The global genetic structure of the wheat pathogen Mycosphaerellagraminicola is characterized by high nuclear diversity,

low mitochondrial diversity, regular recombination, and gene flow

J. Zhan,* R.E. Pettway, and B.A. McDonald

Institute of Plant Sciences, Phytopathology Group, ETH Zentrum/LFW, Universit€aatstrasse 2, Z€uurich CH-8092, Switzerland

Received 5 March 2002; accepted 24 September 2002

Abstract

A total of 1673 Mycosphaerella graminicola strains were assayed for DNA fingerprints and restriction fragment length poly-

morphism (RFLP) markers in the nuclear and mitochondrial genomes. The isolates were collected from 17 wheat fields located in 11

countries on five continents over a six year period (1989–1995). Our results indicate that genetic diversity in the nuclear genome of

this fungus was high for all but three of the field populations surveyed and that populations sampled from different continents had

similar frequencies for the most common RFLP alleles. Hierarchical analysis revealed that more than 90% of global gene diversity

was distributed within a wheat field, while �5% of gene diversity was distributed among fields within regions and �3% was dis-

tributed among regions on different continents. These findings suggest that gene flow has occurred on a global scale. On average,

each leaf was colonized by a different nuclear genotype. In contrast, only seven mtDNA haplotypes were detected among the 1673

isolates and the two most common mtDNA haplotypes represented approximately 93% of the world population, consistent with a

selective sweep. Analysis of multilocus associations indicated that all field populations were in gametic equilibrium, suggesting that

sexual recombination is a regular occurrence globally.

� 2002 Elsevier Science (USA). All rights reserved.

Keywords: Selection; Migration; Genetic drift; Effective population size; Mating system; Evolution; RFLP; Septoria tritici

1. Introduction

The spatial genetic structure of fungal populations

results from interactions among natural selection, ge-

netic drift, and gene flow (Hartl and Clark, 1997). Se-

lection for specific ecological characters affecting

adaptation to local environments may lead to a gradualdivergence in gene frequencies among geographic pop-

ulations, causing adaptive genetic differentiation. Ge-

netic drift can cause random fixation of different alleles

in different geographic populations, leading to non-

adaptive genetic differentiation. In the absence of gene

flow among geographic populations, the accumulation

of both adaptive and non-adaptive genetic differences

will lead to spatial genetic structure.

In agricultural ecosystems, natural selection and ge-

netic drift are expected to play more important roles in

the spatial genetic structure of pathogen populations

due to selection for corresponding virulence alleles

(Martens et al., 1970; Schafer and Long, 1988; Sta-

skawicz et al., 1995) and repeating cycles of extinction

and re-colonization of local populations resulting fromhost dynamics (e.g., deployment of various resistance

genes), chemical applications (e.g., fungicides), and

changing cultural practices (e.g., burning crop stubble).

Therefore, we predict that global populations of agri-

culturally important haploid microbes, including plant

pathogenic fungi (Goodwin et al., 1993), will exhibit low

genetic variation within local populations and a high

degree of differentiation among geographic populations.Mycosphaerella graminicola (Fuckel) Schroeter (ana-

morph Septoria tritici Rob. Ex 20 Desm.) is a haploid

pathogenic fungus. This fungus has gradually emerged

as one of the more damaging foliar pathogens of wheat

Fungal Genetics and Biology 38 (2003) 286–297

www.elsevier.com/locate/yfgbi

* Corresponding author. Fax: +41-1-632-1572.

E-mail address: jiasui.zhan@ipw.agrl.ethz.ch (J. Zhan).

1087-1845/02/$ - see front matter � 2002 Elsevier Science (USA). All rights reserved.

doi:10.1016/S1087-1845(02)00538-8

in many parts of the world (e.g., Polley and Thomas,1991; Scharen, 1999). The pathogen is distributed

globally across a wide range of geographic niches (Eyal,

1999; King et al., 1983). The life cycle of this pathogen

includes both asexual and sexual reproduction. Asexual

pycnidiospores are disseminated from plant to plant via

rain-splash, hence their potential for long-distance

movement is limited (Bannon and Cooke, 1998). As-

cospores produced by the sexual stage are dispersed bywind and have the potential to be blown over a con-

siderable distance (Sanderson, 1972).

The genetic structure of M. graminicola populations

has been studied for over a decade (e.g., Boeger et al.,

1993; Chen and McDonald, 1996; McDonald et al.,

1995; Schneider et al., 2001; Zhan et al., 2001, 2002b). A

mark-release-recapture field experiment indicated that

host selection plays an important role in the populationgenetics of this pathogen (Zhan et al., 2002b), consistent

with earlier field surveys of pathogenicity (Ahmed et al.,

1996). Though the fungus is haploid and populations are

strongly affected by natural selection, previous surveys

revealed that genetic variation for this fungus was high,

with the majority of genetic variation distributed over

an area of less than 1m2 (Linde et al., 2002; McDonald

and Martinez, 1990). However, the previous results werebased on a relatively small number of populations from

North America, Switzerland, and Israel. In this study,

we combined uniform sampling methods, recent collec-

tions, global coverage, and a mixture of genetic markers

to hierarchically analyze the contemporary population

genetic structure ofM. graminicola. The main objectives

of this project were: (1) to determine the magnitude of

genetic variation in M. graminicola across many spatialscales; (2) to describe the hierarchical genetic structure

of the global population of M. graminicola; (3) to infer

the relative importance of the evolutionary forces

shaping the global population genetic structure of

M. graminicola; and (4) to infer the possible center oforigin of M. graminicola.

2. Materials and methods

2.1. Fungal strains

Wheat leaves infected with M. graminicola weresampled from 17 field sites in 11 countries, representing

many of the wheat production areas of the world. Each

leaf was sampled from a different plant or tiller. The

leaves from each location except for Texas, Syria, and

Algeria were collected from a single naturally infected

wheat field. Three sampling methods were used. For 10

of the collections, a standardized 6- or 8-site hierarchical

transect was used (McDonald et al., 1995). This sam-pling method allows us to evaluate the distribution of

genetic variation within a field. The Oregon collection

came from a replicated field experiment with individual

plot sizes of �9m2 as described previously (Boeger

et al., 1993). The Texas collection consisted of two

wheat fields sampled on the same day but located

�10 km apart (Linde et al., 2002). For the other col-

lections, infected leaves were collected at 1–2m intervalsalong a single long transect or were chosen at random

from different parts of a field. The Syrian collection

came from two fields separated by �200 km. Thirty-oneof the isolates in the Algerian collection came from a

single field planted with both durum and bread wheat,

and the remaining 21 isolates came from various loca-

tions across Algeria planted to a mixture of durum and

bread wheats. A summary of the origins and collectionstrategies for each M. graminicola population is pre-

sented in Table 1.

