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dissertation entitled

EPIDEMIOLOGY AND ETIOLOGY OF EUTYPA DIEBACK OF
GRAPEVINE AND PARTIAL CHARACTERIZATION OF
EUTYPELLA VITIS, A NEW PATHOGEN OF GRAPEVINE

presented by

Stephen Andrew Jordan

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5/08 K lPrq/Acc8Pres/ClRC/Date0ue indd

 

EPIDEMIOLOGY AND ETIOLOGY OF EUTYPA DIEBACK OF GRAPEVINE AND
PARTIAL CHARACTERIZATION OF EUTYPELLA VITIS, A NEW PATHOGEN OF
GRAPEVINE
By

Stephen Andrew Jordan

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Department of Plant Pathology

2008

 

ABSTRACT
EPIDEMIOLOGY AND ETIOLOGY OF EUTYPA DIEBACK OF GRAPEVINE AND
PARTIAL CHARACTERIZATION OF EUTYPELLA VITIS, A NEW PATHOGEN OF
GRAPEVINE
By

Stephen Andrew Jordan

Eutypa dieback, caused by the ascomycetous fungus Eutypa lata, affects grapevines
(Vitis spp.) worldwide and limits the longevity of Vitis labrusca ‘Concord’ vineyards in
Michigan. Information from the spatial pattern of infected plants can help shape
management strategies by improving our understanding of disease spread within and
among fields. The spatiotemporal relationship of vines showing Eutypa dieback
symptoms in eight MI vineyards was studied using ordinary runs analysis, spatial
association by distance indices (SADIE), semivariance and Moran’s I. Symptomatic
vines were aggregated in the east-west direction for 7 of the 8 vineyards, suggesting
spread in the direction of prevailing wind and 4 of the 8 vineyards had significant
aggregation overall. Results from the SADIE association test indicate that inoculum was
coming from both within and outside vineyards.

The effects of Eutypa dieback on the yield of ‘Concord’ grapevines in M1 were
studied and crop loss models were developed. Data from selected vines (n=42l) were
collected from for 3 years in three commercial Vineyards with natural Eutypa infections.
There was a direct correlation between disease severity and yield. Models were
developed using one of two measures of disease severity, the percent symptomatic shoots

(R2=0.67) and a disease severity scale (R2=0.66).

While Eutypa lata is considered to be the primary cause of Eutypa dieback, a second
fungus, Eutypella vitis, has been frequently isolated from grapevines in Michigan with
Eutypa dieback symptoms. The pathogenicity of El. vitis was confirmed by production of
vascular necrosis in the wood, and El. vitis was successfully re-isolated from necrotic
tissue. PCR primers were designed for E. lata and El. vitis, and a nested multiplex PCR
protocol was developed that successfully detected E. lata and El. vitis in wood samples
from naturally infected Vines as well as artificially inoculated potted ‘Concord’ canes.

The role of phytotoxins in Eutypa dieback disease development is unknown. To
fiirther elucidate the role of phytotoxins as either pathogenicity or virulence factors,
putative toxin-deficient mutants were generated. Eutypa lata isolate E30, a highly
virulent, phytotoxin-producing isolate, was chosen for transformation. A restriction
enzyme-mediated integration (REMI) protocol was developed using gGFP, a plasmid
which expresses green fluorescent protein (GFP) in the fungus. Transformation
efficiencies of ~1 5 transformants per microgram of plasmid DNA were obtained.
Transformation was also successful with El. vitis. This is the first successful genetic
transformation of E. lata and El.vitis. Through REMI transformation, 2184 transformants
were generated and screened for toxin production. Twenty two toxin-deficient candidates
were identified. Southern analysis indicates single, random insertion events for all 22

candidates. Candidates await further characterization.

For Amanda and Jack

iv

 

 

 

ACKNOWLEDGMENTS

I would like to express my gratitude to Dr. Annemiek Schilder, whose guidance
and support was invaluable for the success of my program. I would also like to thank the
other members of my committee; Dr. Andrew Jarosz. Dr. Frances Trail, and Dr. Jonathon
Walton. Their direction was a blessing.

I would also like to thank my lab mates not only for helping me throughout my
program, but for also being able to put up with me. Special thanks go to
Roger Sysak, Jerri Gillett, my mom away from mom, and Tim Miles. my surrogate little
brother.

I would like tO thank Linda Danhof for BY2 cells, Dr. Ewa Danielewicz and Dr.
Melinda Frame for assistance with microscopy, and Dr. Christine Vandervoort for her
assistance in GCMS.

A special thanks goes to my family, who might not understand what I do, but
support me while I do it anyway. especially Amanda. who puts up with more than she

deserves.

LIST OF CONTENTS

LIST OF TABLES ........................................................................................................... viii

LIST OF FIGURES ............................................................................................................ xi

CHAPTER 1

LITERATURE REVIEW .................................................................................................... 1

CHAPTER 2

SPATIOTEMPORAL ANALYSIS OF EUTYPA DIEBACK INFECTION

IN MICHIGAN ‘CONCORD’ VINEYARDS .................................................................. 28
Abstract .................................................................................................................. 29
Materials and methods ........................................................................................... 33
Results ................................................................................................................... 40
Discussion .............................................................................................................. 64
Literature cited ....................................................................................................... 71

CHAPTER 3

EFFECT OF EUTYPA DIEBACK YIELD ON ‘CONCORD’ GRAPEVINES

IN MICHIGAN WITH EVALUATION OF CROP LOSS MODELS ............................. 75
Abstract .................................................................................................................. 75
Introduction ........................................................................................................... 76
Materials and methods ........................................................................................... 79
Results ................................................................................................................... 83
Discussion ............................................................................................................ l 10
Literature cited ..................................................................................................... l 17

CHAPTER 4

DETECTION OF EUTYPA LATA AND EUTYPELLA VITIS IN

GRAPEVINE BY NESTED MULTIPLEX PCR ................................................ l 19
Abstract ................................................................................................................ l 19
Introduction ......................................................................................................... 120
Materials and methods ......................................................................................... 125
Results ................................................................................................................. 137
Discussion ............................................................................................................ 150
Literature cited ..................................................................................................... 154

CHAPTER 5

MORPHOLOGY, CULTURE CHARACTERISTICS, PATHOGENICITY,
SECONDARY METABOLITE PROFILE, AND GENETIC VARIABILITY

OF EUTYPELLA VITIS ................................................................................................. 159
Abstract ................................................................................................................ 159
Introduction ......................................................................................................... l 60
Materials and methods ......................................................................................... 162

Vi

Results ................................................................................................................. 1 73

Discussion ............................................................................................................ 193
Literature cited ..................................................................................................... 197

CHAPTER 6

TRANSFORMATION OF EUTYPA LATA AND EUTYPELLA VITIS BY

RESTRICTION ENZYME-MEDIATED INTEGRATION ........................................... 200
Abstract ................................................................................................................ 200
Introduction ......................................................................................................... 20]
Materials and methods ......................................................................................... 204
Results ................................................................................................................. 213
Discussion ............................................................................................................ 228
Literature cited ..................................................................................................... 230

APPENDICES ................................................................................................................. 234

APPENDIX A

SUPPLEMENTARY FIGURES FOR CHAPTER 2 ...................................................... 235

APPENDIX B

STATISTICAL OUTPUT FROM STEPWISE REGRESSION

PROCEDURES .............................................................................................................. 254

APPENDIX C

ANOVA OUTPUT F ROM CHAPTER 3 ....................................................................... 272

APPENDIX D

ESTIMATED EXPENSES FOR A MATURE CONCORD

VINEYARD .................................................................................................................... 285

APPENDIX E

ANOVA OUTPUT FROM CHAPTER 5 ....................................................................... 287

APPENDIX E '

DNA SEQUENCES FROM CHAPTER 5 ...................................................................... 292

vii

LIST OF TABLES

Table Page

2.1. Location and size of Vitis labrusca ‘Concord’ vineyards used in a study
on spatial relationships of Vines infected with E. lata. The total number
of Vines used for each vineyard did not change when different
quadrat sizes were analyzed ....................................................................................... 35

2.2. Incidence of vines symptomatic for Eutypa dieback for Vitis labrusca ‘Concord’
Vineyards by year ........................................................................................................ 42

2.3 Ordinary runs analysis by Vineyard and year to test for spatial aggregation
within and across rows for vines symptomatic for Eutypa dieback in
Vitis labrusca ‘Concord’ Vineyards in Southwest Michigan ...................................... 44

2.4. Determination of aggregation Of vines with foliar symptoms Of Eutypa
dieback from Vitis labrusca ‘Concord’ vineyards in Souhwest Michigan
using indices Of aggregation (la) measured for different quadrat sizes for
each vineyard and year using SADIE. SADIE Ia values greater than 1
signify aggregation of symptomatic vines .................................................................. 59

2.5. SADIE association analyses of overall spatial association between cluster
indices of newly symptomatic vines and previously symptomatic vines for
all vineyards for Vines arranged in 2 X 2 and 3 X 3 quadrats ................................... 60

3.1. Disease severity scale adapted from Thanassoulopoulos et al.6' for evaluating
and measuring disease severity of grapevines symptomatic for Eutypa dieback.
Basal shoots were not included in the rating .............................................................. 80

3.2. Linear regression analyses of the relationship between the percent
symptomatic shoots and the number of shoots per vine, clusters per vine,
and clusters per shoots per vine for vines symptomatic for Eutypa dieback
from 2003-2006. Vitis labrusca ‘Concord’ vineyards were located in
Southwest Michigan ................................................................................................... 84

3.3. Analysis of covariance for the effects of vineyard (Vin) and the percent
symptomatic shoots (%SS) on the percent number of shoots per Vine (%Sh),
percent number of clusters per vine (%Clu), and the percent number of clusters
per shoot per vine (%Clu/Sh) by year. Data was collected from three
Vitis Iabrusca ‘Concord’ vineyards located in Southwest Michigan ....................... 103

3.4

3.5.

3.6.

4.1.

4.2.

4.3.

5.1.

5.2.

5.3.

5.4.

Effect of disease severity as defined by the disease severity scale (DSS)
on the number of shoots per vine (Sh), clusters per vine (Cl), and number
of clusters per shoot per vine (Cl/Sh) for all years and vineyards.

Comparisons were conducted using the Tukey's HSD test (PS0.05) ....................... l05

Crop loss models based on the number of shoots (Sh) and percent
symptomatic shoots (%SS) for all vineyards and years individually. A
cumulative model was developed from data collected from all vineyards
and years. A ‘quick’ cumulative model was also developed using

only %SS data .......................................................................................................... 109

Crop loss models based on the number of shoots (Sh) and disease severity
scale (DSS) for all vineyards and years individually. A cumulative model
was developed from data collected from all Vineyards and years. A ‘quick’

cumulative model was also developed using only DSS data ................................... l 10
Isolates of Eutypa lata and Eutypa/la vitis and other fungal species used for
sequencing of the ITS regions and to determine the species specificity of

primers ELI, EL4, EVl, and EV4 for PCR amplification ....................................... 13l
The sequence, guanine-cytosine percentage (%GC), and calculated melting

temperature (Tm) Of the pairs of species-specific primers for Eutypa lata

and Eulypella vitis used in PCR amplifications ....................................................... 140

Comparison of traditional diagnostic techniques to the nested multiplex PCR
for detection of Eutypa lata and Eutypella vitis in naturally infected,

symptomatic grapevines (Vitis labrusca ‘Concord‘) in Michigan ........................... I46

Species, origin, host, and collection date for Eutypa lata and Eutypella vitis

isolates used in this study ......................................................................................... 163

Growth rate, conidial length, and ascospore length of isolates of
Eutypa lata and Eutypella vitis collected from Vitis labrusca ‘Concord’

vineyards in Southwest Michigan ............................................................................ 178

Lesion lengths on Vitis labrusca ‘Concord’ cuttings inoculated with isolates
of Eutypa lata and Eutypella vitis. Control treatment consisted of inoculation
with a sterile agar plug. Data was collected six months post-inoculation (lesion
length measured in mm). Isolates Eutypa lata and Eutypella vitis were isolated
from Vines from Vitis labrusca ‘Concord’ vineyards in Southwest Michigan

‘Concord’ Each treatment was replicated four times for each trial .......................... 18]

Lesion length (mm) on potted grape canes six months after inoculation with
isolates of Eutypella vitis and Eutypa lata. (Lesion length measured in mm).

The control treatment consisted of inoculation with a sterile agar plug .................. 189

ix

 

6.1 The effect of protoplast concentration on the transformation rate of
Eutypa lata isolate E30 using 10 ug oinndIlI-digested gGFP
per transformation .................................................................................................... 216

6.2. The effect of DNA concentration on the transformation rate of Eutypa lata
isolate E30 using linear, HindIIl-digested gGF P and protoplasts at a
concentration of 1x108 protoplasts per ml ................................................................ 217

6.3. The effect of the concentration of restriction enzyme in the transformation
mix on transformation rate of Eutypa lata isolate E30 using lOug
HindIII-digested gGFP and protoplasts at a concentration of 1x108
protoplasts per ml ..................................................................................................... 218

6.4. Number of transformants and transformation rate (transformants per pg DNA)
OfEutypa lata isolate EL130 and Eutypella vitis isolate EV339 using 10 ug
HindIII-digested gGFP and protoplasts at a concentration of 1x108 ........................ 219

D]. Estimated expenses for a mature Vitis labrusca ‘Concord’ Vineyard ...................... 286

 

LIST OF FIGURES

Figure Page

1.1.

1.2.

1.3.

1.4.

1.5.

1.6.

2.1.

2.2.

2.3.

A typical culture of Eutypa lata (anamorph Libertella blepharis) isolated from
a grapevine in Michigan. A) A l-month-old culture on potato dextrose agar.
B) C onidiomata on the surface of the culture. C) Typical curved. hyaline
conidia of L. blepharis. Images in this dissertation are presented in color .................. 4

Sexual fruiting bodies Of Eutypa lata on the surface of grapevine wood.
A) Parallel lines of ostioles emerging from the dark stromata.
B) Perithecial cavities visible after slicing through the stromata .................................. 5

Ascus and ascospores of Eutypa lata. A) Ascus containing 8 ascospores,
B) Allantoid ascospores ................................................................................................ 6

Typical foliar symptoms Of Eutypa dieback on a ‘Concord’ grapevine
in Southwest Michigan. Note the stunted shoots with chlorotic, cupped leaves .......... 7

Eutypa dieback canker on a ‘Concord’ grapevine trunk. A) Canker forming

around an old pruning wound, the likely site of infection. B) Cross section of

an infected trunk showing the wedge-shaped area of discolored, necrotic wood
characteristic of Eutypa dieback ................................................................................... 8

Chemical structures of secondary metabolites produced by Eutypa lata.
Figure from Kim et a1 ................................................................................................. 14

Disease progress curves Of vines symptomatic for Eutypa dieback in

Vitis labrusca ‘Concord’ vineyards A, B, and C from 2003 through 2007.

Incidence is presented as the percentage Of Vines symptomatic for Eutypa

dieback from the total Vines in each vineyard ............................................................ 41

Isotropic Moran’s I correlograms derived from vines symptomatic for

Eutypa dieback in Vitis labrusca ‘Concord’ vineyards in Souhwest

Michigan. Moran’s I values are plotted by separation distances of

symptomatic Vines in 1 X 1 quadrats for each year of the study.

