusted peroxidase (Px-1, Px-2), phosphoglucomutase (Pgm), phosphoglucose isomerase (Pgi-2) and proportionately to the estimated values so that shikimate dehydrogenase (Skdh). The allelic their sum was 1.0. frequencies were calculated based on diploid Genetic distances between estimation points genotypes. Heterogeneity of allelic frequen- then calculated and the matrix of dis- were cies among populations and between all pairs tances was subjected to cluster analysis using of populations was tested using the likelihood the UPGMA (Unweighted pair-group meth- ratio test (G-test). To reveal the pattern of the ode using averages) clustering procedure. The genetic differentiation, genetic distances [15] resulting dendrogram was subsequently divided between populations were calculated and the on a level, providing a reasonable number of matrix of genetic distances was interpreted clusters (seed zones). The kriging standard using the principal coordinate analysis [14]. deviations summed over all alleles were used The geographical coordinates (latitude, lon- for quantification of the precision of allele fre- gitude) of individual populations were con- quency estimates, and thus also for the preci- verted to orthogonal coordinates. The point sion of classification of kriging points to indi- 15°30’ E / 50°00’ N was chosen as the origin of vidual zones. the orthogonal coordinate system. Longitudinal distortion was rectified by multiplying the hor- izontal coordinate by the coefficient, corre- 3. RESULTS sponding to 0.97987 per latitudinal degree (Z6 Líhlavník, personal communication). Var- iogram models were derived and kriging esti- frequencies in the investigated Allelic mates of gene frequencies were calculated for populations are given in table II. The each allele separately (except for biallelic loci). allelic frequencies within the whole pop- The linear model ulation set proved to be heterogeneous in only one locus (Lap-1); however, signifi- cant heterogeneities were found between several pairs of populations in all loci used most frequently 18 alleles, the - for exhibiting major polymorphisms (due to a was model exponential large number of tests, they cannot be pre- sented in a tabular form). Although a con- siderable variation of allelic frequencies in 14 cases, and the model be observed, there are no clear latitu- spherical can dinal or longitudinal clines, nor any cor- relation with altitude. More likely, the character of the genetic variation appears to be mosaic in form. The multilocus evaluation of the genetic differentiation using genetic distances pro- vided quite similar results to the single locus patterns. However, it cannot be stated that there are no differentiation pat- in two cases (in the models, γ(h) is the semi- terns observable. In figure 2, which is an variance, h is the lag distance, C is the sill, a is
of the genetic distance and subjected to cluster analysis. The interpretation concentration of points repre- matrix, resulting dendrogram(figure 4) was a senting eastern Bohemian, Silesian and divided on a level, providing a reasonable number of eight clusters. The structure of Moravian beech populations on the right the dendrogram, however, is not com- side, those representing north-west and north Bohemia on the left side and those pletely unequivocal, i.e. there are no really consistent clusters with tightly linked representing southern and central parts of objects. Another number of clusters (six or Bohemia in the centre is recognizable. three) could therefore be chosen as well. However, the groups overlap consider- Decreasing the cutting level further would ably. In addition, this figure presents only lead to a large number of excessively small the projection into the first two principal clusters. Each kriging point was classified axes, accounting together for only approx- imately 29 % of the total variation; a con- to a proposed seed zone corresponding to cluster. The seed zones are continuous siderable portion of the variation is thus one and do not overlap. Boundaries of seed not displayed there. It also must be noted zones divide the points classified to dif- that the division of the territory into the ferent clusters. eastern, northern and southern/central regions was arbitrary, demonstrating only the seed zones Figure5 presents that some patterns exist. No non-overlap- the basis of eight clusters. defined on ping clusters of points corresponding to Choosing six clusters, the regions 1, 2 and continuous regions could be identified in 3 would be amalgamated. By choosing figure 2. The delineation of seed zones three clusters, the first zone would con- can thus hardly be based on the original tain only cluster 6, i.e. Ore Mountains and samples. Firstly, the differentiation pat- the adjacent basin; the second zone would tern is ambiguous (which, to a large extent, include clusters 7 and 8, i.e. Silesian and can be attributed to sampling error). Sec- Moravian populations (except from the ondly, the sampling network is irregular, &jadnr;eskomoravská vrchovina Mountains); which does not allow any justifiable and and the third zone would be comprised of objective method for drawing the bound- the clusters 1 to 5, i.e. the rest of the terri- aries between zones. tory. The grid density indicates the kriging standard deviation (summed over all loci), Therefore, our approach was based on (a dense grid indicates high KSD, i.e. a estimation of allelic frequencies in a net- low precision of allele frequency estima- work of regularly distributed points using tion and thus also a lower probability of a optimum spatial interpola- kriging as an correct classification of kriging locations tion method. As mentioned in the Methods to individual seed regions). section, kriging estimates were derived for each allele separately, except for the 4. DISCUSSION biallelic loci. Variogram equations were thus optimized for each allele (as an exam- ple, a variogram for the Got-2/A allele is The territory of the Czech Republic is presented in figure 3). The result was a ecophysiographically quite heterogeneous, matrix of allelic frequencies for 459 points but there clear and continuous are no eco- (27 divisions in the longitudinal direction, logical gradients like the north-south gra- 17 divisions in the direction of latitude). dient in Scandinavia. This fact probably contributed considerably to the lack of Before further treatment, 159 points lying outside the territory of the Czech Repub- clear patterns of the genetic differentia- lic were excluded. For the remaining 300 tion observed in the presented material. A significant heterogeneity of allelic fre- points, genetic distances were calculated
quencies, but without unequivocal clines, ical differentiation, as found in Pinus probably results from random processes rigida [12], is more likely an exception as well as the adaptation determined by a than a rule. In European beech, an complex of environmental factors rather unequivocal spatial structure was found than by one predominating factor. The only in range-wide studies; the genetically multilocus approach, however, indicated homogeneous regions cover mostly the the existence of a spatial organization of territory of several states [10, 21]. On a the genetic variation in beech in the Czech smaller scale, the groups of genetically similar populations always overlap con- Republic. siderably in the geographical context [7, 8, From the methodological point of view, 9, 13, 26]. the best solution for the delineation of genetically homogeneous zones would be Westfall and Conkle [28] propose mul- to have a sufficiently dense network of tivariate procedures for designing the populations with large sample sizes to breeding zones on the basis of allozyme reduce the sampling error and define the markers. Their approach is based on sam- boundaries directly on the basis of the pling individual genotypes, transforming original samples. However, in addition to them to numerical scores using the pro- technical and financial demands of such cedure by Smouse and Williams [24] and an approach, even in this case the genetic subjecting the scores to multivariate anal- differentiation pattern might not corre- yses. Sampling individual trees makes a spond enough to the geographical distri- regular covering of the investigated terri- bution of populations to allow an objec- tory technically feasible. A similar tive definition of zone boundaries. A clear approach was applied by Cheliak et al. [5] clustering based on isozyme phenotypes, for Larix laricina, Merkle et al. [19] for even corresponding with the morpholog- Pseudotsuga menziesii and Yeh et al. [29]
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