The infected leaf samples were air dried at room

temperature for two weeks before fungal strains were

Table 1

Mycosphaerella graminicola populations used in this study

Countries/locations Abbreviation Year(s) collected Sampling strategy Source

Algeria ALG 1992–1994 Random G.H.J. Kema

California CAL 1989 Hierarchy J. McDermott

Canada CAN 1992 Hierarchy G. Hughes

Denmark DEN 1994 Hierarchy M. Rasmussen

East Australia AUE 1992 Hierarchy B. Ballantyne

Germany GER 1992 Hierarchy R. Huang, G. Koch

Indiana IND 1993 Hierarchy G. Shaner

Israel ISR 1992 Transect O. Yarden

Mexico MEX 1993 Hierarchy L. Gilchrist

Oregon ORE 1990 Hierarchy J. Boeger, B. McDonald, M. Schmitt

Syria SYR 1995 Random G.H.J. Kema

Texas TEX 1994 Hierarchy B. McDonald, R. Chen

Uruguay URU 1993 Hierarchy M. Diaz de Ackermann

United Kingdom UK 1992 Transect M. Shaw, C. Pjils

West Australia AUW 1991 Random R. Loughman

J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297 287

isolated as described previously (Chen et al., 1994). Inthe majority of populations, only one single-spore strain

of M. graminicola was isolated from each infected leaf

that expressed a viable cirrus. But in the populations

from California, Canada, Denmark, East Australia,

Germany, Israel, Mexico, and Uruguay, there were

several cases where two to six single-spore isolations

were made from different pycnidia of the same lesion

and/or from different lesions on the same leaf.

2.2. DNA extraction, restriction digestion, and Southern

blotting

Total DNA from each strain was extracted using a

CTAB extraction protocol described previously (Mc-

Donald and Martinez, 1991a). The concentrations of the

DNA suspensions were measured using a DNA fluo-rometer (Hoefer TKO 100). Five micrograms of DNA

from each strain was digested with restriction enzymes

PstI or XhoI and the DNA fragments were separated by

electrophoresis through 0.8% agarose gels. The DNA

fragments in the agarose gels were transferred to nylon

membranes (Bio-Rad Zeta Probe) by alkaline capillary

transfer. Afterward, the membranes were dried and

stored in sealed plastic bags until needed. Thirteenanonymous RFLP probes (pSTL2, pSTL10, pSTL31,

pSTL40, pSTL53, pSTS2, pSTS14, pSTS43, pSTS192,

pSTS196, pSTS197, pSTS199, and pSTL70) originating

from a M. graminicola genomic library (McDonald and

Martinez, 1990, 1991a) were chosen to hybridize to the

nuclear DNA in these strains. Mitochondrial DNA

(mtDNA) was purified from several strains using cesium

chloride ultracentrifugation as described by Garber andYoder (1983). A cesium chloride solution with a density

of 1.6 g/ml was used for this purification. Diversity in the

mtDNA was visualized by hybridization of Southern

blots with entire, labelled mtDNA. This allowed us to

visualize the restriction pattern of the complete mtDNA

genome for each isolate.

The probes were radioactively labeled with dCT[32P]

through nick translation using the instructions of themanufacturer. The labeled probes were hybridized to the

membranes overnight at 60 �C in a hybridization incu-

bator. Following hybridization, the membranes were

washed and exposed to X-ray films at )80 �C. The ra-dioactive probes were stripped off the membranes after

the films had been developed.

2.3. Data analysis

A total of 19 RFLP loci were assayed, but only seven

of them, all based on digestion with PstI, were assayed

across all 15 populations. Hybridization to Southern

blots of chromosomes separated by pulsed field gel

electrophoresis indicated that each of these RFLP loci

was located on a different chromosome (McDonald and

Martinez, 1991b). Alleles were assigned based on themolecular weight of the restriction fragment or frag-

ments that hybridized with each probe (Fig. 1C and D).

The multilocus haplotype for each strain was formed by

joining the alleles at each RFLP locus in the same order.

MtDNA RFLP patterns based on digestion by PstI

were treated as mtDNA haplotypes (Fig. 1A). Probes

pSTL40 and pSTL70 hybridized to moderately repetitive

Fig. 1. An autoradiogram showing conservation of mtDNA types and

RFLP alleles in M. graminicola populations from around the world.

M. graminicola DNA was digested with PstI and lambda DNA was

digested with HindIII. (A) mtDNA; (B) locus pSTL10; (C) locus

pSTL31; (D) locus pSTS14; (E) locus pSTS192A.

288 J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297

DNA sequences dispersed across several chromosomes,generating profiles of hybridizing fragments that were

treated as DNA fingerprints (McDonald and Martinez,

1991b). Strains with the same DNA fingerprint, mtDNA

haplotype and/or multilocus haplotype were considered

to be individual members of the same clone, the prod-

ucts of asexual reproduction. Clones were often found

among isolates taken from the same lesion or leaf, and

among isolates taken from the same 1m2 area in a field.The clonal fraction (CF), defined here as the proportion

of fungal isolates in a sample originating from asexual

reproduction, was calculated as 1) [(number of differentgenotypes)/(total number of isolates)]. To prevent a bias

in the estimation of allele frequencies caused by repeat

sampling of the same clone in any population, clone-

corrected allele frequencies were calculated for each

RFLP locus by using only one representative from eachclone. Population genetic parameters were estimated

from clone-corrected allele frequencies using all alleles

present in the populations. Unless specifically indicated,

all population parameters were estimated using Micro-

soft Excel 97 spreadsheets.

Genetic variation in each field population was

quantified using measures of gene diversity (Nei, 1973).

Nei�s (1973) measure of population differentiation wascalculated to examine inter-population diversity among

field populations. The total gene diversity was parti-

tioned into several spatial components using hierarchi-

cal gene diversity analysis (Beckwitt and Chakraborty,

1980; Nei, 1973). Only the seven RFLP loci that were

shared across all populations were included in the hi-

erarchical analysis. All hierarchical gene diversities were

estimated using weighted gene frequencies for small andunequal sample sizes (Nei, 1973; Nei and Chesser, 1983).

Theoretically, the total genetic diversity can be parti-

tioned into all spatial scales existing in the sampling

hierarchy. But because some of our populations were

not sampled using the hierarchical method, we analyzed

our data in two separate steps. At the first step (micro-

geographical scale), total genetic diversity was parti-

tioned into three categories: within plots, among plotswithin fields, and among fields. Only field populations

sampled with the full hierarchy indicated earlier (Mc-

Donald et al., 1995) were included at this step of the

analysis. At the second step (macrogeographical level),

total genetic diversity was partitioned into three cate-

gories: within fields, among fields within regions, and

among regions. All populations were included in this

analysis, with the Texas collection treated as a singlefield with 17 sample sites, while the Syrian and Algerian

collections were treated as though they came from a

single field. Microgeographical analyses of the Texas,

Israel, and Oregon populations were described else-

where (Linde et al., 2002).