A) Vineyard A, B) Vineyard B, C) Vineyard C, D) Vineyard D,

E) Vineyard E, F) Vineyard F, G) Vineyard G, H) Vineyard H ................................ 46

Anisotropic Moran’s I correlograms derived from vines symptomatic

for Eutypa dieback in Vitis labrusca ‘Concord’ vineyards in Souhwest

Michigan. Moran’s I values are plotted by separation distances of

symptomatic vines in 1 X 1 quadrats for 2003. A) Vineyard A,

B) Vineyard B, C) Vineyard C ................................................................................... 51

xi

2.4. Anisotropic Moran’s l correlograms from derived from vines symptomatic

for Eutypa dieback in Vitis labrusca ‘Concord’ vineyards in Souhwest

Michigan. Moran’s I values are plotted by separation distances of

symptomatic Vines in 1 X l quadrats for 2003. A) Vineyard F,

B) Vineyard G, C) Vineyard H .................................................................................. 53

2.5. Anisotropic Moran’s l correlogram from Vitis labrusca ‘Concord’ vineyard D

in Souhwest Michigan. Moran’s I values are plotted by separation distances of
symptomatic vines in l X 1 quadrats for 2006 ........................................................... 55

2.6. Anisotropic Moran’s I correlograms from Vitis labrusca ‘Concord’ vineyard

2.7.

E in Souhwest Michigan. Moran’s I values are plotted by separation
distances of symptomatic vines in l X 1 quadrats. A) 2006, B) 2007 ....................... 56

Correlation between SADIE la and Moran’s l for the first separation distance

(lag interval) between vines with foliar symptoms of Eutypa dieback in Vitis
labrusca ‘Concord’ vineyards in Souhwest Michigan. The first separation

distance for all vineyards and years were used in the analyses. A) 1 X l quadrat

size, B) 2 X 2 quadrat size, C) 3 X 3 quadrat size ...................................................... 62

2.8. Correlation between SADIE Ia and semivariance for the first separation distance

(lag interval) between vines with foliar symptoms of Eutypa dieback in Vitis
labrusca ‘Concord’ vineyards in Souhwest Michigan. The first separation

distance for all vineyards and years were used in the analyses. A) 1 X l

quadrat size, B) 2 X 2 quadrat size, C) 3 X 3 quadrat size ......................................... 63

2.9 The number of vines with foliar symptoms of Eutypa dieback within and across

3.1.

3.2.

rows for vineyard A in 2003 showing a greater number of symptomatic vines
on the South, East, and West edges of the Vineyard. Vineyard A is a commercial
Vitis labrusca ‘Concord’ vineyard located in Souhwest Michigan ............................ 66

Regression analysis between the number of clusters per Vine as a percentage of
healthy and the number of shoots per vine expressed as a percentage of healthy in
three Vitis labrusca ‘Concord’ vineyards located in Southwest Michigan. Data

was collected in 2003 ................................................................................................. 85

Regression analysis between the number of clusters per vine as a percentage of
healthy and the number of shoots per vine expressed as a percentage of healthy

in three Vitis labrusca ‘Concord’ vineyards located in Southwest Michigan.

Data was collected in 2004 ......................................................................................... 86

3.3. Regression analysis between the number of clusters per Vine as a percentage

of healthy and the number of shoots per Vine expressed as a percentage of
healthy in three Vitis labrusca ‘Concord’ vineyards located in Southwest
Michigan. Data was collected in 2005 ....................................................................... 87

xii

3.4. Regression analysis between the number of clusters per Vine as a percentage
of healthy and the number of shoots per Vine expressed as a percentage of
healthy in three Vitis labrusca ‘Concord’ vineyards located in Southwest
Michigan. Data was collected in 2006 ....................................................................... 88

3.5. Regression analysis between the number of shoots per vine as a percentage
of healthy and the percent symptomatic shoots in three Vilis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected
in 2003 ........................................................................................................................ 90

3.6. Regression analysis between the number of shoots per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected
in 2004 ........................................................................................................................ 91

3.7. Regression analysis between the number of shoots per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected
in 2005 ........................................................................................................................ 92

3.8. Regression analysis between the number of shoots per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ Vineyards located in Southwest Michigan. Data was collected
in 2006 ........................................................................................................................ 93

3.9. Regression analysis between the number of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three V iris labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected
in 2003 ........................................................................................................................ 94

3.10. Regression analysis between the number Of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ Vineyards located in Southwest Michigan. Data was collected
in 2004 ...................................................................................................................... 95

3.1 1. Regression analysis between the number Of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected
in 2005 ...................................................................................................................... 96

3.12. Regression analysis between the number of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected
in 2006 ...................................................................................................................... 97

xiii

3.13.

3.14.

3.15.

3.16.

3.17.

3.18.

3.19.

Regression analysis between the number of clusters per shoot as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
Vineyards located in Southwest Michigan. Data was collected in 2003 .................. 98

Regression analysis between the number of clusters per shoot as a percentage
Of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2004 .................. 99

Regression analysis between the number of clusters per shoot as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2005 ................ 100

Regression analysis between the number of clusters per shoot as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2006 ................ 101

Regression analysis between the percent predicted yield expressed as percent

of healthy and the actual yield expressed as a percent of healthy for three Vitis
labrusca ‘Concord’ vineyards located in Southwest Michigan. Data was

collected in 2005 .................................................................................................... 107

Relationship between the amount of disease severity measured as

percent symptomatic shoots and the yield as a percentage with healthy vines

at 100%. The ‘break-even point’ where cost of production equals crop revenue

for different crop yields (6, 8, 10, and 12 ton/acre) are located where the red

lines intersect the black line. Crop price is set at $250 per ton with an estimated
cost of production at $1.99 per vine with 691 vines per acre ................................. I 12

Relationship between the amount of disease severity measured as a disease

severity scale and the yield as a percentage with healthy Vines at 100%. The
‘break-even point’ were cost of production equals crop revenue for different

crop yields (6, 8, 10, and 12 ton/acre) are presented by red lines. Crop price

is set at $250 per ton with an estimated cost Of production at $1.99 per vine

with 691 vines per acre ........................................................................................... 1 13

4.1. Alignment of internal transcribed spacers (ITSI and ITS2) and 5.88

ribosomal DNA sequences of Eutypa lata and Eutypella vitis used to design
species-specific primers. Sequences selected for Eutypa lata-specific primers

EL] and EL4 are underlined and sequences for El. vitis-specific primers EVI

and EV4 are highlighted by black boxes. Eutypa lata and El. vitis isolates are
designated by EL and EV, respectively, followed by isolate numbers. Other

isolates (with GenBank accession numbers) include EL-WB457 (AF455427),
EL-1302466 (AJ302466), E. leptoplaca (AY684229), Diatrype sp. (AY684241),

and Diatrypella sp.(AY684240) ............................................................................... 139

xiv

 

4.2.

4.3.

4.4.

4.5.

4.6.

Diagram showing the positions of the nested, species-specific primers for

Eutypa lata (ELI, EL4) and Eutypella vitis (EVl, EV4); and the universal

primers, ITSIF and ITS4, within the ITS regions. For the nested multiplex

PCR, products from the first round of amplification with the universal ITS 1 F

and ITS4 primers are used as template in the second round with all of the
species-specific primers in a multiplex reaction. Primer pairs ELI-EL4 and
EVl-EV4 yield amplification products of 350 and 300 bp, respectively ................. 141

Detection of Eutypa lata and Eutypella vitis in naturally infected grapevine

cankers with visible stromata using nested multiplex PCR, A) PCR products

from the first step using universal primers lTSl F and ITS4; and B) PCR

products from the second step using species—specific primers EVl, EV4, ELl ,

and EL4. Lane 1: 1KB+ DNA ladder; lanes 2-15: wood samples from infected

vines with visible stromata; lane 16: wood sample from apparently healthy vine
(negative control); lane 17: E. lata DNA (positive control); lane 18: El. vitis

DNA (positive control); lane 19: l KB+ DNA ladder .............................................. 143

Detection of Eutypa lata and Eutypella vitis in cankers without visible stromata

from naturally infected grapevine using nested multiplex PCR, A) PCR products
from the centers of the cankers and B) PCR products from the margins of the
cankers. Lane 1: ”(3+ DNA ladder; lanes 2-13: wood samples from grapevine
cankers without visible stromata; lane 14: wood sample from apparently healthy

vine (negative control); lane 15: E. lata DNA (positive control); lane 16: El. vitis
DNA (positive control); lane 17: 1 KB+ DNA ladder .............................................. 144

Detection of Eutypa lata and Eutypella vitis from inoculated Vitis labrusca
‘Concord’ canes using nested multiplex PCR. Lane 1: 1KB+ DNA ladder;

lanes 2-5: PCR product from canes inoculated with El. vitis isolate EV70;

lanes 6-9: PCR product from canes inoculated with El. vitis isolate EV3 39;

lane 10: El. vilis DNA (positive control); lanes 11-14: PCR product from

canes inoculated with E. lata isolate EL130; Lanes 15-18: PCR product

from canes inoculated with E. lata isolate E30; lane 19: E. lata DNA

(positive control); lanes 20-23; PCR product from mock-inoculated canes

(negative controls); lane 24: 1 KB+ DNA ladder ..................................................... 147

Sensitivity of standard PCR and nested multiplex PCR for the detection of
Eutypa lata and Eulypella vitis. A) PCR products from standard PCR and B)
PCR products from nested multiplex PCR. Lane 1: l KB+ DNA ladder,

, Lane 2-10: serial dilution of genomic DNA; Lane 2: 3 ng, Lane 3: 300 pg,

Lane 4: 30 pg, Lane 5: 3 pg, Lane 6: 300 fg, Lane 7: 30 fg, Lane 8: 3 fg,
Lane 9: 300 ag, Lane 10: 30 ag, Lane 11: water control .......................................... 149

XV

5.1.

5.2.

5.3.

5.4.

5.5.

5.6.

Fruiting bodies of Eutypa lata and Eulypella vitis on the surface of grapevine
wood. A) Stromata and perithecia of E. lata, B) Stromata and perithecia of
El. vitis, C) Scanning electron microscope image of the ostioles of E. lata,
D) Scanning electron microscope image of erumpent, 3-4 sulcate ostioles of

E1. vitis. Images in this dissertation are presented in color ...................................... 175

Compound microscope images of asci, ascospores, and conidia of Eutypa lata
and Eutypella vitis from grapevine. A) Ascus of E. lata with eight ascospores,
B) Ascus of El. vitis with eight ascospores, C) Ascospores of E. lata.

D) Ascospores of El. vitis, E) Hyaline conidia of E. lata, F) Hyaline conidia

Of El. vitis ................................................................................................................. 176

Culture characteristics of Eutypa lata and Eutypella vitis isolated from
Vitis labrusca ‘Concord’ vineyards in Southwest Michigan after 1 month
of growth on PDA. A) Typical culture of E. lata (EL198), B) Typical
culture of E1 .vilis (EVA9), C) Cross-section of E. lata on PDA, note the
appressed mycelium D) cross-section of El. vitis on PDA, note the aerial
mycelium and subconical pycnidia indicated by black arrow, B) Isolate
of El. vitis EV270 with slick, appressed mycelium, F) Isolate of El. vitis

EV300 with darkened mycelium .............................................................................. 177

Longitudinal and cross sections of lesions caused by isolates of

Eutypella vitis and Eutypa lata in inoculated, l-year-Old Vitis labrusca
‘Concord’ canes. Longitudinal sections are to the left and cross sections are
to the right. Note the wedge-shaped necrosis in the cross sections indicated
by black arrows. A.) El. vitis EV229, B) El. vitis EV232, C) E. lata E30

D) control mock-inoculated with an agar plug ......................................................... 183

Mean length of lesions caused by isolates of Eutypa lata and Eutypella vitis
on mature grapevines. Eight-year-Old Vitis labrusca ‘Concord’ grapevines
were inoculated with mycelial plugs Of agar, and measurements were taken
one year post-inoculation. Vines were maintained at the Clarksville
Horticultural Experiment Station at Clarksville, MI. The control treatment
was mock-inoculation with a sterile plug of agar. Columns followed by the
same letter are not significantly different according to Tukey’s

HSD test (P s 0.05) .................................................................................................. 186

Mean length of lesions caused by isolates of Eutypa lata and Eurypella vitis
on mature grapevines. Eight-year-old Vitis labrusca ‘Concord’ grapevines
were inoculated with ascospores, and measurements were taken one year
post-inoculation. Vines were maintained at the C Iarksville Horticultural
Experiment Station at Clarksville, MI. The control treatment was
mock-inoculation with a sterile plug of agar. Columns followed by the
same letter are not significantly different according to Tukey’s

HSD test (P S 0.05) .................................................................................................. 187

xvi

5.7.

5.8.

6.1.

6.2.

6.3.

6.4.

Bootstrap consensus tree constructed using the UPGMA method from the

alignment of the ITSl-5.88-ITSZ region of 42 isolates of Eutypella vitis and

14 isolates of Eutypa lata from Southwest Michigan and California.

Branches corresponding to partitions reproduced in less than 60%

bootstrap replicates are collapsed. The percentage of replicate trees in

which the associated isolates clustered together in the bootstrap test

(1000 replicates) are shown next to the branches ..................................................... 191

Bootstrap consensus tree constructed using the UPGMA method from the

alignment of the B-tubulin gene of 42 isolates of Eulypella vitis and 14 isolates

of Eutypa lata from Southwest Michigan and California. Branches corresponding to
partitions reproduced in less than 60% bootstrap replicates are collapsed. The
percentage of replicate trees in which the associated isolates clustered together

in the bootstrap test (1000 replicates) are shown next to the branches .................... 192

Circular map of transformation vector gGFP. ngd: Aspergillus nidulans
glyceraldehyde-3-phosphate dehydrogenase promoter, hph: E. coli hygromycin
phosphotransferase gene for resistance to hygromycin B, TtrpC: Aspergillus
nidulans tryptophan C transcriptional termination signals, SGFP: synthetic

green fluorescent protein gene, Amp: ampicillin resistance gene ............................ 205

The effect of varying concentrations of hygromycin B on the growth of Eutypa

lata isolated from grapevine. Isolate E30 was grown on PDA amended with

0, 5, 10, 50, 100, and 200 mg/L hygromycin B. Growth was completely

inhibited at 50 mg/L ................................................................................................. 215

PCR conformation of transformation of Eutypa lata isolate E30 with gGFP

using primers Hptl and Hpt2. A 500 bp band indicates that the hgh gene on

the gGFP plasmid was successfully integrated into the genome of a transforrnant.