The degree of gene flow within each geographic re-

gion and among the regions was estimated with the

method described by Nei (1973). A statistical test forisolation by distance was based on the method described

by Slatkin (1993). The physical distance between pairs of

populations was based on the geographic coordinates of

locations from which the populations were sampled. The

genetic similarity among field populations was measured

using Nei�s unbiased measures of genetic identity and

genetic distance (Nei, 1978). Pairwise genetic distances

were subjected to a cluster analysis using UPGMA(unweighted pair group using arithmetic averages) and

displayed in a phenogram using the NTSYS software

package (Version 2.1, 2000, Applied Biostatistics).

Multilocus associations within each population were

evaluated using Brown et al.�s (1980) method.

2.4. Statistical tests for genetic variation

Statistical significance of differences in gene diversity

and allele numbers among regional populations were

tested by bootstrap analyses using the Resampling Stats

software package (Version 5.02, 2000; Resampling

Stats). We combined the field populations from Cali-

fornia, Canada, Denmark, Germany, Indiana, Israel,

Oregon, Syria, Texas, Uruguay, and UK into three re-

gional populations based on geographical origin. Thepopulations from Australia, North Africa (Algeria), and

Mexico were excluded from this analysis either due to

small sample sizes (Algeria) or unusual population ge-

netic structures (Australia and Mexico) described later

in the paper. For each bootstrap replication, the total

number of alleles and their frequencies were recorded

from a random sample of 200 strains (the actual size of

the smallest regional population, the Middle East) takenfrom each of the three regional populations and the gene

diversity was calculated. This procedure was repeated

100 times. The mean and variance of gene diversity and

allele number for each population was calculated and

used for a t test.

3. Results

3.1. Diversity in the nuclear genome

Genotype diversity. A total of 1673 fungal strains were

included in this survey (Table 2). Among these strains,

1327 distinct genotypes were detected. Most genotypes

were detected only once in each population. Identical

genotypes usually were found among fungal strainsoriginating from different pycnidia within the same le-

sion or from different lesions on the same leaf. Field

populations collected in Israel, Algeria, Indiana, Ger-

many, and Syria had the highest genotype diversity and

the field populations from California and Mexico had

the lowest genotype diversity. Twenty two genotypes

were found among 93 isolates originating from only 19

J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297 289

leaves in the California population. Nine multilocus

haplotypes representing 48 DNA fingerprints were

found among the 126 strains originating from 78 leaves

at the Mexican field site. In this population the most

common fingerprint was detected 20 times and the most

common multilocus haplotype was detected 68 times.

Gene diversity. Eight RFLP loci were assayed in most

populations, but only seven loci were assayed for Can-ada, West Australia, Denmark, and Germany. The total

number of alleles found in the 15 populations ranged

from seven alleles at locus pSTS192B-PstI to 33 alleles

at locus pSTL10-PstI. The average number of alleles

found across the eight shared RFLP loci was 18. The

number of private alleles found in each population

ranged from 0 to 20, with an average of 0.49 per locus

(Table 3). The average number of alleles found withinfields across eight shared RFLP loci ranged from 1.5 to

10.3 (Table 3). In general, the field populations sampled

from Israel, Oregon and Europe had high numbers of

private alleles and the field populations sampled from

North America (except Oregon), Australia and Syria

had low numbers of private alleles. Gene diversity

within the field populations ranged from 0.11 to 0.50

(Table 3). The populations from the Middle East hadthe highest overall gene diversity and the populations

from Australia had the lowest overall gene diversity

(Table 3). Bootstrap analysis revealed that the gene di-

versity of the Middle East population (mean¼ 0.493,

SD¼ 0.035) was significantly higher than the European

(mean¼ 0.394, SD¼ 0.027) and American populations

(mean¼ 0.368, SD¼ 0.022) at P ¼ 0:05. The differencebetween the latter two regional populations was not

significant. This analysis also revealed that the Middle

East population had the highest number of alleles

(mean¼ 7.98, SD¼ 0.52), European population had an

intermediate number of alleles, (mean¼ 6.58, SD¼ 0.41)

and American population had the fewest alleles

(mean¼ 5.67, SD¼ 0.36). These differences were sig-nificant at P ¼ 0:05.

3.2. Diversity in the mitochondrial genome

Only seven mitochondrial haplotypes were found

among the strains assayed (Table 2). The two most

common mtDNA haplotypes (Types 1 and 3) repre-

sented approximately 93% of the world population.Type 1 was found in all geographic regions except

Mexico and Canada. Types 4 and 6 were found only in

populations sampled from the Middle East and North

Africa. The differences among these mtDNA haplotypes

were due mainly to insertion or deletion events. The

mtDNA Types 4 and 6 contained a 3.0 kb insertion that

was not present in the other mtDNA haplotypes. When

Type 4 or Type 6 mtDNA haplotypes were hybridizedwith CsCl-purified Type 1 or 3 mtDNA, hybridization

of the 4.7 kb fragment (containing the 3.0 kb insertion)

was weaker than the hybridization with all other frag-

ments (data not shown). When Type 4 and Type 6

mtDNA strains were hybridized with CsCl purified Type

4 mtDNA, the hybridization signal was equal for all

Table 2

Number of isolates assayed, number of distinct nuclear and mitochondrial haplotypes, and frequency of mitochondrial haplotypes in each of the

M. graminicola populations, organized according to region

Populations No. of strains Haplotypes Mitochondrial types

Nuclear Mitochondrial 1 2 3 4 5 6 7

America

CAL 93 22 2 0.96 0.04 — — — — —

CAN 33 28 — — — — — — — —

IND 29 29 1 1.00 — — — — — —

MEX 126 48 1 — — 1.00 — — — —

ORE 711 654 2 0.99 0.01 — — — — —

TEX 85 72 1 1.00 — — — — — —

URU 69 41 3 0.24 0.10 0.66 — — — —

Australia

AUE 27 15 3 0.63 — 0.25 — 0.12 — —

AUW 37 23 2 0.12 — 0.88 — — — —

Europe

DEN 115 92 3 0.76 0.23 0.01 — — — —

GER 26 26 2 0.62 0.38 — — — — —

UK 70 46 3 0.69 0.27 0.02 — 0.02 — —

Middle East

ISR 169 160 3 0.49 — 0.46 — — — 0.05

SYR 31 30 4 0.40 — 0.20 0.07 0.31 — —

North Africa

ALG 52 41 6 0.31 0.04 0.23 0.35 0.02 0.06

290 J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297

fragments. This suggests that the 3.0 kb insertion in

Type 4 and Type 6 mtDNA genomes represents novel

DNA rather than a duplication of mtDNA sequences

existing in the other mtDNA types.

3.3. Hierarchical genetic structure and gene flow

Spatial distribution of genotype diversity. Genotypediversity was distributed on a fine spatial scale, except in

the East Australia and Mexico field populations. Mul-

tiple genotypes were often found among strains sampled

from different lesions of the same leaf and different py-

cnidia of the same lesion (Linde et al., 2002). In the field

populations from Canada, Denmark, Germany, Israel,

Oregon, Texas, UK, and Uruguay, an average of 13.9

genotypes were found among 16.0 (CF ¼ 0:13) strainssampled from sites covering 1–9m2 within each field. No

identical genotypes were shared between sites within

these fields. In contrast, an average of 2.0 genotypes

were found among 4.2 (CF ¼ 0:52) strains sampled fromeach site of the East Australian population, and one

identical genotype was found at two different sites

within this field. In Mexico, an average of 8.0 genotypes

were found among 21.9 (CF ¼ 0:63) strains sampledfrom each site, and four identical genotypes were found

at more than one site. Strains with identical DNA fin-

gerprints always came from the same field. No strains

with the same DNA fingerprint were found in different

field populations.

Spatial distribution of gene diversity. Field popula-

tions collected worldwide shared the most common al-

leles across RFLP loci, which often occurred at similar

frequencies (Table 4). For all RFLP loci, the three most

common alleles within a field were found in at least 90%

of the isolates from that field.The majority of gene diversity was distributed on a

small spatial scale. In the microgeographical comparison

that included 10 of the 15 populations, 79.2% of global

genetic diversity was distributed within a 1–9m2 plot

within a field, 10.8% was distributed among plots within

a field, and 10.0% was distributed among the fields when

East Australian andMexican populations were included.

When East Australian and Mexican populations wereexcluded from the analysis, 84.2% of total genetic di-

versity was distributed within a plot, 10.4% was distrib-

uted among plots within a field, and 5.4% was distributed

among the fields. In the macrogeographical comparison

that included all 15 populations, 87.8% of total genetic

diversity was distributed within fields, 9.3% was distrib-

uted among fields within regions and 2.9% was distrib-

uted among regions when East Australian, WestAustralian, and Mexican populations were included.

When the Australian and Mexican populations were

excluded, 92.3% of total genetic diversity was distributed

Table 3

Number of RFLP loci assayed, number of private alleles found, measures of gene diversity, and the average number of alleles found in field

populations of M. graminicola

Populations Shared loci All available loci

NLa NPb NAc Gene diversity NL NA Gene diversity

America 8 32 12.0 0.417 19 9.5 0.435

CAL 8 1 2.6 0.300 16 2.9 0.402

CAN 7 0 2.9 0.373 8 2.9 0.367

IND 8 0 4.1 0.439 8 4.1 0.439

MEX 8 0 1.5 0.150 8 1.5 0.150

ORE 8 20 10.3 0.400 17 9.2 0.607

TEX 8 0 3.6 0.415 8 3.6 0.415

URU 8 2 3.6 0.436 10 3.4 0.398

Australia 8 3 2.6 0.232 9 2.4 0.207

AUE 8 2 2.3 0.180 8 2.3 0.180

AUW 7 0 1.3 0.110 8 1.3 0.096

Europe 8 19 9.0 0.403 9 9.0 0.411

DEN 7 7 5.3 0.427 7 6.3 0.427

GER 7 3 3.1 0.343 8 3.8 0.394

UK 8 8 5.3 0.403 8 5.3 0.403

Middle East 8 8 8.0 0.503 10 7.9 0.457

ISR 8 6 6.8 0.502 8 6.8 0.502

SYR 8 0 3.1 0.359 10 3.0 0.322

North Africa 8 2 4.8 0.420 10 4.2 0.403

ALG 8 2 4.8 0.420 10 4.2 0.403

aNumber of RFLP loci.bNumber of private alleles.cAverage number of alleles.

J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297 291

within fields, 4.7% among fields within regions, and 3.0%

among the regions. Population subdivision within re-

gions as measured by GST ranged from 0.03 in Europe to

0.37 in Australia. Based on these GST values, the estimate

of average number of migrants exchanged among pop-

ulations within regions per generation (Nm) ranged from

0.9 in Australia to 25.7 in Europe (Table 5). GST across

all populations was 0.08 (Nm ¼ 8:8), when Australianand Mexican populations were excluded from the anal-

ysis. When all 15 populations were included in the

analysis, the global GST was 0.13 (Nm ¼ 4:9). The cor-relation coefficient between log(geographic distance) and

log(Nm) among all 15 geographic populations was

)0.39, which was significant at P < 0:001 (Fig. 2), indi-cating a significant decrease in Nm as the physical dis-

tance among populations increased. This pattern of

correlation did not change when the Australian popu-lations were excluded from the analysis. Pairwise mea-

sures of genetic identity ranged from 0.381 to 0.983 and

pairwise measures of genetic distance ranged from 0.017

to 0.966 (Table 6). Cluster analysis with UPGMA placed

populations from the Middle East, North and South

America, and Africa in the same clade, while theWestern

Australia population was closest to the European pop-

ulations (except UK) (Fig. 3).

3.4. Multilocus associations

Analyses of multilocus associations among RFLP

loci are summarized in Table 7. The hypothesis of

Table 4

Allele frequencies for eight shared RFLP loci across 15M. graminicola populations originating from America, Australia, Europe, Middle East, and

North Africa

RFLP

loci

Allele America Australia Europe Middle East N. Africa

CAL CAN IND MEX ORE TEX URU AUE AUW DEN GER UK ISR SYR ALG

SS192A 1 0.64 1.00 0.97 1.00 0.92 1.00 0.88 0.00 0.63 0.88 0.88 0.91 0.99 1.00 0.83

2 0.36 0.00 0.00 0.00 0.04 0.00 0.00 0.29 0.00 0.01 0.00 0.00 0.00 0.00 0.06

11 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.36 0.00 0.00 0.00 0.00 0.00 0.00 0.00

16 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.38 0.01 0.04 0.00 0.00 0.00 0.00

SS192b 1 1.00 1.00 0.97 1.00 0.98 0.92 1.00 1.00 1.00 0.87 0.96 1.00 0.64 1.00 1.00

2 0.00 0.00 0.03 0.00 0.02 0.08 0.00 0.00 0.00 0.13 0.04 0.00 0.33 0.00 0.00

SS14 1 0.91 0.54 0.79 1.00 0.82 0.70 0.88 0.00 — — — 0.89 0.77 1.00 0.76

2 0.09 0.43 0.21 0.00 0.17 0.30 0.05 0.00 — — — 0.00 0.21 0.00 0.18

4 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.87 — — — 0.02 0.00 0.00 0.00

SL10 1 0.91 0.48 0.66 0.77 0.68 0.76 0.46 0.88 0.82 0.67 0.94 0.64 0.64 0.53 0.77

3 0.05 0.15 0.21 0.23 0.21 0.17 0.49 0.13 0.18 0.26 0.00 0.00 0.30 0.35 0.14

5 0.00 0.37 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.00 0.00 0.02 0.00 0.00 0.03