A 500 bp band is present in lanes 2-13 and 15. Lane I and17: I Kb+ DNA

Ladder, Lanes 2-13: putative hygromycin-resistant transformants, Lane 14:
wild-type E30 isolate, Lane 15: gGFP plasmid, Lane 16: negative control ............. 221

Southern blot analysis of E30 transformants . Lane 1: linear gGF P, Lane 2:

Genomic DNA from Xbal digested E30 wild-type, Lanes 3-24:

Xbal-restricted genomic DNA of 22 putative toxin-deficient transformants

generated through REM]. The blot was probed with a 500 bp

digoxigenin-labeled fragment of the hph gene ......................................................... 222

xvii

6.5. Light, fluorescent, and overlay micrographs of mycelia of gGFP
transformants of Eulypella vitis and Eutypa lata. Mycelium of wild-type
El. vitis, EV339, A) light micrograph, B) fluorescent micrograph,C)
overlay of A and B. Mycelium of El. vitis transformant EV339-12H,
D) light micrograph, E) fluorescent micrograph, F) overlay of A and B.
Mycelium Of wild-type E. lata, E30, G) light micrograph, H) fluorescent
micrograph, I) overlay of A and B. Mycelium of E. lata transformant E30-13H,
.1) light micrograph, K) fluorescent micrograph, L) overlay of A and B ................. 224

6.6. Light, fluorescent, and overly micrographs of gGFP transformants of
Eutypa lata and Eutypella vilis colonizing wood of ‘Concord’ grapevines
6 months after inoculation. Steril agar-inoculated grapevine wood, A) light
micrograph, B) fluorescent micrograph, and C) overlay of of image A and B.
Grapevine wood inoculated with E. lata transformant E30-13H, D) light
micrograph, E) fluorescent micrograph F) overlay of image D and E.
Grapevine wood inoculated with El. vitis transformant EV339-12H G)
light micrograph, H) fluorescent micrograph, J) overlay of image D and E ............ 225

6.7. Cell Viability assay using tobacco BY2 cells stained with 0.04% Trypan blue.
A) Untreated cells, B) Cells incubated with malt extract/yeast extract broth.
C) Cells incubated for 3 days with an equal volume Of culture filtrate from
Eutypa lata isolate E30. Note blue staining resulting from sell death,
D) Cells incubated for 3 days with and equal volume of culture filtrate from
gGF P transformant E30-928H .................................................................................. 227

A. 1. Map of Eutypa dieback incidence for Vineyard A. The locations of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 236

AZ. Map Of Eutypa dieback incidence for vineyard B. The locations Of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ...................................................... 237

A3. Map Of Eutypa dieback incidence for Vineyard C. The locations of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 238

A4. Map of Eutypa dieback incidence for vineyard D. The locations of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 239

A5. Map of Eutypa dieback incidence for Vineyard E. The locations of

individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 240

xviii

A.6. Map of Eutypa dieback incidence for vineyard F. The locations of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 241

A.7. Map of Eutypa dieback incidence for vineyard G. The locations of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 242

A8. Map of Eutypa dieback incidence for vineyard H. The locations of
individual symptomatic vines are represented as black rectangles.
Vineyard rows run in the direction of the y axis ..................................................... 243

A9 Isotropic Moran’s I correlograms derived from Vitis labrusca ‘Concord’
vineyards in Souhwest Michigan. Moran’s 1 values are plotted by
separation distances of symptomatic vines in 2x2 quadrats for each year
of the study. A) Vineyard A, B) Vineyard B, C) Vineyard C,
D) Vineyard D, E) Vineyard E, F) Vineyard F, G) Vineyard G,
H) Vineyard H .......................................................................................................... 244

A.10. Isotropic Moran’s l correlograms derived from Vitis labrusca ‘Concord’
vineyards in Souhwest Michigan. Moran’s 1 values are plotted by separation
distances of symptomatic vines in 3x3 quadrats for each year of the study. A)
Vineyard A, B) Vineyard B, C) Vineyard C, D) Vineyard D, E) Vineyard E,
F) Vineyard F, G) Vineyard G, H) Vineyard H ..................................................... 248

A.11. Semivariograms of vines symptomatic for Eutypa dieback from
Vilis labrusca ‘Concord’ vineyard D located in Souhwest Michigan.
Moran’s I values are plotted by separation distances of symptomatic vines.
A) 2 X 2 quadrats, B) 3 X 3 quadrats ..................................................................... 252

A.12. Semivariograms of vines symptomatic for Eutypa dieback from
Vitis labrusca ‘Concord’ vineyard E located in Souhwest Michigan.
Moran’s I values are plotted by separation distances of symptomatic vines.
A) 2 X 2 quadrats, B) 3 X 3 quadrats ..................................................................... 253

xix

CHAPTER 1. LITERATURE REVIEW

Michigan grape production

Michigan is the fourth largest grape-producing state in the United States with over
5900 ha of grapes, most of which are located in Berrien and Van Buren counties in
Southwest Michigan (43). The majority of this acreage consists of Vitis labrusca L.
‘Concord’ and ‘Niagara,’ which are primarily used for juice production (43). The
remaining acreage is used for wine grape and table grape production. The major areas
where wine grapes are grown are the southwest comer of the Lower Peninsula and in the
Traverse Bay area in the northwest comer of the Lower Peninsula. The cultivar Concord
is the most widely planted with 3682 ha, which represents nearly 73% of the entire grape
acreage, and has been cultivated in Michigan for over a century (43 51). More than 75%
of ‘Concord’ plantings are over 30 years old (43). ‘Niagara’ is the second most widely
planted cultivar with a total Of 1424 ha (43). For several years in the 1990’s, many new
‘Niagara’ vineyards were planted due to an anticipated demand for white grape.
However, renewed interest in ‘Concord’ juice might reverse this trend as the health
benefits of purple grape juice are discovered (75) Wine grape (Vitis vinifera L. and
French-American hybrids resulting from crosses of American Vitis spp. with Vitis
vinifera L.) acreage is also increasing as the wine industry is growing in Michigan (33

43).

Euggga lata
Eutypa lata (Pers.:F r.) Tul. & C. Tul. (syn. E. armeniacae Hansf. & Carter,

anamorph Libertella blepharis A. L. Smith [syn. Cytosporina sp.]) is an ascomycete
fungus in the family Diatrypaceae (order Diatrypales). The Diatrypaceae family contains
nine accepted genera including the economically important Diatrype, Diatrypella,
Cryptosphaeria, Eutypella, and Eutypa (3). This family contains plant pathogens as well
as saprophytic fimgi that are commonly found on numerous dead or declining woody
angiosperrns worldwide. Some pathogens in this group are believed to have a very
limited host range. Diatrypella betulina (Peck) Sacc., for example, is reported to infect
only birch tree species (Betula spp.) (1). Others, like E. lata, are known to cause disease
on numerous hosts.

The host range of E. lata includes over 80 plant species from more than 27
botanical families (9, 11, 58) Hosts of agronomic importance include Prunus species like
apricot (Prunus armeniaca L.) (6) and Japanese plum (Prunus salicina Lindl.) (8), as
well as lemon (Citrus limon L.) (37) and apple (Malus domesticus L.) (29). However, the
most economically important host of E. lata worldwide is grape (Vitis spp.) (9).

Eutypa lata is the causal pathogen of Eutypa dieback or eutyposis, a destructive,
perennial disease of woody grapevine tissue. The pathogen can be found in every major
grape-growing region in the world and is considered one of the most costly diseases to
grape and wine production (10). In California alone, the estimated annual loss to the
wine industry from E. lata exceeds $260 million (76) The perithecia] stroma of E. lata

was first found on dead grapevines in Michigan in 1977 (80). In 1983, approximately

10% of all mature Concord grapevines in Michigan were estimated to have Eutypa

dieback (27), and some Concord vineyards have up to 30-50% infected Vines (5).

Historical significance

Eutypa lata and Eutypa armeniacae were originally believed to be separate
species. Eutypa armeniacae was first described by Carter (6) on apricot in southern
Australia in 1957. Rappaz (66) later determined that they were in fact the same species.
From this point forward, both species will be referred to as E. lata.

Symptoms caused by E. lata and symptoms caused by Phomopsis viticola Sacc.
were previously considered to be a single disease called “dead arm”. Nearly a century
ago, Riddick (68, 69) listed the symptoms of “dead arm” to be “dwarfed, chlorotic spring
foliage, dead arms, and a dry wood rot” in addition to “green shoot and leaf necrosis”.
He believed that the symptoms were the result of a single pathogen, P. viticola (61).
Coleman (14) reported that the important disease symptoms of “dead arm” were the
weak, stunted spring growth along with the dead arms associated with cankers at pruning
wounds. He indicated that the green shoot and leaf necrosis were not important
symptoms (14). Researchers in other parts of the world stressed the importance of the
green shoot lesions, pointing out that the dieback of arms and direct invasion of wounds
by P. viticola rarely occurred (22, 32, 78). Nearly 70 years afier Riddick first described
“dead arm”, Moller and Kasimatis (47, 48) inoculated vines with E. lata and were able to
show development of disease symptoms that occurred in “dead arm” that were separate
from those caused by P. viticola. The disease previously known as “dead arm” is now

known to be two separate diseases, Phomopsis leaf and cane spot (P. viticola) which is

characterized by necrotic leaf and shoot lesions and Eutypa dieback (E. lata) which

causes stunting of shoots, cankers, and dieback of arms.

Fungal characteristics

Eutypa lata (anamorph: Libertella blepharis) can be isolated by culturing infected
wood chips or ascospores on artificial media (9). Mycelium grows appressed to the agar
and is hyaline, becoming white or yellow over time, often becoming dark brown on the
underside of the media (9). Conidia are produced in culture on conidiogenous cells or
conidiomata, often arising from mycelium or in subconical pycnidia. The conidia are
single-celled, hyaline, curved near the apex, and measure 18-45 x 1.5 pm (6, 9, 30). A
typical culture of L. blepharis, including conidial morphology, is shown in Figure 1.1.
The conidia usually do not germinate in culture and are therefore thought to play no part
in dissemination of the pathogen, but has been proposed to act as spermatia (9). Sexual
fruiting bodies have not been obtained in culture indicating that E. lata is likely

heterothallic.

 

Figure 1.1. A typical culture of Eutypa lata (anamorph Libertella blepharis) isolated fi'om
a grapevine in Michigan. A) A l-month-old culture on potato dextrose agar. B)
Conidiomata on the surface of the culture. C) Typical curved, hyaline conidia of L.
blepharis. Images in this dissertation are presented in color.

Approximately five years after infection, sexual fruiting bodies of the fungus,
called perithecia, become readily visible on the surface of the dead wood as loose bark
falls away (48). Perithecia can be up to 450 pm in diameter and are formed in a single
layer in black stromata under the bark of the vine (9). Ostioles of the perithecia can be
seen on the surface of the stromata and are arranged singularly in parallel lines (Figure
1.2A). Slicing through the surface of the stromata reveals dark, shiny cavities that are the
lumina of the perithecia (Figure 1.2B). The perithecium is lined with pseudoparenchyma
cells and also contains paraphyses and periphyses. Asci are formed on pedicels that are
60-130 um long within the perithecia (58, 67). Asci are unitunicate, cylindrical to clavate,
and range in size fi‘om 30-60 x 5-7.5 pm with eight ascospore per ascus (Figure 1.3A)
(58, 67). The ascospores are pale yellow and allantoid and measure 65-11 x 1.8-2 um

(Figure 1.3B) (58, 67). Pycnidia can also form on infected wood and when moistened,

will form orange or light yellow tendrils containing conidia (44).

 

Figure 1.2. Sexual fruiting bodies of Eutypa lata on the surface of grapevine wood. A)
Parallel lines of ostioles emerging from the dark stromata. B) Perithecil cavities visible
after slicing through the stromata.

   

Figure 1.3. Ascus and ascospores of Eutypa lata. A) Ascus containing 8 ascospores, B)
Allantoid ascospores.

Disease Symptoms

The most commonly seen symptoms of E. lata are stunted and deformed shoots
(5 8). These symptoms are thought to be caused by the release of toxins by the fungus
that result in chlorosis, tattering, and upward cupping of the leaves as well as shortening
Of the internodes (Figure 1.4) (58). Additional symptoms are uneven berry size, abortion
of clusters, and shoot death in severe cases (35, 58). Shoot symptoms are most apparent
early in the spring when shoots are less than 25 cm long. By summer, vigorous healthy
shoots will ofien obscure diseased shoots. F oliar symptoms do not occur until at least 2
years after infection and precede the formation of fruiting bodies on the canker (9, 46).
The expression of foliar symptoms has been reported to fluctuate between years in
Australia, France, and North America (5, 18, 21, 77). In a 6-year study conducted on
‘Shiraz’ grapevines in South Australia, variation in symptom expression was influenced
by climatic factors (77). A number of possible relationships were identified between
symptom expression and climate including; increased symptom expression after

increased winter rainfall 18 months prior, decreased disease incidence when spring

temperatures were warmer, and a reduction in disease incidence when rainfall was either

very high or very low in October (spring) (77).

 

Figure 1.4. Typical foliar symptoms of Eutypa dieback on a ‘Concord’ grapevine in
Southwest Michigan. Note the stunted shoots with chlorotic, cupped leaves.

Eutypa infections usually start at the site of pruning wounds. Afler the third or
fourth year, a canker becomes evident around the site of infection, and can be seen if the
bark is carefully removed (Figure 1.5A) (9). The canker typically extends down from the
site of infection, sometimes below the soil line (9, 58). The visible presence of the canker
often correlates with the onset of the previously mentioned foliar symptoms. A cross
section of the infected trunk or cane through the canker will often show a brown, wedge-
shaped necrotic area in the vascular tissue (Figure 1.5B) (9, 58). The necrosis is caused

by a dry soft rot caused by E. lata (24). As the disease progresses over several years,

large sections of the cordon, and often an entire arm, will die. Ifthe canker girdles the

trunk, the whole vine will die.

 

Figure 1.5.Eutypa dieback canker on a ‘Concord’ grapevine trunk. A) Canker forming
around an old pruning wound, the likely site of infection. B) Cross section of an infected
trunk showing the wedge-shaped area of discolored, necrotic wood characteristic of
Eutypa dieback.
Disease Cycle

The infective propagule of E. lata, the ascospore, requires moisture for its release
and wind for dissemination. From trapping studies conducted in several grape-growing
regions around the world, rain events of as little as 2 mm were sufficient for ascospore
release from perithecia when temperatures were over 0°C (9, 45, 55, 63, 80). Once
perithecia were wetted, peak ascospore release occurred 0.5-3 hrs later. Ascospore
release continued while the perithecia remained wet, sometimes lasting for months (45,
55). In a Michigan study, relative humidity, solar radiation, and wind speed did not play a

significant role in ascospore release, but wetness and the maturity of the perithecia were

critical (80). In the absence of rain, snowmelt has been shown to cause ascospore release
when temperatures were above freezing (5 5). Ascospore release was found to be the
greatest in the spring and the fall for both Michigan and New York (55, 80). Ascospore
release did occur in the summer and winter, but only following rain events (55, 80).
Ascospores can be spread distances exceeding 50 km on air currents (9, 63).

Eutypa lata infection can occur when a wound, most likely from priming, occurs
on the woody tissue of the grapevine. Ascospores land on a wound and are drawn into
the xylem, most likely through capillary action (7). Once the ascospores germinate,
hyphae grow into the Vine through the xylem pits and eventually through the cell walls
via cell-wall degrading enzymes. Moller et a1. (49) inoculated grapevines with mycelium
and found that cankers grew an average of 76 mm a year. As the pathogen spreads
through the vine, it causes a staining of the wood and soft rot, causing the wood to
become brittle (24).

The susceptibility of pruning wounds is significantly affected by the age of the
vine and the date of pruning. Wounds on older wood are more susceptible to infection
than wounds in l-year old wood, but it is uncertian whether this is due to the increase in
the size of the wound of older wood or differences in inherent susceptibility (47). Eutypa
dieback symptoms are rarely found in vineyards less than 8 years old (9, 23). Munkvold
and Marois (54) inoculated pruning wounds on ‘Thompson Seedless’ grapevines in
California with E. lata ascospores to determine factors that affected wound susceptibility.
Wound infection incidence was higher when Vines were pruned early in the dormant
season (November and December) and lower when Vines were pruned later in the

dormant season (January or March). Wounds were highly susceptible for 2 weeks after

pruning, followed by a decline in susceptibility with the vines becoming relatively
resistant to infection after 28 days. The decrease in susceptibility was highly correlated
with an increase in suberin and lignin deposition at the site of the wound. The rate of
increase of suberin and lignin was highly correlated with the rate of accumulation of
degree-days over 0°C. A similar study was conducted in Michigan on
‘Concord’grapevines that showed less infection when pruning was conducted during

extended periods of cold weather (< 5°C for 2 weeks) (80).

TM

Moller and Kasimatis (48) were the first to propose that a toxin was involved in
the development of foliar symptoms when they were unable to isolate mycelium of E.
lata from symptomatic shoots. Mauro et a1. (41) tested phytotoxicity of filtrates from E.
lata cultures using young plantlets and leaf disk bioassays. Foliar symptoms in young
plantlets and necrosis of leaf disks were caused by culture filtrate rather than by direct
application of mycelia. The active component in culture filtrate was heat stable at 110°C
for 20 minutes, indicating that the toxin was not likely an enzyme, which would have
denatured at 100°C. In 1991, the toxin was isolated from the sap of infected vines,
crystallized, and analyzed (79). The toxin, 4-hydroxy-3 -(3-methyl-3-butene-l-
ynyl)benzaldehyde, was given the name eutypine.

The toxicity of eutypine has been investigated with cell suspension cultures of
Vitis vinifera cv. Gamay and C-14-1abeled eutypine (19). The study of the chemical
characteristics of eutypine revealed that the toxin is a weak acid (pKa = 6.2) with a

lipophilic character. Eutypine was shown to be rapidly taken up by the cells. Uptake of

10

the toxin was not hindered by a concentration gradient showing no saturation from high
eutypine concentrations, and neither structural analogues of eutypine nor protein-
modifying reagents had an inhibitory effect on eutypine uptake. These data suggest a
mechanism of passive diffusion for eutypine uptake.

The effect of eutypine has also been studied at the mitochondrial level, using
methyl-eutypine, an unprotonable derivative of the toxin (20). The effects of these
molecules on mitochondrial respiration and membrane potential were compared using
isolated mitochondria from grapevine cells in suspension cultures and potato tuber
mitochondria. Eutypine induced marked stimulation of oxygen consumption and had a
depolarizing effect on the membrane, while methyl-eutypine exhibited a very small effect
on both the rate of oxygen uptake and membrane potential. For high eutypine
concentrations, a mixed effect corresponding to a direct inhibition of electron transport
and uncoupling was observed. It was concluded that eutypine uncouples mitochondrial
oxidative phosphorylation and decreases the adenosine diphosphate/oxygen ratio in
grapevine cells by increasing the proton leaks Via a cyclic protonophore mechanism.

A similar effect from eutypine can be seen in the plasma membrane Of plant cells.
In Amborabe et a1. (4), three plant species (Beta vulgaris L., Mimosa pudica L., Vitis
vinifera L.) were used to study the effect of eutypine on the plasma membrane. The
fungal toxin at 100 uM triggered a rapid, dose-dependent hyperpolarization of the
membrane potential in M pudica pulvinar motor cells. It also enhanced proton
permeability in plasma membrane vesicles without modifying H+-ATPase activity. This

resulted in the hindrance of sucrose and valine absorption by plasma membrane vesicles

11

and plant tissues. These data further indicate that eutypine behaves like a protonophoric
compound.

In Colrat et al. (16), the detoxification of eutypine by Vitis vinifera cell-
suspension cultures was investigated. Eutypine was converted by grapevine tissues into a
single compound, identified by mass spectrometry and nuclear magnetic resonance as 4-
hydroxy-3-(3 -methyI-3-butene-1-ynyl) benzyl alcohol, designated eutypinol. This
compound was found to be non-toxic to grapevine tissues and failed to affect the
oxidation rate or membrane potential of isolated mitochondria. In grapevine cells,
reduction of eutypine into the corresponding alcohol is an NADPH-dependent enzymatic
reaction. An enzyme which reduced eutypine was partially purified and was found to
have a molecular mass of 54-56 kDa. The enzyme exhibited an apparent Michaelis
constant for eutypine of 44 [mu] M, and was active between pH 6.8 and 7.5 with a
maximum at pH 7.0. The eutypine reductase activity was improved by Mn2+ and Mg2+
and inhibited by disulfirarn and p-hydroxymercuribenzoate (16).

A similar eutypine-reducing enzyme (ERE) was isolated from etiolated mung
bean (Vigna radiata L.) hypocotyls (17). The purified protein was an NADPH-dependent
aldehyde reductase of 36 kD. The enzyme exhibited a K-m value of 6.3 uM for eutypine
and detoxified eutypine efficiently over a pH range from 6.2 to 7.5. The enzyme failed to
catalyze the reverse reaction using eutypinol as a substrate. The gene coding for the
enzyme was isolated and named VR-ERE (Vigna radiata eutypine-reducing enzyme)
(31). Expression of a VR-ERE transgene in Vitis vinifera cells cultured in vitro conferred
resistance to the toxin. It has not yet been reported whether or not the eutypine-resistant

transformants display disease resistance.

12

The conversion of eutypine to eutypinol was studied in different tissues of
grapevine by HPLC and LC-MS (2). Grape callus tissues were able to convert eutypine
into eutypinol within the first 3 h of exposure. The grape plantlets cultured in Vitro could
also transform eutypine into eutypinol. These results further verified that detoxification
of eutypine in grape tissues is an active process with eutypinol being rapidly metabolised
into other compounds.

The production of other metabolites in Vitro has been studied for E. lata.
Molyneux et a1. (50) examined three strains of E. lata for their metabolite composition
and yield. Both composition and yield differed significantly by strain and growth
medium, but usually reached a maximum after 24-30 days of fungal growth. Eutypine
was produced by only one of the strains examined, with eutypinol occurring in the
cultures of the other two strains. Two other novel metabolites were isolated: a
methoxyquinol, named eulatinol, and a chromene, named eulatachromene. In Mahoney et
a1. (40), the phenolic metabolite profiles of 11 strains of E. lata were examined on
different artificial growth media. Six compounds were generally produced in significant
amounts, namely eutypine, eutypinol, and eulatachromene, and its benzofuran cyclization
product, together with siccayne and eulatinol (Figure 1.6). The two most widely
distributed and abundant metabolites were eutypinol and eulatachromene, which were
present in 8 of the 11 strains. Using a grapeleaf disc bioassay to establish their relative
toxicity, neither eutypinol nor siccayne were phytotoxic while eulatachromene, eulatinol,
eutypine, and the benzofuran induced necrosis. These results indicate that the symptoms
expressed in Eutypa dieback may result from several metabolites rather than a single

compound. In another study using the yeast Saccharomyces cerevisiae Meyen ex.

13

Hansen to determine the mode of action and toxicity of these metabolites, all of the
metabolites were found to be either lethal or inhibited growth through the inhibition of

mitochondrial respiration (36).

(7H20H
HO
/ 0
I 03‘
\ \ /
§ (,H, \ CH2 HOCH;
()H ' ()II l
CH3 CHI
Eutypine Eutypinol Eulatachromene
11 ll
OHC /
l I
.\ \
W % CH2 \ CH;
-. OCHI
(11} OH -.
cm ‘H‘
5-Formyl-2-(methylvinyl) Siccayne Eulatinol

[1]benzofuran

Figure 1.6. Chemical structures of secondary metabolites produced by Eutypa lata.
Figure from Kim et a1. (36).

Impact on Yield
Eutypa dieback impacts grape production in all grape-growing regions of the
world. The disease causes economic losses by reducing yield and the longevity of a
vineyard, and increasing the cost of management. Several studies have been conducted to
evaluate the effect of Eutypa dieback on yield. The severity of Eutypa dieback on
individual grapevines was assessed in 1991 and 1992 in California vineyards of ‘Chenin
blanc’ and ‘French Colombard’ (53). Disease severity was measured as fire proportion of

the vines' spurs killed or symptomatic. The disease caused a significant reduction in yield

14

 

of infected vines compared with healthy vines. Yield reduction was found to be due
primarily to a diminished number of clusters per vine, while mean cluster weights were
smaller but not always significant. Yield reductions for entire vineyards ranged from
30.1 to 61.9%, depending on mean disease severity. Records for ‘Chenin blanc’
vineyards in Merced County, CA, revealed a trend of declining yields beginning at 12 yr
of age, which closely followed a period of rapid increase of Eutypa dieback incidence. In
‘Barbera’ vineyards, which are rarely affected by Eutypa dieback, yields increased up to
age 10 and then remained constant.

Commercial ‘Concord’ vineyards in south central Washington were surveyed
during the springs of 1983 and 1984 (1334 vineyards sampled) for the incidence of
Eutypa dieback (35). The mean incidence of Eutypa dieback increased from 0.3 to
15.7% as the diameter of grapevine trunks increased from <38 to >51 mm. The highest
disease incidence observed in a vineyard was 76%. Sprinkler irrigation did not affect
disease incidence. When yields of healthy and diseased vines were compared, total yield,
number of clusters, and weight per cluster were significantly reduced on diseased Vines.
Yield components generally decreased as disease severity increased from moderate to
severe. The mean yield loss was 75% (range of 62 to 95%) on vines with severe
symptoms, and 41% (range of 19 to 65%) on vines with moderate symptoms of Eutypa

dieback.

D_iagnostics and Detection
Diagnosis and detection of E. lata can often be difficult, especially when

considering the latency of the disease and the difficulty in obtaining pure cultures from

15

infected wood (9). A number of serological and molecular diagnostic techniques have
been developed to identify and detect E. lata, both in plant tissue and in culture.
Serological techniques using antibodies to the fungus have proven to be unreliable (25,
26, 62). Using restriction fragment length polymorphism (RFLP) or polymerase chain
reaction (PCR) probes have been shown to be more useful (38, 39, 71), but were either
not effective in detecting E. lata isolates from Michigan or have not yet been tested

against isolates from Michigan (5).

Host Resistance

No grapevine cultivar is immune to Eutypa dieback, however, the severity of the
symptoms varies. In Michigan, the most commonly planted, highly susceptible cultivars
are Cabernet Sauvignon, Concord, and Mare'chal Foch (9, 41, 43, 84). Other susceptible
cultivars include Chardonnay, Gewiirztraminer, Pinot Noir, Vignoles, and Riesling.
Cabernet Franc, Catawba, Cayuga White, Chambourcin, Chancellor, Niagara, and Seyval
have some natural tolerance or resistance (9, 41, 83). Merlot and Semillon have been
thought to be relatively resistant to E. lata, but a number of studies have indicated that
both cultivars can be infected, but do not show symptoms as quickly or severely as
susceptible cultivars (13, 41 , 54, 79). The range in susceptibility of cultivars may be
explained by the ability of the cultivar to detoxify or sequester the toxins that cause

symptoms (15-17, 41).

16

Chemical and Biological Control

The only effective treatment for the control of Eutypa dieback is a prophylactic
fungicide application to pruning wounds. Different chemistries have been evaluated and
shown varying success, with benomyl as the most successful firngicide (7, 9, 56, 65). In a
five-year study conducted in Michigan with pruning followed by an application of
benomyl Via an airblast sprayer, naturally occurring E. lata infections were reduced
48.5% when pruned in mid-January to mid-February and 34% between mid-February to
mid-April (65). No reduction in the number of infections were seen when pruning in
conjunction with benomyl applications was conducted in December (65). Effective
application requires a high spray volume (0.1 ml of water per cmz), via an air blast
sprayer or manually painting the fungicide directly onto the pruning wound (7, 9, 56, 65).
Manually applying fungicides to pruning wounds is cost prohibitive for all but the most
valuable vines, making this a less than desirable control method. Also, benomyl was
removed from the market in 2002 and is no longer available. Topsin M (thiophanate
methyl), a fungicide that is labeled for use on grapevine, has been proposed as a potential
replacement for benomyl (84). Boric acid has been evaluated as an effective replacement
for benomyl, with good success in field trials (96.9% control for single inoculations and
71.9% control for double inoculations of 5% w/v boric acid in water); commercial
adoption is still to be determined (70).

Several organisms have been screened as potential biological agents for the
control of Eutypa dieback (12, 34, 52, 42, 72-74). Bacillus subtilis, Erwinia herbicola
(73, 75), and a Bacillus sp. (72) as well as the fungi T richoderma harzianum (34) and

Fusarium Iateritium (34, 42) have been evaluated. Several of these biological control

17

‘__

§'_

 

:54.

EJ

agents have been shown, under certain conditions, to prevent infections of E. lata
ascospores as equally well as fungicides (12, 52). In a California study using ‘Thompson
Seedless’ and ‘Chenin blanc’ grapevines, F. lateritium was as effective as benomyl in
preventing infection by E. lata ascospores when applied 14 days prior to inoculation, but
was not as effective as benomyl when applied 2 days prior to inoculation (52). While
most of the biological control agents have shown control of Eutypa dieback through
colonizing and outcompeting E. lata at the site of infection, commercial application of

these organisms has yet to be developed.

CmmonUOI

Vineyard sanitation is the most practical means of managing Eutypa dieback.
Vineyards should be scouted in the spring to find vines symptomatic for Eutypa dieback.
Cankers should be identified and removed, including wood at least 10 cm below the
canker to reduce the chance of leaving some of the pathogen behind. Removing and
burning dead wood with fruiting bodies will also help reduce inoculum and should
decrease disease spread within the field (65).

Hand pruning is preferable to mechanical pruning because mechanical pruning
tends to increase the number of wounds, which increases the chance of infection (65).
Timing of pruning can also be used to decrease the chance of infection by pruning when
ascospore release is less likely, i.e. during dry, freezing periods in winter (9, 81). Several
studies have shown that pruning close to bud break is also effective in reducing infection,

bUt can be difficult when managing large vineyards (9, 60, 81).

18

One practice that has been adopted in the northeast United States to decrease the
damage of Eutypa dieback is training Vines by the double trunk system (57, 64). With this
system, each Vine is trained with two trunks up to the trellis. If one trunk is infected with
E. lata, it can be removed, leaving the other trunk and reducing the loss of productivity
that would result from removing the entire vine.

Another pruning technique that has been developed to decrease the potential of
infections of Eutypa dieback is the double pruning method. Double pruning involves
pruning vines first by non-selectively trimming canes to a uniform length, followed by a
second round of pruning down to the traditional two-bud spurs when Eutypa infections
are less likely to occur (82). While this technique should decrease the number of Eutypa
dieback infections, it has yet to be tested in a Vineyard with naturally occurring infections

(82).

Objectives

The first objective of this study is to evaluate and measure the spatio-temporal
spread of Eutypa dieback in Michigan ‘Concord’ vineyards. Information from the spatial
pattern of infected plants can help shape management strategies by improving our
understanding of disease spread within and among fields. Recently, an innovative
technique for evaluating spatial patterns, known as spatial analysis by distance indicas, or
SADIE (59), has been adapted for use in epidemiological plant disease studies. SADIE,
geostatistics, and several classical spatial analysis techniques will be used to look at the

pattern of disease spread over time for Eutypa dieback in Michigan Vineyards.

19

The second objective Of this study is to determine the effect of Eutypa dieback on the
yield Of ‘Concord’ grapevines in Michigan with the purpose of developing a crop loss
model for measuring and predicting yield loss. By developing a model for measuring and
predicting yield loss, we will improve the ability of producers to make decisions on the
best time to remove diseased vines.

Isolation from dead and diseased ‘Concord’ grapevines in Michigan has consistently
yielded two fungi, E. lata and Eutypella vitis (5). These fungi belong to the same family
(Diatrypaceae), and the appearance of perithecia and ascospores on wood is very similar.
Eutypella vitis was previously reported on a ‘Concord’ grapevine in Illinois (28), and the
US National Fungal Herbarium contains two specimens of El. vitis collected from Vitis
sp. in Lawton, MI and Paw Paw, MI. However, no reference was made to any disease
symptoms on the plant on which they were found. Therefore, the third objective of this
study was to develop a diagnostic PCR-based assay to detect and distinguish these two
fungi in infected wood. The characterization of the pathogenicity, morphology, and
secondary metabolite profile of El. vitis is the fourth goal of this research.

To further our understanding of the role of phytotoxins in disease development of
Eutypa dieback, either as pathogenicity or virulence factors, toxin-deficient mutants are
necessary. The fifih objective of this study is the development of a genetic transformation

system for E. lata for the purpose of generating toxin-deficient mutants.

20

10.

11.

12.

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27

CHAPTER 2. SPATIOTEMPORAL ANALYSIS OF EUTYPA DIEBACK INFECTION

IN MICHIGAN ‘CONCORD’ VINEYARDS.

Abstract

Eutypa dieback, caused by the ascomycete fungus Eutypa lata, affects grapevines
(Vitis spp.) worldwide and limits the longevity of Vitis labrusca ‘Concord’ Vineyards in
Michigan. The spatiotemporal relationship of eight vineyards in Michigan with vines
sowing symptoms of Eutypa dieback was studied using ordinary runs analysis, spatial
association by distance indices (SADIE), semivariance and Moran’s I. Symptomatic
Vines were aggregated in the east-west direction for 7 of the 8 vineyards by ordinary runs
analysis and 4 of the 8 vineyards by anisotropic Moran’s I analysis. Anisotropic Moran’s
I and ordinary runs analysis did not indicate exclusive, within row aggregation, reducing
the likelihood that pruning equipment plays a role in the dissemination of inoculum.
SADIE analysis indicated that 4 of the 8 vineyards had significant aggregation overall for
quadrat sizes of l, 3, and 9 vines. Moran’s I analysis found 4 of the 8 vineyards with
aggregated symptomatic vines. Semivariogram analysis found 3 of the 8 vineyards with
aggregated symptomatic vines. The SADIE association test indicates that inoculum was
being spread both within and between vineyards depending on year and Vineyard. The
findings of this study indicated that SADIE can be a valuable tool for analyzing the
spatial patterns of plant diseases and should be used in conjunction with other techniques,
such as ordinary runs analysis and Moran’s I, to better measure and interpret disease

epidemics.