SL53 1 0.50 0.79 0.34 0.00 0.50 0.49 0.51 1.00 1.00 0.71 0.81 0.72 0.48 0.70 0.28

2 0.18 0.14 0.28 0.66 0.13 0.14 0.32 0.00 0.00 0.07 0.12 0.02 0.06 0.15 0.33

3 0.14 0.00 0.03 0.00 0.13 0.19 0.00 0.00 0.00 0.10 0.04 0.00 0.34 0.00 0.05

5 0.09 0.04 0.07 0.34 0.03 0.07 0.00 0.00 0.00 0.00 0.00 0.00 0.07 0.00 0.00

6 0.05 0.00 0.14 0.00 0.12 0.00 0.00 0.00 0.00 0.10 0.04 0.07 0.01 0.15 0.25

SS43 1 1.00 0.32 0.34 0.98 0.65 0.46 0.59 0.00 0.00 0.51 0.30 0.50 0.35 0.48 0.62

2 0.00 0.29 0.59 0.02 0.28 0.39 0.24 0.00 1.00 0.28 0.43 0.25 0.09 0.04 0.08

3 0.00 0.29 0.03 0.00 0.07 0.13 0.00 1.00 0.00 0.09 0.22 0.09 0.28 0.04 0.11

SL31 1 0.43 0.08 0.48 0.00 0.73 0.60 0.24 0.88 1.00 0.65 0.57 0.51 0.37 0.00 0.00

2 0.33 0.08 0.00 1.00 0.15 0.20 0.22 0.06 0.00 0.11 0.29 0.12 0.04 0.29 0.18

3 0.24 0.00 0.03 0.00 0.04 0.03 0.10 0.06 0.00 0.01 0.00 0.02 0.08 0.00 0.00

5 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.04 0.00 0.05 0.03 0.12 0.47

6 0.00 0.77 0.07 0.00 0.01 0.06 0.27 0.00 0.00 0.08 0.00 0.00 0.33 0.06 0.18

SS2 1 0.86 — 0.66 0.77 0.65 0.70 0.51 1.00 1.00 0.76 0.71 0.66 0.69 0.81 0.86

2 0.09 — 0.10 0.23 0.09 0.19 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00

3 0.05 — 0.14 0.00 0.21 0.12 0.49 0.00 0.00 0.17 0.29 0.30 0.11 0.13 0.11

Alleles with frequencies lower than 0.25 across all 15 populations were not listed.

Table 5

Measures of population subdivision and gene flow in the M. gra-

minicola populations collected from America, Australia, Europe, and

Mediterranean basin (including both Northern Africa and Middle East

populations) regions

Regions GST Nm

Global 0.08 8.84

America 0.08 10.56

Australia 0.37 0.88

Europe 0.03 25.69

Mediterranean basin 0.05 15.67

292 J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297

gametic equilibrium was not rejected in any of the 15populations analyzed.

4. Discussion

4.1. Diversity in the nuclear genome

Our results indicate that a high level of diversity inthe nuclear genome is typical for field populations of

M. graminicola. In a fungus utilizing both sexual and

asexual reproduction, it is useful to differentiate between

gene diversity, based on the numbers and frequencies of

alleles at individual loci, and genotype diversity, based

on the numbers and frequencies of genetically distinct

individuals in a population. In terms of gene diversity,

18 alleles were detected, on average, across the eight

Table 6

Pairwise comparisons of Nei�s (1978) genetic identity (above diagonal) and genetic distance (below diagonal) between the 15 M. graminicola

populations

ALG CAL CAN DEN AUE GER IND ISR MEX ORE SYR TEX UK URU AUW

ALG **** 0.911 0.759 0.814 0.542 0.796 0.893 0.877 0.874 0.896 0.934 0.894 0.888 0.894 0.635

CAL 0.093 **** 0.693 0.836 0.611 0.811 0.867 0.859 0.879 0.938 0.889 0.912 0.912 0.876 0.670

CAN 0.275 0.366 **** 0.739 0.486 0.741 0.794 0.814 0.634 0.779 0.783 0.805 0.789 0.825 0.615

DEN 0.205 0.180 0.303 **** 0.713 0.969 0.869 0.858 0.686 0.908 0.818 0.915 0.889 0.833 0.888

AUE 0.613 0.492 0.722 0.338 **** 0.742 0.563 0.596 0.381 0.613 0.538 0.627 0.633 0.506 0.725

GER 0.228 0.210 0.299 0.032 0.299 **** 0.860 0.817 0.680 0.877 0.795 0.905 0.886 0.801 0.907

IND 0.114 0.143 0.230 0.140 0.574 0.151 **** 0.902 0.794 0.961 0.891 0.975 0.938 0.921 0.803

ISR 0.131 0.152 0.205 0.154 0.518 0.202 0.103 **** 0.759 0.921 0.901 0.941 0.902 0.888 0.700

MEX 0.135 0.129 0.456 0.376 0.966 0.386 0.230 0.276 **** 0.82 0.85 0.819 0.78 0.839 0.474

ORE 0.110 0.064 0.250 0.096 0.489 0.131 0.040 0.082 0.198 **** 0.906 0.983 0.969 0.935 0.78

SYR 0.068 0.118 0.245 0.202 0.619 0.229 0.116 0.104 0.163 0.099 **** 0.894 0.929 0.935 0.657

TEX 0.113 0.092 0.216 0.089 0.467 0.099 0.026 0.061 0.200 0.017 0.112 **** 0.952 0.91 0.813

UK 0.119 0.092 0.237 0.118 0.457 0.121 0.064 0.104 0.249 0.032 0.074 0.049 **** 0.928 0.780

URU 0.112 0.133 0.193 0.183 0.682 0.222 0.082 0.118 0.175 0.067 0.067 0.094 0.075 **** 0.669

AUW 0.454 0.400 0.486 0.119 0.321 0.098 0.220 0.357 0.746 0.248 0.419 0.207 0.248 0.401 ****

Fig. 3. UPGMA phenogram of genetic distances among 15 geographic populations of M. graminicola.

Fig. 2. Relationship between the logarithm of geographical distance

and the logarithm of estimated number of immigrants per generation

for M. graminicola populations collected from 15 locations.

J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297 293

RFLP loci that were shared by most populations. In

terms of genotype diversity, isolates from different leaves

had different genotypes, as defined by DNA fingerprints

and multilocus RFLP haplotypes, in most field popu-

lations. The California field population had low geno-

type diversity compared to the other field populations,

but this probably reflects the large number of isolates

sampled per leaf in this population compared to otherpopulations. Twenty-two genotypes were found among

93 isolates originating from 19 leaves sampled from

this field.

We propose several mechanisms that can contribute

to the high nuclear gene and genotype diversity observed

inM. graminicola. One mechanism is regional gene flow.