28

Introduction:

Eutypa dieback, previously known as “dead-arm,” affects grapevines (Vitis spp.)
around the world, including the United States, Europe, Australia and South Afiica (7).
The disease is caused by the ascomycete fungus Eutypa lata Pers. Tul. & C. Tul (syn.
Eutypa armeniacae Hansf. & Carter) which infects vines when windbome ascospores
land on fresh pruning wounds and invade the xylem vessels (6). Two to four years later,
cankers become apparent around sites of infection on trunks and cordons (20, 24). At this
time, foliar symptoms can be observed early in the season and include yellowing and
cupping of newly emerged leaves, stunting of shoots with shortened intemodes, and
shedding of blossom clusters (17, 21). Vines decline slowly and eventually die (37). After
the bark weathers away from the cankered area, the fungus produces black stromata
containing numerous perithecia with infectious ascospores (23, 24). In grape-growing
regions receiving less than 500 mm of rainfall annually, perithecial development is rare
(5, 19, 30). In Michigan, perithecia are relatively common on infected and dead vines in
older ‘Concord’ vineyards with a history of Eutypa dieback. Spore trapping studies
conducted in Michigan vineyards indicate that ascospores are released throughout the
year, except during hot, dry periods in the summer (31).

The cultivar Concord is the most widely planted in Michigan with 3682 hectares,
which is nearly 73% of the entire grape planting, and more than 75% of ‘Concord’
plantings are over 30 years old (16), placing them at high risk for Eutypa dieback as
mature vines are more susceptible to infection (7).

Understanding the manner in which a pathogen spreads between plants in a field

and between fields is necessary in designing better control strategies. Information about a

29

pathogen’s movement among host plants is often necessary in determining the source,
type, and spread of inoculum. Much of this information can be gained from studying the
spatiotemporal dynamics of disease epidemics. In several studies using data from
California vineyards with Eutypa dieback, the presence of perithecia resulted in a higher
likelihood of spatial aggregation or “clumping” of diseased vines using the statistical
methods of ordinary runs, two-dimensional distance class, and spatial autocorrelation
(22), as well as fit to the logistic-normal-binomial distribution (15).

One of the most common methods for measuring spatial relationships is spatial
autocorrelation, which uses the distance between similar quadrants or individuals, and the
variance of this distance, to measure aggregation. One of the earliest measurements
developed using spatial autocorrelation is the Moran’s Index (I) (21). Moran’s I can range
in value from -1 to 1; a positive value indicates spatial relatedness or aggregation while a
negative value indicates a lack of spatial relatedness or dispersion. A value of zero
indicates a random pattern. Moran’s I values are often graphed by different lag intervals
(distances) to determine the extent of spatial relationships in a population.

Currently, one of the more popular measures of spatial autocorrelation is the
semivariogram, a plot of semivariance across different lag intervals or distances. The
semivariance is inverse to Moran’s I when assessing aggregation, i.e. the more densely
clustered the samples, the lower the semivariance value (y) with a minimum infinitely
close to 0, which is referred to as the nugget. The range of spatial autocorrelation is
typically a rising slope with a sill or plateau occurring at separation distances with
diminished spatial autocorrelation. The semivariogram is used in geostatistics in a

process called Kriging to develop maps of relative density. For Kriging, the

30

 

semivariogram serves as a model to interpolate values for areas that have not been
sampled and measured (18, 36).

Over the last decade, a new technique, spatial analysis by distance indices
(SADIE), has been developed to quantify spatial patterns (26, 27). The basis of SADIE is
to quantify the spatial pattern of a sampled population by measuring the total effort
expended in terms of the distance (D) individuals must move to an extreme arrangement,
such that they become as uniformly or regularly distributed as possible. The primary
statistic generated by SADIE is the index of aggregation (1,), defined as D/Ea where D is
the distance sampled individuals would need to move to become uniformly distributed,
and Eat is the mean distance to uniform distribution resulting from multiple permutations
or rearrangements of the data set of an equal number of individuals. A data set with
aggregated individuals would have an I, greater than 1, as the distance required for those
individual to move to uniformity would be greater than a random data set of same
number of individual. Based on the results of the permutations of the data, we can
determine the probability that D is significantly greater then E, which we can then use to
judge whether the population is significantly aggregated or not. The SADIE I, has
already been used in a number of studies to quantify the spatial properties of plant
diseases (2, 29, 33, 34).

A second set of statistics generated by SADIE is the mean clustering index (vi,vJ-),
which measures the degree to which an individual contributes to aggregation as a
member of a patch or cluster (Vi), or as a member of a gap (vj). These data can be used to
generate maps showing the relative aggregation or dispersion of mapped units, similar to

the density maps generated by spatial autocorrelation and Kriging. Clustering indices can

31

also be used by SADIE to determine the spatial association of two different populations
mapped to the same grid (28, 38). This is a promising tool for studying the spatial
relationships between different plant diseases and has already been used in one plant
disease study (29).

Scale is an important concept in studying the spatial properties of plant disease.
When aggregation is present at a specific scale, changing the quadrat size (sampling unit
size), can often change the degree of spatial dependence among the quadrats, altering
interpretation (4). The ideal scale, or unit of measure, for spatial studies on Eutypa
dieback of grapevine has not been determined. Munkvold et al. used quadrats of 9 vines
when assessing aggregation with spatial autocorrelation, but no other scale sizes were
tested (22). Information on the effect of scale size on the spatial relationships of infected
vines would be beneficial, not only for its own sake, but for future spatial studies as well.

The aim of this study was to evaluate SADIE for use in assessing the spatial
distribution of naturally infected ‘Concord’ grapevines in Michigan and compare its
usefulness with the more traditional spatial autocorrelation techniques, Moran’s I and
semivariogram analysis. Different quadrat sizes were analyzed to determine the
importance of scale on the spatial analysis techniques. Determination of aggregation
among and across rows was analyzed using ordinary runs analysis and anisotropic

Moran’s I analysis.

32

Materials and Methods:
Vineyards and data collection

Eight commercial ‘Concord’ vineyards in southwest Michigan were selected for
this study. All vineyards have been in production for over 30 years and are trained to the
Hudson River Umbrella training system with Vines 2.1 m apart in rows 2.4 m wide.
Vineyards A, C, D, E, G, and H have rows running east to west, while the rows in
vineyards B and F nm north to south. All Vineyards, with the exception of vineyard A,
are mechanically pruned in late winter or early spring, with vineyard A being hand-
pruned. Vineyard locations and sizes are presented in Table 2.1. Scouting for Eutypa
dieback symptoms took place in the spring when shoot length was approximately 25—30
cm. Vineyards A-C were scouted in the springs of 2003 through 2007 while vineyards D-
H were scouted in the springs of 2006 and 2007. Disease incidence was recorded for
every vine as a 0 (no symptoms) or a 1 (symptomatic for Eutypa dieback). The
expression of Eutypa dieback foliar symptoms has been reported to fluctuate between
years in Australia, France, and North America (3, 6, 8, 9, 32). In a 6-year study
conducted on ‘Shiraz’ grapevines in South Australia, variation in symptom expression
was influenced by climatic factors (32). For this study, vines that were asymptomatic
(lacking distinct foliar symptoms) following a previous designation as symptomatic were
counted as symptomatic in subsequent years to maintain continuity of the study. This
practice was also used in a previous study analyzing the spatial relationships of
grapevines infected with E. lata (22). Three different scale sizes were used in analyzing
spatial associations: 1 X 1 quadrats consisting of a single vine, 2 X 2 quadrats consisting

of 4 vines, and 3 X 3 quadrats consisting of 9 vines. Cluster sampling, in which data is

33

 

taken from a group or cluster of vines and then averaged to get a mean that is used for
analysis, was used to record incidence for 2 X 2 and 3 X 3 quadrats, resulting in mean
incidence values ranging from 0-4 and 0-9, respectively (14). To utilize the various
statistical programs, incidence data were entered as a grid with x coordinates describing

the rows, and the y coordinates describing the location of the vines within the rows.

34

Table 2.1. Location and size of Wm labrusca ‘Concord’ vineyards used in a study on
spatial relationships of Vines infected with E. lata. The total number of vines used for
each vineyard did not change when different quadrat sizes were analyzed.

 

 

 

Number of quadrats
Town and Number Number of

Vineyard countya of rows vines/row 1 X 1b 2 X 2° 3 X 3d

Vineyard A Baroda, 18 66 1188 297 132
Berrien

Vineyard B Lawton, 18 66 1188 297 132
Van Buren

Vineyard C Schoolcraf’t, 36 36 1296 324 144
Van Buren

Vineyard D Baroda, 18 48 864 216 96
Berrien

Vineyard E Baroda, 30 36 1080 270 120
Berrien

Vineyard F Scottdale, 24 42 1008 252 112
Berrien

Vineyard G Scottdale, 18 48 864 216 96
Berrien

Vineyard H Scottdale, 24 48 1152 288 128
Berrien

 

" Towns and counties are located in southwest Michigan

b l X 1 quadrats contain 1 vine, equal to the total number of vines per vineyard
° 2 X 2 quadrats contain 4 vines, 2 adjoining vines in 2 rows
d 3 X 3 quadrats contain 9 Vines, 3 adjoining vines in 3 rows

35

Ordinary runs analysis

Data from all vineyards and years were analyzed by ordinary runs to determine if
symptomatic vines were aggregated along or across rows. To perform ordinary runs
analysis, rows in either direction were combined with the last Vine of row i considered
contiguous to the first plant of row i + 1. A run was defined as a succession of one or
more healthy or infected vines followed or proceeded by the opposite infection state in an
ordered sequence (17). The number of expected runs is calculated by the following
equation:

E(U) = 1 + [2m(N-m)]/N

where E(U) is the expected number of runs, In is the number of infected or symptomatic
vines, and N is the total number of vines in the Vineyard. A Z-statistic (Z = [U-E(U)]/S(U)
where U is the observed number of runs and s(U) is the standard deviation) is used to
determine aggregation (P = 0.05). Ordinary runs analyses were performed in

Microsoft Excel (Microsoft, Redmond, WA).

Moran ’s I

The Moran's I statistic is a measure of autocorrelation that ranges between -1.0
and 1.0 depending on the degree and measure of correlation that is calculated. Moran’s

I is calculated using the following equation:

Ithl=N(h) >2 2 z. 2.4. / 2 2.4.2

36

 

where IQ.) is the autocorrelation for interval distance class h, zi is the measured sample

value (incidence) at point i, and zi+h is the measured sample value at point i+h. In this

study, we calculated Moran’s I for a number of lag distances to generate correlograms
for each data set. When assessing a Moran’s I correlogram, values greater than 0
indicate similarity of the measured pairs, in this case aggregation of symptomatic
vines. The closer these values get to 1, the greater the positive autocorrelation. A
Vineyard with symptomatic vines in clusters would have a Moran’s I correlogram with
a positive value at the shortest separation distance that decreases as separation distance
increases (a negative slope). A vineyard with a relatively random pattern of
symptomatic Vines would have no well defined shape, with values random around 0. In
this study, Moran’s I values were generated using the statistical program GS+ Version
7 (Gamma Design Software, Plainwell, MI). Isotropic and anisotropic (0° and 90°)
correlograms were generated using SigmaPlot Version 9.0 (Systat Software, Inc, Point

Richmond, CA).

Semivariograms

Semivariance is an autocorrelation statistic defined as:

7(h) = [1/2N(h)] 2 [Zi'zi+h]2

where y(h) is the semivariance for interval distance class h, zi is the measured sample

value (incidence) at point i, 2%, is the measured sample value at point i+h, and N(h) is

37

the total number of sample couples for the lag interval h. Semivariance was calculated
using the statistical program GS+ Version 7. Isotropic semivariograms were constructed

using SigmaPlot Version 9.0.

SADIE

SADIE analysis was conducted on data from each Vineyard for all three quadrate
sizes using SADIESheIl version 1.22 (provided by J. N. Perry of Rothamsted Research,
England, UK) with 5967 randomizations to determine significance (P S 0.1, P S 0.05).
To determine if newly symptomatic vines were spatially associated with previously
symptomatic vines, we used the SADIE association test. Cluster indices of each vineyard
with newly symptomatic vines were compared to cluster indices generated from the

previous year using the index, xk, where:

M? N(ZkI—CIIXZkz—(h) / [2k(zkl_91)2 231<(Zk2"C12)2] m

where 2],. is the clustering index for the kth sampling unit (k = 1, ..., N) for newly

symptomatic vines and those from the previous year, and q1 and q; are the respective
means over the N sampling units. Overall spatiotemporal association between newly
symptomatic vines and those fi'om the previous year was then calculated as the mean of

the local values:

X=Xi Xi /N

Significance of ‘X’ was tested by randomizations with values of 2;, after allowance for

small-scale spatial autocorrelation in 2;, from either population (1 1).

38

Linear regression goodness of fit

Linear regression analysis was used to compare SADIE analysis to the spatial
autocorrelation techniques, Moran’s I and semivariance. The first lag distance for each
measure of autocorrelation was compared to the SADIE Ia value for each quadrat size for

all vineyards and years. Linear regression was performed using SigmaPlot Version 9.0.

39

Results:
Disease incidence

Disease incidence increased over the four year period in Vineyards A, B, & C
(Figure 2.1), and across the two years for vineyards D through H (Table 2.2). Maps of
diseased vines in each vineyard over time can be found in Appendix A (Figures A.1-8).
The degree and orientation of aggregation are not easily discerned from these maps
making statistical analyses necessary. I will utilize four types of analyses, Ordinary
Runs, Moran’s I, Semivariance, and SADIE, on these data and will conclude with a

comparison of the four techniques.

40

 

25

 

 

 

 

 

 

 

+ \fineyardA
-----O VineyardB
A -1— Vineyard C
8 20 -
.E
>
.2
”S
2 15 -
o.
E.
in
5 1o -
8
c
a)
23
s 5 ‘
o I l l l T
2003 2004 2005 2006 2007
Year

Figure 2.1. Disease progress curves of vines symptomatic for Eutypa dieback in Vitis
labrusca ‘Concord’ vineyards A, B, and C fi'om 2003 through 2007. Incidence is
presented as the percentage of vines symptomatic for Eutypa dieback from the total vines
in each Vineyard.

41

Table 2.2. Incidence of Vines symptomatic for Eutypa dieback for Vitis labrusca
‘Concord’ vineyards by year.

 

Disease incidencea

 

 

Vineyard 2006 ‘ 2007

Vineyard D 23.5% 28.6%
Vineyard E 13.7% 17.4%
Vineyard F 14.9% 16.7%
Vineyard G 21.4% 22.3%
Vineyard H 14.8% 17.4%

 

8‘ Incidence presented as the percentage of vines symptomatic from the total number of
vines

42

Ordinary runs analysis

Ordinary runs analyses detected significant aggregation of disease within rows in
six of the eight vineyards (Table 2.3). However, aggregation was not found consistently
across years. This can be seen in Vineyards A and C where significant clumping was
detected in two or three years, respectively, of the five years surveyed. Significant
aggregation across rows was less common but more consistent across years. Significant
aggregation across rows was found for all years in vineyards, B, C, and E, while no cross
row aggregation was found in vineyards D, F, G and H. Vineyard A displayed the only
variable across row pattern, with disease being significantly aggregated in 2004, but not
in other years. All vineyards except F displayed some aggregation along an East-West
direction. North-South aggregations were less common, being found only in vineyards A

(one year only), C and E.