Regional populations of M. graminicola (with the ex-

ception of Australia and Mexico) appear to be linked bya high degree of gene flow (Boeger et al., 1993; Mc-

Donald et al., 1999). Recurring gene flow among spa-

tially isolated populations increases gene variation in

local populations as a result of the influx of new alleles

from neighboring populations. A second contributing

factor is frequent sexual recombination (Chen and Mc-

Donald, 1996; Zhan et al., 1998, 2000). The level of

genetic variation in natural populations is positivelycorrelated with the rate of sexual recombination (e.g.,

Kraft et al., 1998). Sexual reproduction increases geno-

type diversity by creating novel recombinants and by

decreasing the effects of hitchhiking or background

selection on linked deleterious alleles at other loci

(Braverman et al., 1995; Nordborg et al., 1996). Thoughthe greatest impact of sexual reproduction will be on

genotype diversity, sexual recombination may also in-

crease gene diversity through intragenic recombination

that can create new alleles, such as novel combinations

of restriction sites for RFLP loci. A third factor that

maintains high gene diversity is the large effective size of

these populations. Based on our most conservative es-

timate, a population inhabiting an area of 50m2 in awheat field has an effective size of at least 3000 indi-

viduals (Zhan et al., 2001). Populations with large ef-

fective size tend to have higher gene diversity, as more

alleles can emerge through mutation and fewer alleles

will be lost due to random genetic drift (Kimura, 1983).

The M. graminicola populations originating from

Australia and Mexico had much lower gene diversity

compared to the other populations in this study. Thesewere the only populations that had fixed alleles at sev-

eral RFLP loci. Because the number of isolates origi-

nating from Australia was relatively small, it is possible

that our findings reflect sampling error, but we consider

it more likely that they reflect random genetic drift. We

hypothesize that the low degree of gene diversity in

Australian populations is due to a combination of re-

cent founder events, recurring bottlenecks, geographicalisolation, and effective quarantine measures. The in-

troduction of wheat cultivation to the Australian con-

tinent was relatively recent (�200 years ago), coincidingwith the colonization of Australia by the British starting

in �1780. It is possible that only a fraction of the globalgenetic diversity existing for M. graminicola came into

Australia during the introduction of wheat as most of

the original wheat seed and grain was probably im-ported from England. This hypothesis is consistent with

the occurrence of the rare Type 5 mtDNA haplotype in

both UK and East Australia populations. The genetic

variation of Australian populations is likely to have

been restricted further by bottlenecks resulting from

periodic droughts affecting a large fraction of the

Australian wheat acreage. In dry years, overall levels of

infection are reduced because asexual spores are spreadbetween plants by rain-splash and moisture is needed

for infection. As a result, we expect that genetic drift is

more likely to occur in dry years as a result of lower

pathogen population sizes. A combination of random

drift and geographical isolation can also explain the

significant differences between East and West Austra-

lian populations (Tables 4 and 5). These two popula-

tions are separated by the Nullabor desert, whichpresents a formidable barrier to movement of ascosp-

ores between wheat growing areas in East and West

Australia.

The Mexican population sample originated from a

CIMMYT disease nursery where wheat breeding lines

and varieties are screened for resistance to M. gramini-

cola. This was the only field population where we ob-

Table 7

Multilocus associations (Brown et al., 1980) among RFLP loci in 15

M. graminicola populations collected from around the world

Populations NLa S2kb Lc

America

CAL 11 2.8435 2.9490

CAN 8 1.8339 2.0433

IND 8 1.6388 2.2107

MEX 8 0.7828 0.9786

ORE 6 1.0914 1.2093

TEX 8 1.7878 2.0529

URU 10 2.1590 2.5097

Australia

AUE 8 0.9180 1.1954

AUW 6 0.2401 0.2813

Europe

DEN 7 1.6387 2.0320

GER 8 1.8792 2.2182

UK 8 1.8743 2.1199

Middle East

ISR 8 1.7571 1.9569

SYR 4 0.5799 0.8351

North Africa

ALG 8 2.2284 2.4484

aNumber of RFLP loci.bObserved variance of the number of heterozygous comparisons.cUpper 95% confidence limit.

294 J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297

served several clones distributed over spatial scales ofgreater than 1–2m. The two most common multilocus

haplotypes made up 71% (90/126) of the population and

the five most common DNA fingerprints made up 44%

(56/126) of the population. Many of the isolates that had

the same multilocus haplotype differed by only one or

two bands in their pSTL70 DNA fingerprints, suggesting

that these differences may result from a transposition

event within a single clonal lineage (Goodwin et al.,2001). Though the genetic diversity in this collection was

very low, the hypothesis of random mating was not re-

jected in the clone-corrected sample (Table 7). This CI-

MMYT disease nursery was located in Patzcuaro, a

remote mountainous area where wheat is not widely

grown. The closest intensive wheat production area is

several hundred kilometers distant (L. Gilchrist, personal

communication). We hypothesized that a few strainswere introduced into this field by deliberate inoculation

after the disease nursery was established (McDonald

et al., 1999) and this hypothesis was confirmed by Eyal

(1999). This population has all of the characteristics of a

founder population that has remained isolated from

other populations in this region, probably as a result of

geographical isolation. We found both mating types

present at roughly equal frequency in this population(Zhan et al., 2002a) and hypothesize that new genotypes

have emerged as a result of sexual recombination among

a limited number of founder isolates.

4.2. Diversity in the mitochondrial genome

The mitochondrial genome of this fungus had much

lower genetic variation than its nuclear counterpart.Only seven mitochondrial haplotypes were detected

among the strains surveyed. The two most common

haplotypes (Types 1 and 3) were found in more than

90% of the isolates. In the Oregon field population, 654

nuclear genotypes were found among 711 strains, but

only two mtDNA haplotypes were found. Type 1

mtDNA was found in 706 of the strains, and five strains

had Type 2 mtDNA.We consider two hypotheses to explain the low

diversity in the mitochondrial genome. The lower

variation in the mtDNA could result from a lower

mutation rate in the mitochondrial genome. It has

been observed that the base-substitution rate in yeast

mitochondrial genes is lower than in nuclear genes

(Clark-Walker, 1991; Fournier et al., 1990; Saliola

et al., 1990). Clark-Walker (1992) hypothesized thatother fungal species might share this pattern of genetic

variation. An alternative hypothesis is that the low

mitochondrial diversity reflects a selective sweep that

occurred during the last century, perhaps as a result

of the rapid replacement of old varieties by dwarf

wheat varieties during the green revolution in the

1960s and 1970s.

4.3. Spatial structure and evidence for gene flow

Hierarchical analyses revealed that the majority

(�80%) of global genetic diversity was distributed withinfield sites measuring approximately 1–9m2. The finding

that all field populations had the majority of genetic

diversity distributed on a small spatial scale suggests

that populations of this fungus possess a significant

potential to evolve rapidly in response to changing en-vironments, including the deployment of new resistance

genes or fungicides. This analysis also revealed that

�12% of diversity was distributed among sites within a

field, while only �5% of total genetic diversity was dis-

tributed among fields within a region and �3% was

distributed among different continents. The finding of

relatively little population differentiation among fungal

populations collected from four continents was unex-pected because we thought that geographical isolation

would lead to genetic differentiation. It was shown

previously that populations of M. graminicola become

adapted to local wheat populations (Ahmed et al.,

1996), which also could lead to regional subdivision.

One hypothesis to explain the low degree of population

subdivision globally is that M. graminicola populations

in different locations (except Australia and Patzcuaro,Mexico) are linked through gene flow. If we exclude the

Australian and Mexican populations, and use the as-

sumptions of Wright�s island model (Wright, 1951), the

observed GST values would require exchange of ap-

proximately nine individuals among populations on

different continents each generation, and exchange of

�10–26 individuals among the regional populations

each generation. Rates of gene flow estimated fromWright�s F-statistics reflect the long-term effects of mi-

gration among semi-isolated populations (Slatkin, 1987)

and cannot be used to determine the time scale over

which gene flow occurred. Thus we cannot determine

from this analysis whether the current lack of popula-

tion differentiation among these populations represents

historical movement of gametes and/or genotypes from

hundreds of years ago or whether migration is a signif-icant unifying force at the present time. However, the

time scale of gene flow among populations could be

resolved by analyzing the relative ages and historical

relationships of alleles among geographic populations

on the basis of DNA sequences of non-recombining

regions (Carbone and Kohn, 2001; Hare, 2001).

4.4. Evidence for sexual reproduction

The high levels of genotype diversity found in all

populations except Mexico coupled with the random

associations among RFLP loci found in all populations

suggest that sexual recombination plays a significant

role in shaping the population genetic structure of this

fungus. This hypothesis is consistent with previous

J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297 295

findings thatM. graminicola forms sexual fruiting bodiesand undergoes recombination both during and between

growing seasons (Hunter et al., 1999; Shaw and Royle,

1989; Zhan et al., 1998, 2000). The sexual offspring

(ascospores) serve as primary inoculum that initiates an

epidemic of S. tritici leaf blotch (Hunter et al., 1999;

Shaw and Royle, 1989), and also contributes to sec-

ondary infections on the upper leaves during the grow-

ing season (Zhan et al., 1998, 2000). Zhan et al. (1998,2000) found that nearly 25% of the M. graminicola

population collected from flag leaves originated from

sexual reproduction during a four-month period in one

growing season.

Our recent survey of the Mat locus (Zhan et al.,

2002a) showed that the two mating types coexist at

approximately equal frequency at all spatial scales tes-

ted, from lesions to continents. The even distribution ofthe two mating types across all spatial scales maximizes

the probability that sexually compatible strains will meet

and have the opportunity to recombine. This may con-

tribute to the high rate of sexual reproduction observed

in populations of this fungus.

4.5. Center of origin

The Fertile Crescent, including portions of present-

day Syria and Israel, is the center of origin for cultivated

wheat (Harlan and Zohary, 1966; Nevo and Beiles,

1989) and we believe that it also is the most likely center

of origin for M. graminicola. We present two lines of

evidence to support this hypothesis. First, fungal pop-

ulations from this region displayed the highest mito-

chondrial diversity. Five of the seven mtDNAhaplotypes were identified in the collections from Syria

and Israel and Type 7 mtDNA was found only in this

region. Second, the fungal populations from this region

had the highest diversity in the nuclear genome. The

average gene diversity across the shared loci for the

populations from the Middle East was 0.503 as com-

pared to 0.417, 0.232, 0.420, and 0.403, respectively, for

the populations from America, Australia, North Africa(Algeria), and Europe. A bootstrap analysis showed that

populations sampled from the Middle East had signifi-

cantly higher gene diversity and greater numbers of al-

leles than populations in America and Europe.

Acknowledgments

This project was funded by the USDA National

Research Initiative Competitive Grants Program (#93-

37304-9039), the National Science Foundation, (#DEB-

9306377), and the Swiss Federal Institute of Technology

(ETH), Zurich. We thank our colleagues listed in Table

1 for making the collections of leaf samples used in this

study. And we recognize the dedicated effort of many

undergraduate students at Texas A&M University whocollected much of this data under the supervision of

R.E. Pettway.

References

Ahmed, H.U., Mundt, C.C., Hoffer, M.E., Coakley, S.M., 1996.

Selective influence of wheat cultivars on pathogenicity of Mycosp-

haerella graminicola (anamorph Septoria tritici). Phytopathology

86, 454–458.

Bannon, F.J., Cooke, B.M., 1998. Studies on dispersal of Septoria

tritici pycnidiospores in wheat-clover intercrops. Plant Pathol. 47,

49–56.

Beckwitt, R., Chakraborty, R., 1980. Genetic structure of Pileolaria

pseudomilitaris (Polychaeta: Spirorbidae). Genetics 96, 711–726.

Boeger, J.M., Chen, R.S., McDonald, B.A., 1993. Gene flow between

geographic populations of Mycosphaerella graminicola (anamorph

Septoria tritici) detected with RFLP markers. Phytopathology 83,

1148–1154.

Braverman, J.M., Hudson, R.R., Kaplan, N.L., Langley, C.H.,

Stephan, W., 1995. The hitchhiking effect on the site frequency-

spectrum of DNA polymorphisms. Genetics 140, 783–796.

Brown, A.H.D., Feldman, M.W., Nevo, E., 1980. Multilocus structure

of natural populations of Hordeum spontaneum. Genetics 96,

523–536.

Carbone, I., Kohn, L.M., 2001. A microbial population-species

interface: nested cladistic and coalescent inference with multilocus

data. Mol. Ecol. 10, 947–964.

Chen, R.S., Boeger, J.M., McDonald, B.A., 1994. Genetic stability in a

population of a plant pathogenic fungus overtime. Mol. Ecol. 3,

209–218.

Chen, R.S., McDonald, B.A., 1996. Sexual reproduction plays a major

role in the genetic structure of populations of the fungus Mycosp-

haerella graminicola. Genetics 142, 1119–1127.

Clark-Walker, G.D., 1991. Contrasting mutation rates in mitochon-

drial and nuclear genes of yeast. Curr. Genet. 20, 195–198.

Clark-Walker, G.D., 1992. Evolution of mitochondrial genomes in

fungi. Int. Rev. Cytol. 141, 89–127.

Eyal, Z., 1999. The Septoria/Stagonospora blotch diseases of wheat:

past, present, and future. In: van Ginkel, M., McNab, A.,

Krupinsky, J. (Eds.), Septoria and Stagonospora Diseases of

Cereals: A Compilation of Global Research, CIMMYT, Mexico.

Eur. J. Plant Pathol. 105, 629–641.

Fournier, A., Fleer, R., Yeh, P., Mayaux, J.F., 1990. The primary

structure of the 3-phosphoglycerate kinase (PGK) gene from

Kluyveromyces lactis. Nucleic Acids Res. 18, 365.

Garber, R.C., Yoder, O.C., 1983. Isolation of DNA from filamentous

fungi and separation into nuclear, mitochondrial, ribosomal, and

plasmid components. Anal. Biochem. 135, 416–422.

Goodwin, S.B., Cavaletto, J.R., Waalwijk, C., Kema, G.H.J., 2001.

A DNA fingerprint probe from Mycosphaerella graminicola

identifies an active transposable element. Phytopathology 91,

1181–1188.

Goodwin, S.B., Saghai Maroof, M.A., Allard, R.W., Webster, R.K.,

1993. Isozyme variation within and among populations of Rhyn-

chosporium secalis in Europe, Australia and the United States.