43

 

Table 2.3. Ordinary rims analysis by Vineyard and year to test for spatial aggregation
within and across rows for vines symptomatic for Eutypa dieback in Vitis labrusca
‘Concord’ vineyards in Southwest Michigan.

 

 

Vineyard/ Expected Observed runs Z-Statistic Observed runs Z Statistic
Year runs within rows within rows“ across rows across rowsa
Vineyard A East-WesTtb North-SouthF
2003 130.98 128 -0.79 130 -0.26
2004 189.14 182 -1.31 176 -2.41*
2005 274.97 262 -1.63"‘c 263 -1.51
2006 353.18 332 -2.07* 338 -1.49
2007 367.01 356 -1.04 352 -1.42
Vineyard B North-South” East-Westb
2003 163.96 163 -0.20 153 -2.32*
2004 185.83 185 -0.15 177 -1.65*
2005 210.35 205 -0.88 198 -2.04*
2006 299.44 299 -0.05 276 -2.71 *
2007 307.85 306 -0.21 293 -1.67*
Vineyard C East-Westb North-Southb
2003 76.54 71 -2.71* 71 -2.71 *
2004 113.37 109 -1.49 105 -2.77*
2005 152.38 145 -I .87* 142 -2.59*
2006 281.06 269 -I .79* 262 -2.68*
2007 295.68 289 -O.80 273 -2.71*
Vineyard D East-Westb North-Southb
2006 311.61 287 -2.33* 307 -0.44
2007 353.78 322 -2.65* 353 -0.06
Vineyard E East-Westb North-South"
2006 259.87 248 -1.53 237 -2.95*
2007 317.09 278 -4.67* 280 -4.43*
Vineyard F North-South" East-Westb
2006 256.36 249 -0.92 255 -0.17
2007 281.00 275 -0.68 279 -0.22
Vineyard G East-Westb North-Southb
2006 291.78 260 -3 .22* 289 -O.28
2007 300.78 277 -2.34* 294 -0.67
Vineyard H East-Westb North-Southb
2006 290.83 265 -3 .03 "‘ 290 -0. 10
2007 331.57 309 -2.32* 329 -0.26

 

a Z-statistic = (observed number of runs — expected number Of runs)/ standard deviation
of the observed number of runs.

b Direction of rows in the analysis in either the East-West or North-South direction

° Significant aggregation when Z values are less than -1.6 (P=0.05).

44

Isotropic Moran ’s I

Correlograms from isotropic Moran’s I analyses of single vines as a focus and
comparing to vines within and among rows at increasing distance from the source, found
that all Vineyards displayed some aggregation at the level of nearest neighbors (Figure
2.2). However, vineyards differed in the rate that aggregation decreased with distance.
Correlograms touched or crossed the zero line, which indicates a lack of aggregation,
within 10 meters in vineyards A, B, F and H. Much larger spatial aggregations were
detected in vineyards C and E with correlograms not crossing the zero line until 20 to 30
meters fi'om the source vine. While correlograms were similar across years in most
vineyards, Vineyards D and G displayed less aggregation in 2006 compared to 2007. In
both vineyards the correlogram dipped to the zero line within 10 meters in 2006, but
displayed slightly larger scale spatial aggregation in 2007.

Correlograms at the 2 X 2 (Appendix A, Figure A9) and 3 X 3 (Appendix A,
Figure A.10) quadrats did not display any consistent pattern among vineyards. No spatial
aggregation was found for these larger scale quadrats in vineyards A, B and H, while
significant large scale aggregation extending out for 10 to 30 meters was evident in
vineyards C, E and F. Vineyards D and G displayed complex patterns, especially for the
2 X 2 quadrat size. Significant spikes of aggregation were found in these two vineyards

at 10 and 25 meters, respectively.

45

0.04

 

A —O— 2003
.. . o. . ... 2004
0.03 - -—+-- 2005

 

 

0.02 -

Moran's I
.o
S
l

0.00 -

-0.01 4

 

 

 

0.02 I I I T I l
0 10 20 30 40 50 60 70 80

 

Separation distance (m)

 

 

 

 

 

 

 

0.10
B —c—— 2003
... . o. .. 2004
0.08 - —--v--— 2005
_. —A-—- - 2006
cool — a — 2007
_ 0.04 4
.0)
C
g 0.02 -
E
0.00 -
-0.02 -
-0.04l
0 20 40 60 80

Separation distance (m)
Figure 2.2. Isotropic Moran’s I correlograms derived from vines symptomatic for Eutypa
dieback in Vitis labrusca ‘Concord’ vineyards in Souhwest Michigan. Moran’s I values
are plotted by separation distances of symptomatic Vines in 1 X 1 quadrats for each year
of the study. A) Vineyard A, B) Vineyard B, C) Vineyard C, D) Vineyard D, E)
Vineyard E, F) Vineyard F, G) Vineyard G, H) Vineyard H.

46

(108

 

(106 . '

 

2003
2004
2005
2006
2007

 

0.04‘ \Rx. o

(102 a

Moran's I
/
\ '.

. " \
k. ' ' " . *f A .'\. .‘..
(100 TEL .' ‘..

 

 

-4102 T I I

 

 

 

Separation distance (m)

(105

 

OTPI‘

OIIB-

(102 -

(101 -

Moran's I

(100

 

 

 

4101 -

4102‘-

 

 

 

4103 u I u
0 10 20 30 40

Separation distance (m)

Figure 2.2. (cont’d).

47

50

 

60

0.10

 

0.08 a

0.06 -

0.04 -

Moran’s l

0.02 -

0.00 -

 

 

+ 2006
-- O-- 2007

 

 

 

 

0 1 0 20 30 40

Separation distance (m)

0.04

50 60

 

0.03 -

0.02 a

0.01 -

Moran's I

0.00 .

+ 2006
"O - 2007

 

 

 

-0.01 ~

 

-0.02 I

 

 

 

Separation distance (m)

Figure 2.2. (cont’d).

48

50 60

(104

 

(i -fil—-2006
' - -o - 2007
0.03 -

 

 

(102 4

DIN .

 

Moran's l

(100

 

4101 .

 

4102 -

 

'0.03 Ti I I r r
0 10 20 30 40 50 60

Separation distance (m)

 

(103

H + 2006
~ 0 - 2007

 

 

 

(102 -

OLH -

Moran's I

(100

 

 

-0.01 -

 

 

4102 u

 

 

Separation distance (m)

Figure 2.2. (cont’d).

49

 

Anisotropic Moran ’s I

Aggregation pattern was also analyzed within and across rows to determine if
spread away from an infected Vine was asymmetrical. No difference in aggregation
within versus across rows was detected in vineyards A, B, and C (Figure 2.3). In
contrast, vineyards F, G and H displayed higher within row aggregation compared to
across rows (Fig 2.4). These six vineyards displayed very similar trends across all years
monitored (Data not shown). In vineyard D the correlogram displayed high aggregation
within row at 2 meters and another spike at approximately 10 meters (Figure 2.5).
Vineyard E displayed a pattern of low aggregation within a row at distances less than 2
meters followed by an aggregation spike between 2 and 10 meters (Figure 2.6). This

pattern was more pronounced in 2007 than in 2006.

50

0.08

A + Within rows. East-West

-- - v - Across rows. North-South
0.06 -

 

 

 

0.04 -

0.02 -

Moran's I

 

 

 

 

o 20 40 60 80
Separation distance (m)

0.08

 

+ Along rows, North-South
O 06 _ - V- Across rows, East-West

 

 

0.04 -

0.02 -*

 

Moran's I
O
8
l

 

-0.02 -

-o.04 -

-0.06 .

 

 

 

-0.08 r 1 r
0 20 40 60 80

Separation distance (m)

Figure 2.3. Anisotropic Moran’s I correlograms derived from vines symptomatic for
Eutypa dieback in Vitis labrusca ‘Concord’ vineyards in Souhwest Michigan. Moran’s I
values are plotted by separation distances of symptomatic vines in 1 X l quadrats for
2003. A) Vineyard A, B) Vineyard B, C) Vineyard C.

51

0.10

 

 

+ Within rows, East-West
- v-- Across rows. North-South

 

0.08 -

 

0.06 -

0.04 -

Moran's l

0.02 -

 

0.00

 

-0.02 -I

 

 

-0.04 r u r
0 1 0 20 30 40 50 60 70

 

Separation distance (m)

Figure 2.3. (cont’d)

52

0.10

A + Within rows, North-South
0.08 - - v ~ Across rows, East-West

 

 

 

0.06 J
0.04 -
0.02 -

0.00

 

Moran’s I
<1

-0.02 ‘

 

-0.04 -

~0.06 -

 

 

 

'0.08 I I I I I
0 1 0 20 30 40 50 60

Separation distance (m)

0.12

 

+ Within rows, East-West
- v-- Across rows. North-South

 

0.10 -

 

 

0.08 ~

0.06 -

0.04 -

0.02 -

Moran's I

0.00

 

 

-0.02 -

-0.04 a

 

 

 

’0.06 I I I I I

Separation distance (m)

Figure 2.4. Anisotropic Moran’s I correlograms from derived from vines symptomatic for
Eutypa dieback in Vitis labrusca ‘Concord’ vineyards in Souhwest Michigan. Moran’s I
values are plotted by separation distances of symptomatic vines in 1 X 1 quadrats for
2003. A) Vineyard F, B) Vineyard G, C) Vineyard H.

53

 

 

 

 

 

 

 

 

 

 

C T + Within rows, East-West
- v-- Across rows. North-South
0.10 -
7a -
.c 0.05
I!
O
E
0.00
-0.05 - V
0 10 20 30 4O 50 60 70

Separation distance (m)

Figure 2.4. (cont’d)

54

 

 

 

 

 

 

 

 

+ Along rows, East-West
-- V - Across rows. North-South
0.10 -
— 0.05 -
JD
:
i!
o
2
0.00
-0.05 - ‘v
0 10 20 30 40 50 60

Separation distance (m)

Figure 2.5. Anisotropic Moran’s I correlogram from Vitis labrusca ‘Concord’ vineyard D
in Souhwest Michigan. Moran’s I values are plotted by separation distances of
symptomatic vines in 1 X 1 quadrats for 2006.

55

 

+ Vifithin rows, East-West

 

 

 

 

 

 

 

 

 

 

 

 

 

A ~ v-r Across rows, North-South
0.10 -
— 0.05 -
(I)
’r:
E
o
E
0.00
-0.05 ~
0 10 20 30 4O 50 60
Separation distance (m)
r115 - + Within rom. East-West
B -v-- Across rows, North-South
0.10 -
rn
-: 0.05 -
g -.
. Y
2 . . v" .V
0.00 .VV “‘7 '.‘v
v
-0.05 -
0 10 20 30 4o 50 60

Separation distance (m)

Figure 2.6. Anisotropic Moran’s I correlograms from Vitis labrusca ‘Concord’ Vineyard
E in Souhwest Michigan. Moran’s I values are plotted by separation distances of
symptomatic vines in 1 X 1 quadrats. A) 2006, B) 2007.

56

Semivariance

Semivariograms for all vineyards except D and E did not reveal any definite trend
for aggregation within a Vineyard (Data not shown). Semivariance increased over time
in nearly all vineyards, but this was due to an increase in disease incidence. When
calculating semivariance, an increase in paired, sample couples (symptomatic, paired
Vines) increases semivariance. Within Vineyards D and E, a sill was reached at ~17 and
10 m, respectively, indicating some aggregation within these Vineyards. There was no
well defined sill in vineyard D when the analysis utilized either the 2 X 2 or 3X 3 quadrat
size (Appendix A, Figure A.11). Semivariograms for the 2 X 2 and 3 X 3 quadrats

suggested a sill at approximately 25 m (Appendix A, Figure A.12).

57

SADIE analyses

SADIE analyses detected significant and strong aggregations over time in
vineyards C, E and F, and somewhat weaker aggregation in vineyard D (Table 2.3).
Random patterns of disease incidence were found in vineyards A, B, G and H. SADIE
association tests were conducted to investigate the spatiotemporal pattern of new
infections within a vineyard. This test uses the pattern found in one year as a baseline of
infection, then determines whether new infections are aggregated to existing diseased
vines. Single vine data (i.e., 1 X l quadrats) were too large to process with the most
current version of SADIEShell. In consequence, data are analyzed using only 2 X 2 and
3 X 3 quadrat sizes. No consistent pattern was found among the eight vineyards.
Overdispersed patterns of new infections were detected in vineyards D and F, while
largely random patterns were found in Vineyards A and G (Table 2.4). The remaining
vineyards did not display any consistent pattern across either time or quadrat size.
Aggregated patterns were detected in some years in vineyard C, with other years
displaying a random pattern. In Vineyard B, all patterns were random at the 2 X 2
quadrat size, while there was significant aggregation in some years with the 3 X 3
quadrats. The spatial pattern differed with quadrat size in vineyards E and H over the one
year tested. In vineyard E the new infections were aggregated for the 2 x 2 quadrat size,
but were random when analyzed as 3 X 3 quadrats. New infections in vineyard H

displayed a regular pattern at the 2 X 2 scale, but random at the 3 X 3.

58

Table 2.4. Determination of aggregation of vines with foliar symptoms of Eutypa dieback
from Vitis labrusca ‘Concord’ vineyards in Souhwest Michigan using indices of
aggregation (1,) measured for different quadrat sizes for each vineyard and year using
SADIE. SADIE Ia values greater than 1 signify aggregation of symptomatic vines.

 

 

 

 

SADIE Ia

Quadrat size 2003 2004 2005 2006 2007
Vineyard A

1X1‘al 1.272 1.110 1.104 0.941 0.849

2 X 2b 1.239 1.083 1.072 0.899 0.829

3 X 3° 1.191 1.079 1.063 0.878 0.783
Vineyard B

1 X 1 1.031 0.960 0.992 1.243 1.187

2 X 2 1.042 0.897 0.937 1.145 1.147

3 X 3 1.025 0.921 0.940 1.108 1.081
Vineyard C

1 X 1 2.351"""d 2.594" 2.604" 2.271“ 2.189M

2 X 2 2.238M 2.523” 2.503" 2.164“ 2.089"

3 X 3 1.947” 2.209" 2.205” 2.082" 2.037"
Vineyard D

1 x 1 f - - 1.360“ 1272*d

2 X 2 - - - 1.378“ 1.304*

3 X 3 - - - 1280* 1256*
Vineyard E

1 X 1 - - - 1.792“ 2.030M

2X2 - - - 1.710** 1.879"

3 X 3 - - - 1.678” 1.737"
Vineyard F

1 X 1 - - - 1.346“ 1.302"

2X2 - - - 1.310** 1.323"

3 X 3 - - - 1.330* 1.294"
Vineyard G

1 X 1 - - - 1.029 1.037

2 X 2 - - - 0.990 0.967

3 X 3 - - - 0.955 0.977
Vineyard H -

1 X 1 - - - 1.071 0.973

2 X 2 - - - 1.054 0.952

3 X 3 - - - 1.070 0.988
" Quadrat consisting of an individual vines.
b Quadrat consisting of 4 Vines (2 vines wide by 2 vines long).
° Quadrat consisting of 9 vines (3 vines wide by 3 vines long).
d Significant aggregation (p50.05) determined by 5967 permutations.
: Significant aggregation (p501) determined by 5967 permutations.

59

Indicates that no data was collected for that year.

Table 2.5. SADIE association analyses of overall spatial association between cluster
indices of newly symptomatic vines and previously symptomatic vines for all vineyards
for vines arranged in 2 X 2 and 3 X 3 quadrats.