Mycol. Res. 97, 49–58.

Hare, M.P., 2001. Prospects for nuclear gene phylogeography. Trends

Ecol. Evol. 12, 700–706.

Harlan, J.R., Zohary, D., 1966. Distribution of wild Emmer wheat and

barley. Science 153, 1074–1080.

Hartl, D.L., Clark, A.G., 1997. Principles of Population Genetics,

third ed. Sinauer Associates, Inc, Sunderland.

296 J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297

Hunter, T., Coker, R.R., Royle, D.J., 1999. The teleomorph stage,

Mycosphaerella graminicola, in epidemics of Septoria tritici blotch

on winter wheat in the UK. Plant Pathol. 48, 51–57.

Kimura, M., 1983. The Neutral Theory of Molecular Evolution.

Cambridge University Press, Cambridge, UK.

King, J.E., Cook, R.J., Melville, S.C., 1983. A review of Septoria

diseases of wheat and barley. Ann. Appl. Biol. 103, 345–373.

Kraft, T., Sall, T., Magnusson-Rading, I., Nilsson, N.O., Hallden, C.,

1998. Positive correlation between recombination rates and levels

of genetic variation in natural populations of sea beet (Beta vulgaris

subsp. maritima). Genetics 150, 1239–1244.

Linde, C., Zhan, J., McDonald, B.A., 2002. Population structure of

Mycosphaerella graminicola: from lesions to continents. Phytopa-

thology 92, 946–955.

Martens, J.W., McKenzie, R.I.H., Green, G.J., 1970. Gene-for-gene

relationships in the Avena Puccinia graminis host–parasite system

in Canada. Can. J. Bot. 48, 969–975.

McDonald, B.A., Martinez, J.P., 1990. Restriction fragment length

polymorphisms in Septoria tritici occur at a high frequency. Curr.

Genet. 17, 133–138.

McDonald, B.A., Martinez, J.P., 1991a. DNA fingerprinting of the

plant pathogenic fungus Mycosphaerella graminicola (anamorph

Septoria tritici). Exp. Mycol. 15, 146–158.

McDonald, B.A., Martinez, J.P., 1991b. Chromosome length polymor-

phisms in a Septoria tritici population. Curr. Genet. 19, 265–271.

McDonald, B.A., Pettway, R.E., Chen, R.S., Boeger, J.M., Martinez,

J.P., 1995. The population genetics of Septoria tritici (teleomorph

Mycosphaerella graminicola). Can. J. Bot. 73 (suppl. 1), s292–

s301.

McDonald, B.A., Zhan, J., Yarden, O., Hogan, K., Garton, J.,

Pettway, R.E., 1999. The population genetics of Mycosphaerella

graminicola and Phaeosphaeria nodorum. In: Lucas, J.A., Bowyer,

P., Anderson, H.M. (Eds.), Septoria on Cereals: A Study

of Pathosystems. CAB International, Wallingford, UK, pp. 44–69.

Nei, M., 1973. Analysis of gene diversity in subdivided populations.

Proc. Natl. Acad. Sci. USA 70, 3321–3323.

Nei, M., 1978. Estimation of average heterozygosity and genetic

distance from a small number of individuals. Genetics 89, 583–590.

Nei, M., Chesser, R.K., 1983. Estimation of fixation indices and gene

diversities. Ann. Hum.Genet. 47, 253–259.

Nevo, E., Beiles, A., 1989. Genetic diversity of wild emmer wheat in

Israel and Turkey. Theor. Appl. Genet. 77, 421–455.

Nordborg, M., Charlesworth, B., Charlesworth, D., 1996. The effect of

recombination on background selection. Genet. Res. 67, 159–174.

Polley, R.W., Thomas, M.R., 1991. Surveys of diseases of winter-wheat

in England and Wales, 1976–1988. Ann. Appl. Biol. 119, 1–20.

Saliola, M., Schuster, J.R., Falcone, C., 1990. The alcohol dehydro-

genase system in the Kluyveromyces lactis. Yeast 6, 193–204.

Sanderson, F.R., 1972. Mycosphaerella graminicola (Fuckel) Sander-

son comb, nov., the ascogenous state of Septoria tritici Rob. and

Desm. N. Z. J. Bot. 14, 359–360.

Schafer, J.F., Long, D.L., 1988. Relations of races and virulences of

Puccinia recondita f. sp. tritici to wheat cultivars and areas. Plant

Dis. 72, 25–27.

Scharen, A.L., 1999. Biology of Septoria/Stagonospora pathogens: an

overview. In: van Ginkel, M., McNab, A., Krupinsky, J. (Eds.),

Sepotoria and Stagonospora Diseases of Cereals: A Compilation of

Global Research. CIMMYT, Mexico.

Schneider, F., Koch, G., Jung, C., Verreet, J.A., 2001. Genotypic

diversity of the wheat leaf blotch pathogen Mycosphaerella

graminicola (anamorph) Septoria tritici in Germany. Eur. J. Plant

Pathol. 107, 285–290.

Shaw, M.W., Royle, D.J., 1989. Airborne inoculum as a major source

of Septoria tritici (Mycosphaerella graminicola) infections in winter

wheat crops in the UK. Plant Pathol. 38, 35–43.

Slatkin, M., 1987. Gene flow and the geographic structure of natural

populations. Science 236, 787–792.

Slatkin, M., 1993. Isolation by distance in equilibrium and non-

equilibrium populations. Evolution 47, 264–279.

Staskawicz, B.J., Ausubel, F.M., Baker, B.J., Ellis, J.G., Jones, J.D.G.,

1995. Molecular genetics of plant disease resistance. Science 268,

661–667.

Wright, S., 1951. The genetical structure of populations. Ann. Eugen.

15, 323–354.

Zhan, J., Kema, G.H.J., Waalwijk, C., McDonald, B.A., 2002a.

Distribution of mating type alleles in the wheat pathogen

Mycosphaerella graminicola over spatial scales from lesions to

continents. Fungal Genet. Biol. 36, 128–136.

Zhan, J., Mundt, C.C., Hoffer, M.E., McDonald, B.A., 2002b. Local

adaptation and effect of host genotype on the evolution of

virulence: an experimental test in a plant pathosystem. J. Evol.

Biol. 15, 634–647.

Zhan, J., Mundt, C.C., McDonald, B.A., 1998. Measuring immigra-

tion and sexual reproduction in field populations of Mycosphae-

rella graminicola. Phytopathology 88, 1330–1337.

Zhan, J., Mundt, C.C., McDonald, B.A., 2000. Estimating rates of

recombination and migration in populations of plant pathogens—a

reply. Phytopathology 90, 324–326.

Zhan, J., Mundt, C.C., McDonald, B.A., 2001. Using RFLPs to assess

temporal variation and estimate the number of ascospores that

initiate epidemics in field populations of Mycosphaerella gramini-

cola. Phytopathology 91, 1011–1017.

J. Zhan et al. / Fungal Genetics and Biology 38 (2003) 286–297 297