 

SADIE association testQQ

 

 

Qgg‘em 0304‘ 0405b 0506° 0607d 03(04-07)° 04(0507)‘ 05(06-07)‘
vineyard A
2 x 2h 0.0323 0.0920 0.1035 0.0407 0.0121 0.1355 , -0.0690
3 x 3' 0.0316 0.0755 0.1122 -0.0654 0.0336 0.0998” 0.0591
vineyard B
2 x 2 0.0697 0.0124 0.0970 0.0399 0.0241 0.0126 0.1031
3 x 3 0.0160 0.0988 0.1987 0.0184 0.1680 0.185*“ 0.2325*
vineyard C
2 x 2 0.1486" 0.1874“ 0.0566 -0.1088 0.0882 0.0555 0.0382
3 x 3 02108" 0.1773* 0.0631 0.1752 0.1298 0.0460 0.0097
vineyard D .
2 x 2 J - - 0.1704“ - - -
3 x 3 - - - 0.2399” - - -
vineyard E
2 x 2 - - - 0.2009* - - -
3 x 3 - - - 0.0944 - - -
vineyard F
2 x 2 - - - 0.1704“ - - -
3 x 3 - - - 0.2399“ - - -
vineyard G
2 x 2 - - - 0.1272 - - -
3 x 3 - - - 0.1277 - - -
vineyard H
2 x 2 - - - 0. 2079“ - - -
3 x 3 - - 0.1322 - - -

 

a Data set from 2004 compared to the 2003 data set

b Data set from 2005 compared to the 2004 data set

° dData set from 2006 compared to the 2005 data set
“Data set fi'om 2007 compared to the 2006 data set

° Data sets from 2004-2007 combined and compared to the 2003 data set
Data sets from 2005-2007 combined and compared to the 2004 data set

: Data sets from 2006-2007 combined and compared to the 2005 data set
hQuadrat consisting of 4 vines (2 vines wide by 2 vines long).

' IQuadrat consisting of 9 vines (3 Vines wide by 3 vines long).

I Indicated that no data was collected.

k Significant association (P<0. 025).

' Significant dissociation (P>0. 975).

60

Regression goodness of fit

Linear regression plots of SADIE Ia values with values for the first separation
distance for Moran’s I and semivariance. Correlations were positive and relatively strong
when comparing SADIE values to Moran’s I (Figure 2.7). A negative relationship was
found when SADIE values were compared to semivariance (Fig. 2.8). The strength of
the correlations was also much weaker when semivariance was compared to SADIE. The
relationships did not change across the three quadrat sizes. Although the goodness of fit

value (r2) for Moran’s I and 2 X 2 quadrats were very high.

61

0.10
008 , r2=0.60

 

0.06 -

Moran's I

0.04 -

0.02 -

 

 

 

0.00 I I I I I I I I I
0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8

SADIE Ia

 

0.25

 

B -
02° r2=0.93 o

0.15 -

O
0.10 4 O

Moran's I

_ C
0.05 .

0.00 -

 

-0-05 I I I

 

 

I I I I I

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8
SADIE Ia

 

O

Moran's I

 

 

 

 

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4
SADIE Ia

Figure 2.7. Correlation between SADIE Ia and Moran’s I for the first separation distance
(lag interval) between vines with foliar symptoms of Eutypa dieback in Vitis labrusca
‘Concord’ Vineyards in Souhwest Michigan. The first separation distance for all

vineyards and years were used in the analyses. A) 1 X 1 quadrat size, B) 2 X 2 quadrat
size, C) 3 X 3 quadrat size.

62

 

0.20 j o r2=0.26

Semivariance

 

 

 

 

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8
SADIE la

 

 

 

 

 

 

 

 

 

 

1.0

B
#028

8
C
(B
'C
.8
E
0
(D

0.0 l I I I l I l I I I

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4 2.6 2.8
SADIE la

2.0
C A ‘9 o

"8 , o . r2=0.29
8 .0
.5 c
5
.2
E
d)
(O

0.0 ‘I I I I I l I I

0.6 0.8 1.0 1.2 1.4 1.6 1.8 2.0 2.2 2.4

SADIE la

Figure 2.8. Correlation between SADIE Ia and semivariance for the first separation
distance (lag interval) between vines with foliar symptoms of Eutypa dieback in Vitis
labrusca ‘Concord’ vineyards in Souhwest Michigan. The first separation distance for all
Vineyards and years were used in the analyses. A) l X 1 quadrat size, B) 2 X 2 quadrat
size, C) 3 X 3 quadrat size.

63

Discussion

The spatial pattern of ‘Concord’ grapevines displaying typical foliar Eutypa
dieback symptoms was determined for eight vineyards over the course of two or five
years. A combination of spatial analysis techniques was used to determine if symptomatic
vines were aggregated or random. An aggregated pattern of symptomatic vines would
indicate that inoculum spread within a vineyard, most likely from infected vines with
perithecia releasing ascospores. Other explanations for aggregated vines would include
the transmission of E. lata by insect or animal vectors, which have not previously been
implicated in the spread. Mechanical spread via pruning equipment is a more likely
alternative explanation for pathogen spread. A random pattern of symptomatic vines
would indicate that inoculum, in the form of wind-dispersed ascospores, came from
outside the vineyard and was deposited at random throughout the vineyard (22, 35).

When grapevines are pruned, either by hand or mechanically, the direction of
pruning is typically within the row. If inoculum was spread by pruning equipment, we
would expect to see a pattern of aggregation primarily within the row. The latter is not
the case as indicated by the results of the ordinary nms analysis and anisotropic Moran’s
I analysis. By ordinary runs analysis, six of the eight vineyards had significant
aggregation of vines within rows, but half of the vineyards showed significant
aggregation across rows as well (Table 2.3). Anisotropic Moran’s I analysis of the
vineyards produced similar results with no predilection for either across- or within-row
spread. Munkvold et al. also found no evidence that pruning equipment was involved in
disease spread based on the patterns of vines symptomatic for Eutypa dieback in

California vineyards (22). The results of this study indicate that spread of inoculum is

occurring within vineyards, reinforcing the importance of sanitation for management of
Eutypa dieback.

A stronger relationship exists with the direction of the rows. With the exception of
vineyard H, all of the vineyards had significant aggregation, either along or across the
rows, in the East-West direction by ordinary runs analysis. Only three vineyards were
aggregated in the north-south direction. Anisotropic Moran’s I analyses indicated that
four of the vineyards (A, D, G, H) had symptomatic vine aggregation in the East-West
direction while only vineyard F had aggregation in the North-South direction. The likely
explanation for this is the direction of prevailing winds, which are predominantly out of
the west; westerly winds would favor the spread of wind dispersed inoculum in that
direction and create aggregated vines in the east-west direction. Fluctuations in the wind
direction, especially when inoculum is present, and inoculum coming fi'om outside a
vineyard are both plausible explanations for the inconsistency in our results.

There was significant aggregation in half of the vineyards shown by SADIE 1,. Of
these four vineyards, vineyard A had the highest 11, values in all of the years studied.
Aside from the aggregation of symptomatic vines, the other reason for such high 1. values
is the location of aggregated vines. Symptomatic Vines in vineyard C are predominantly
on the edges of the vineyard, as can be seen by plotting the number of symptomatic vines
by row location (Figure 2.9). This edge effect causes an increase in 1, values because
groups of symptomatic vines on the edge of a vineyard will have higher Ia values than
groups in the center (25, 39, 41). SADIE determines aggregation by looking at the
distance that data points must move to reach uniformity. It requires a greater distance of

movement to move vines at the edge to a uniform pattern as opposed to moving Vines in

65

the center to a uniform pattern. Xu and Madden, using artificially generated data sets to
study the interrelationships of SADIE statistics, found that distance to regularity for
individual observed counts was location-dependent but did not affect results when groups
were moved, except when located in the comer of sampling grids (40). They further
recommended the creation of a new-scaled index for each count x location combination

to mitigate the edge-effect.

 

+ Within rows (East to West)
- -O - Across rows (South to North)

 

 

Number of symptomatic vines

 

 

 

Row number
Figure 2.9 The number of Vines with foliar symptoms of Eutypa dieback within and
across rows for vineyard A in 2003 showing a greater number of symptomatic vines on
the South, East, and West edges of the vineyard. Vineyard A is a commercial Vitis
labrusca ‘Concord’ vineyard located in Souhwest Michigan

66

Spatial analysis by distance indices and the two measures of spatial
autocorrelation, semivariance and Moran’s I, were in agreement when assessing the
amount of spatial aggregation for most of the vineyards. While SADIE and spatial
autocorrelation use different techniques to measure spatial aggregation, Xu and Madden
have found that SADIE’s I, and spatial autocorrelation measure some common properties
of patterns (39). In this study, the SADIE Ia share a greater similarity with Moran’s I than
with semivariance. For this study, semivariance was not as useful as either Moran’s I and
SADIE’s In due to the difficulty in interpreting semivariograms. The advantage that
semivariance and semivariograms provide is the ability to create an empirical model to
estimate the value of units in locations that have not been measured so that contour maps
can be generated through Kriging. In this study, the smallest reasonable unit of measure,
individual vines, was completely assessed for all data sets, eliminating the need to fill in
missing data. Also, SADIE’s clustering indices can be used to generate contour maps that
display the spatial relationships of mapped units. This is an advantage over Kriging,
which only show the values of the measured unit, in this case, disease incidence. With
SADIE cluster indices contour maps, the regions of significant clustering (spatial
aggregation) can be seen as well as regions of significant gapping, a property that is more
useful than Kriging when assessing spatial relationships.

The SADIE association analysis was conducted for the 2 X 2 and 3 X 3 quadrats
for all vineyards to look at the spatial relationship of newly symptomatic vines to known
infected vines. Significant association occurred when Ia values increased from a previous

year and significant disassociation typically occurred when Ia values decreased.

67

Association would indicate that newly symptomatic vines were infected by inoculum
from nearby vines. Disassociation would indicate that newly symptomatic vines were
inoculated from distant sources, likely outside the Vineyard. The pattern of association
and disassociation in vineyards with aggregated symptomatic vines implied that
infections were occurring from inoculum from both within and outside the vineyard. In
some years, high levels of inoculum might be released in a vineyard due to a rain event
right after pruning. This would likely result in a pattern of aggregation as inoculum levels
would be greatest near diseased vines. In other years, there might not be a rain event after
vines have been pruned and are susceptible, but a rain event away from the Vineyard
might release inoculum from a distant source, some of which might find its way to the
vineyard. These conditions would result in alternating patterns of new infections; some
years they might be aggregated around symptomatic Vines producing inoculum while in
other years, they might show a random pattern. This is not an unlikely explanation for the
variation in aggregation among vineyards, especially given the large acreage of old
‘Concord’ plantings in Southwest Michigan that could serve as sources of inoculum.

To the best of our knowledge, this is the first time the SADIE association test has
been used studying the spatiotemporal progression of a plant disease. A previous study
used the SADIE association analysis to determine if three different viruses were spatially
linked (29), but did not look at a single pathogen or host over time. The SADIE
association test is a promising tool for studying the spatiotemporal aspects of plant
diseases.

In ecological studies, it has established that observation scales can influence

inference (13). Parameters used to make inferences about spatial relationships are all

68

dependent on the size and shape of sampling units. Altering quadrat size in this study did
not have a dramatic effect on the results of SADIE 1,, but did alter the shape of Moran’s I
correlograms or semivariograms for several of the vineyards (E, F, and G). Most of the
symptomatic vine clusters in the study were small, consisting of only 2 or 3 vines. As
quadrat sizes increased to include four or nine vines, these small clusters were grouped
together and appeared as single, large clusters. Moran’s I correlograms and
semivarigrams change when increasing sampling unit size (1). The SADIE Ia value is a
global measure of aggregation and would therefore be less susceptible to changes in
quadrat sizes. In this study, directional, or anisotropic, analysis was conducted with only
the 1 X l quadrat size. Certain shapes of quadrats can create the impression of significant
anisotropy that is not actually present (10, 12), therefore the smallest sampling unit was
chosen.

Certain dangers exist when exploring the spatial patterns of vines symptomatic for
Eutypa dieback, especially in Michigan. Anecdotal evidence indicates that Eutypa
dieback expression is variable from year to year (3). When scouting vines for symptoms,
Vines that are infected but asymptomatic would be inadvertently left out of the study.
Also, recent infections do not become symptomatic for several years. Another problem is
the presence of dead vines in vineyards. Since we have no way of confirming that dead
vines in the vineyard died from Eutypa dieback, the impact of dead Vines on spatial
relationships is ignored. This necessitates long-term studies to get the best representation
of the condition of Eutypa dieback in any given vineyard. Ideally, vineyards that are just
beginning to show symptoms would need to be studied for several decades to get the best

understanding of spatial relationships and the spread of the disease.

69

One disadvantage of SADIE’s I8 is the inability to measure aggregation
directionally. This drawback of SADIE necessitates using some other technique, such as
ordinary runs analysis or anisotropic Moran’s I, to fully analyze and explore the
spatiotemporal relationships of plant diseases. The findings of this study indicated that
SADIE can be a valuable tool for analyzing the spatial patterns of plant diseases and
should be used in conjunction with other techniques, such as spatial autocorrelation, to

better measure and interpret disease epidemics.

70

10.

11.

12.

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74

 

CHAPTER 3: EFFECT OF EUTYPA DIEBACK YIELD ON ‘CONCORD’

GRAPEVINES IN MICHIGAN WITH EVALUATION OF CROP LOSS MODELS.

Abstract

The effect of Eutypa dieback on the yield of ‘Concord’ grapevines in MI was
studied and crop loss models were developed. Data from selected vines (n=421) were
collected from three commercial vineyards with natural Eutypa infections for three years.
There was a direct correlation between disease severity and yield. Significant
relationships were detected between disease severity and the number of shoots and
clusters. Yield loss in symptomatic vines can be explained partially by a reduction in the
number of shoots and the number of clusters on symptomatic vines.

Models were developed for individual vineyards by year. A cumulative model, using
all of the data collected from the study along with the number of shoots included, as well
as a ‘quick’ model, using only the measure of severity, were developed for each measure
of severity. When using the percent symptomatic shoots as the measure of severity, the
regression coefficient (R2) for the individual vineyard models ranged from 0.50 to 0.86,
while the R2 for the cumulative model was 0.66, and for the quick model was 0.60. When
using the disease severity scale, the goodness of fit for the individual vineyard models
ranged fiom 0.59 to 0.90 while the cumulative model was 0.67 and the quick model was
0.58. The models developed from this study were combined with economic data to

produce cost-benefit-analysis tools to aid producers in making management decisions.

75

Introduction

Eutypa dieback, previously known as “dead-arm”, affects grapevines (Vitis spp.)
around the world, including the United States, Europe, Australia and South Africa (15).
The disease is caused by the ascomycete fungus Eutypa lata Pers. Tul. & C. Tul (syn.
Eutypa armeniacae Hansf. & Carter), which is also a pathogen of other woody plants,
such as almond, apricot, cherry, olive, peach, and walnut (3, 12, 15).

Infection takes place when windbome ascospores of the fungus land on fresh
pruning cuts and invade the xylem vessels. Two to four years later, cankers become
apparent around pruning cuts on trunks and cordons (12, 15). Foliar symptoms are
typically observed early in the season and include yellowing and cupping of newly
emerged leaves, stunting of shoots due to shortened intemodes, and shedding of blossom
clusters (12, 15 ). Symptoms are attributed to one or more toxins produced by the fungus
in the wood tissue (6, 13, 18). In most grape-growing regions of the world, an infected
vine progressively declines and, without removal of the canker, will die (20).
Observations in Michigan indicate that expression of disease symptoms can vary fiom
one year to the next, with some vines exhibiting symptoms for several years followed by
a year or more of no symptom expression (2). The expression of foliar symptoms has
been reported to fluctuate between years in Australia and France (5, 7, 17). In a 6-year
study conducted on ‘Shiraz’ grapevines in South Australia, variation in symptom
expression was influenced by climatic factors (17). A number of possible relationships
were identified between symptom expression and climate including; increased symptom

expression after increased winter rainfall 18 months prior, decreased disease incidence

76

when spring temperatures were warmer, and a reduction in disease incidence when
rainfall was either very high or very low in October (spring) (17).

The cultivar Concord is the most widely planted grape in Michigan with 3682
hectares, nearly 73% of the entire grape planting. More than 75% of ‘Concord’ plantings
are over thirty years old (11), placing them at high risk for Eutypa dieback as mature
vines are more susceptible to infection (4). Primary management of Eutypa dieback
consists of preventing infections by protecting pruning wounds with a fungicide, delaying
pruning into the late dormant season, and removing dead, infected vines, and pruned
wood which can serve as inoculum (15). Benomyl, the fungicide used historically to
prevent infection, is no longer available. Delayed pruning and sanitation are currently the
only viable methods of preventing infections. These cultural management practices are
primarily used in premium wine grape production where hand pruning is more common.
The high cost of labor involved in management and the lack of information on the
benefits of managing Eutypa dieback are common reasons why producers of ‘Concord’
grapes in Michigan do little to manage the disease.

Yield reduction due to Eutypa dieback has been studied in California, Washington
and Australia. Studies in Washington indicated that yield, number of clusters, and weight
per cluster were reduced on ‘Concord’ vines that were symptomatic (10). In Australian
‘Shiraz’ vineyards, increasing severity of the disease causes a reduction in both yield and
the number of clusters per vine (21). A more robust yield study in California was
conducted with vineyards of the wine grape cultivars ‘Chenin blanc’, ‘French
Colombard’, and ‘Barbera.’ In this study, a linear relationship was found between disease

severity and yield with yield reduction due primarily to a reduced number of clusters per

77

vine (l4). Eutypa dieback has an estimated cost to wine production in California of over
$260 million per year (16) and is the primary constraint to vineyard longevity in northern
California (14).

Munkvold et al. developed a linear crop loss model that is useful for both
anticipating and estimating yield loss (14). However, due to the differences in production
and growth of wine and juice grapes as well as climatic differences between Michigan
and the west-coast, it is likely that a yield loss model for ‘Concord’ grapevines will need
to be derived from data from Michigan ‘Concord’ vineyards. While a yield study on
‘Concord’ vines was conducted in Washington state, symptoms were classified as either
‘moderate’ or ‘severe’ with a very broad range of yield loss (19 to 65% and 62 to 95%,
respectively) (10). To develop a crop loss model for ‘Concord’ based on disease severity,
a more defined and usable measure of disease severity would be beneficial. Disease
severity is often measured one of two ways; either as a percentage of infected or
symptomatic tissue or with a disease severity scale or diagram. Disease severity
diagrams are most useful when estimating the amount of disease on a single plant part,
such as a leaf, while disease severity scales are better suited to analyze entire plants. A
disease severity scale is made up of the range of disease severities placed into well-
defined classes. Several disease severity scales have been developed for studying Eutypa
dieback (1, 19).

The main objectives of this study was to determine the effect of Eutypa dieback on
the yield of ‘Concord’ grapevines in Michigan and to develop a crop loss model for
measuring and predicting yield loss in order to develop a producer decision guide for

management of the disease.

78

Materials and Methods
Disease Assessment

During the springs of 2003 through 2006, three ‘Concord’ vineyards in Michigan
were surveyed for incidence and severity of Eutypa dieback symptoms when shoots were
approximately 25 cm in length. Vineyard A was located in Berrien Springs and had a
total of 1911 vines arranged in 21 rows of 91 vines. Vineyard B was located in Lawton
and had a total of 1641 vines in 4 rows of 56 vines and 19 rows of 75 vines. Vineyard C
was located in Schoolcraft and had a total of 1833 vines arranged in 47 rows with 39
Vines. All three vineyards had a vine density of 1,349 Vines per hectare (2.1 In between
vines, 2.4 In between rows), and were trained by the Hudson River Umbrella (I-IRU)
method. Vineyards A and B were mechanically pruned in the late winter or early spring
prior to bud swell with follow-up manual pruning in vineyard A. Vineyard C was
manually pruned only in the spring.

Severity was measured for all symptomatic vines in each vineyard with two
methods, 1) percentage of symptomatic shoots, and 2) a disease severity scale (DSS)
modified fi'om Thanassoulopoulos et al. (19) (Table 3.1). Vines were selected across the
range of the severity scale, including healthy Vines, to provide a sufficient representation
for analysis. A total of 421 vines were used in the study with the following number of
vines per category; healthy-107, 1-88, 2-84, 3-86, 4-57. Symptomatic vines were required
to have two productive cordons to avoid vines that would have a reduced yield due to
death of a cordon. Healthy control vines were chosen in close proximity to symptomatic

vines to minimize variation arising from environmental effects.

79

Table 3.1. Disease severity scale adapted from Thanassoulopoulos et al." for evaluating
and measuring disease severity of grapevines symptomatic for Eutypa dieback. Basal
shoots were not included in the rating.

 

DSSb Description of symptoms

 

0

1

5

Vine apparently healthy. No symptoms present on shoots.

Symptoms restricted mainly to leaves. Stunting of symptomatic shoots slight,
between 10 and 20% of the normal growth. Some chlorosis, partial cupping
and size reduction of symptomatic leaves.

Symptoms mostly on the leaves with noticeable sttmting of shoots (>20%).
Severe chlorosis, cupping and size reduction present on some symptomatic
leaves.

Symptoms severe, often present throughout an entire arm (symptoms can be
apparent on both arms). Severe symptom expression on most symptomatic
leaves.

Symptoms present throughout vine. Severe stunting of shoots, chlorosis, and
cupping of leaves. Few to no asymptomatic shoots present on vine.

Vine apparently dead. No visible new growth in the spring.

 

"Thanassoulopoulos, C. C. Roumbos, I. C., Tsahouridou, P., Tsoupeis, D., and Gatzas, A.
1996. A proposed disease index for estimating disease progress and losses caused by the
fungus Eutypa lata in grapevine. Phytopath. medit. 35: 191-198.

bDSS: Disease Severity Scale

80

Yield assessment

In the fall of 2003 through 2006, the clusters on each selected vine were counted.
In addition, 100 clusters were randomly selected and harvested from healthy vines for
each vineyard. The clusters were weighed to determine average cluster weight for each
vineyard. Yield for each Vine was estimated by taking the average cluster weight for the
Vineyard and multiplying it by the number of clusters per vine. To allow comparison
between years, the yield was adjusted to % yield with the average yield of healthy Vines
set at 100%. To determine the accuracy of our yield estimation, selected vines were hand
harvested in the fall of 2005. Linear regression analysis was performed with SigmaPlot
Version 9.0 (Systat Software, Inc, Point Richmond, CA) to determine the correlation

between estimated yield and actual yield.

Evaluation of disease and yield assessment

To evaluate the effect of disease severity as measured by the percent symptomatic
shoots on the number of shoots, the number of clusters and the number of clusters per
shoot, values were standardized to an intercept of 100% (average of apparently healthy
vines), and were then evaluated by regression analysis using SigmaPlot Version 9.0. The
linear regression coefficients were then compared between vineyards and years using the
general linear models procedure in SAS Version 9.1.3 (SAS Institute, Cary, NC). The
effect of disease severity, as measured using the DSS, on the number of shoots, the
number of clusters and the ntunber of clusters per shoot was analyzed using the analysis
of variance procedure (PROC AN OVA) in SAS and mean separation by Tukey's HSD

test (P5005) for multiple comparisons.

81

Development of crop loss models

Models of percent yield loss for individual vines were developed using the
stepwise regression procedure (PROC REG) in SAS with the following independent
variables: number of shoots, disease severity as a percentage of infected shoots, and
disease severity as the DSS. Percent yield loss for each vine was calculated by
subtracting the percent estimated yield for each vine from the average yield (100%) of the
healthy vines for each vineyard. As the average weight of clusters was directly used to
estimate the yield of each vine, the number of clusters per shoot was not added to the

model for evaluation.

82

Results
Regression analyses of the efiect of percent symptomatic shoots on vegetative and yield
components

Regression analysis was performed to determine if any correlation existed
between Eutypa dieback disease severity, measured as the percent symptomatic shoots
per individual vines, and the number of shoots, clusters, and clusters per shoots (Table
3.2). For all Vineyards and years, there was a significant negative linear correlation
between the number of clusters and the percent symptomatic shoots (P S 0.05), indicating
that, as disease severity increases, the number of clusters are reduced. The number of
shoots per vine also showed a significant negative correlation with the percent
symptomatic shoots for all years and vineyards, with the exceptions of vineyard C in
2003 and vineyard B in 2004. This indicates that, as disease severity increases, the
number of shoots per vine decreases. The number of clusters per shoot also was
negatively correlated with the percent symptomatic shoots, but only significantly in
vineyard A in 2003, 2004, and 2005, vineyard B in 2006, and vineyard C in 2004 and
2006.

To be able to compare vineyards and years, the number of shoots and the number
of clusters was adjusted to a percentage with 100% set as the average from healthy Vines.
Linear regression analysis was performed between the percent number of shoots and the
percent number of clusters per shoot per vine (Figures 3.1-3.4). In all years and
vineyards, there was a significant positive correlation with the percent number of shoots

per vine and the percent number of clusters per Vine.

83

Table 3.2. Linear regression analyses of the relationship between the percent
symptomatic shoots and the number of shoots per vine, clusters per vine, and clusters per
shoots per vine for vines symptomatic for Eutypa dieback from 2003-2006. Vitis labrusca
‘Concord’ vineyards were located in Southwest Michigan.

 

 

 

 

 

 

 

2003 2004
Intercept Slope R2 P value‘I Intercept Slope R2 P value

Vineyard Ab

Shoots per vine 67.43 -42. 14 0.370 <0.0001 55.65 -28.11 0.351 <0.0001

Clusters per vine 123.52 -106.36 0.596 <0.0001 114.04 -83.24 0.625 <0.0001

Clusters per shoot 2.05 -1.01 0.134 0.0047 2.28 -1.14 0.214 0.0003
Vineyard B

Shoots per vine 85.64 -62.50 0.368 <0.0001 50.73 -21.64 0.262 0.0612

Clusters per vine 195.84 -138.32 0.389 <0.0001 99.06 -87.7 0.430 0.0109

Clusters per shoot 2.30 0.01 0.028 0.2737 1.94 -1.1 0.255 0.0657
Vineyard C

Shoots per vine 48.74 -33.07 0.168 0.0647 30.75 -20.26 0.399 0.0005

Clusters per vine 104.57 -98.70 0.349 0.0048 56.87 -53.86 0.729 <0.0001

Clusters per shoot 2.40 -1.68 0.163 0.0694 2.04 -1.46 0.445 0.0002

2005 2006
Intercept Slope R2 P value Intercept Slope RI P value

Vineyard A

Shoots per vine 72.74 -47 .35 0.579 <0.0001 55.52 -32.91 0.553 <0.0001

Clusters per vine 331.90 -289.40 0.589 <0.0001 60.48 -43.20 0.749 <0.0001

Clusters per shoot 4.84 -2.77 0.326 0.0002 1.19 -0.37 0.102 0.0540
Vineyard B

Shoots per vine 106.31 -67.78 0.352 0.0018 55.38 -35.32 0.518 <0.0001

Clusters per vine 453.43 -349.88 0.349 0.0019 94.94 -88.80 0.653 <0.0001

Clusters per shoot 4.33 -1.04 0.067 0.2119 1.79 -l . 14 0.567 <0.0001
Vineyard C

Shoots per vine 48.60 -24.89 0.254 0.0033 49.27 -29.47 0.497 <0.0001

Clusters per vine 157.51 -108.74 0.385 0.0002 85.88 -69.57 0.807 <0.0001

Clusters per shoot 3.28 -0.63 0.049 0.2237 1.85 -0.67 0.193 0.0046

 

" P value of the slope is significant when P S 0.05.
b Vineyard A is located in Baroda, MI, Vineyard B is located in Lawton, MI, and
Vineyard C is located in Schoolcraft, MI.

84

 

 

 

160
O Vineyard A
140 1 V Wneyard B
I Vineyard C
120 4
100 -
80 . O
60 4

Number of clusters per vine (% of healthy)

 

    

 

 

40 y = 15.0 + 0.71x
1. 3‘7 3 R2=0.51
c
204 l . . '
9 c
0- o ‘
o 20 40 60 80 100 120 140 160

Number of shoots per vine (% of healthy)

180

Figure 3.1. Regression analysis between the number of clusters per Vine as a percentage
of healthy and the number of shoots per vine expressed as a percentage of healthy in three
Vitis labrusca ‘Concord’ vineyards located in Southwest Michigan. Data was collected in

2003.

85

 

  

160
O \fineyard A

140 l V Vineyard B V v
I Vineyard C

 

 

120 4
100 r
80 -

60 -
y = 12.4 + 0.78x

Number of clusters per vine (% of healthy)

 

 

 

40 -
, v R2 = 0.46
20 g o .9 $
0
0 - Cl 0
0 20 40 60 80 100 120 140 160 180

Number of shoots per vine (% of healthy)

Figure 3.2. Regression analysis between the number of clusters per vine as a percentage
of healthy and the number of shoots per vine expressed as a percentage of healthy in three
Vitis labrusca ‘Concord’ vineyards located in Southwest Michigan. Data was collected in
2004.

86

 

180

 

 

 

 

 

 

A O VineyardA
.5 160 - v VineyardB
(u I VineyardC V
2 140
Q.—
o
°\° 120 -
a)
.E 100 -
>
3 80 -
9
93. 60 -
m
2
8 40 -
o
g 20 -
E
a o-
0 20 40 60 80 100 120 140 160

Number of shoots per vine (% of healthy)

Figure 3.3. Regression analysis between the number of clusters per vine as a percentage
of healthy and the number of shoots per vine expressed as a percentage of healthy in three
Vitis labrusca ‘Concord’ vineyards located in Southwest Michigan. Data was collected in
2005.

87

 

 

 

 

   

 

 

 

160
A O VineyardA
.g’ 140 - V Vineyard B
g I Vineyard C V v
f 120 -
o
O
E 100 -
a:
.E
3 80 -
E 60 -
9
‘3
e 40 ‘ y=8.1+0.78x
“5 . R’- = 0.64
5 20 - I
E
3 0 _ 0 v e
Z

0 20 40 60 80 100 120 140 160

Number of shoots per vine (% of healthy)

Figure 3.4. Regression analysis between the number of clusters per vine as a percentage
of healthy and the number of shoots per vine expressed as a percentage of healthy in three
Vitis labrusca ‘Concord’ vineyards located in Southwest Michigan. Data was collected in
2006.

88

The number of shoots, clusters, and clusters per shoots were adjusted to a
percentage with 100% set as the average from healthy Vines, and linear regression
analysis was conducted for the percent symptomatic shoots by year for all vineyards
(Figures 3.5 — 3.16). There was a strong, negative correlation between the number of
shoots and disease severity measured as the percent symptomatic shoots for all years and
Vineyards (Figures 3.5-3.8). As the percent symptomatic shoots increased the percent
number of vines decreased. There was also little deviation from year to year in the slope
of regression (ranging from 0.57 to 0.62). A stronger, negative correlation was seen with
the number of clusters, a measure of yield, and disease severity measured as the percent
symptomatic shoots for all years and vineyards (Figures 3.9-3.12). Again, there was very
little deviation between years, with slopes ranging from 0.75 to 0.78. There was a weak,
negative correlation between the number of clusters per shoot and disease severity

measured as the percent symptomatic shoots for all years and vineyards (Figures 3.13-

3.16).

89

 

 

 

    

 

 

 

180
A O VineyardA
E 160 - . v v \fineyardB
Til I VineyardC
2 140- V V
‘5 5 VV v
c}: 120- ' 9v. V
a: v. y 2= 94.9 — 0.62x
: _
.5 100- I ”3| V v R—0.34
a 80‘ 8
a _ I
3 60 9 lo 0
O . V
- 40- 0'
8
E 20 ' V I
- I
z
o 0
0 T I I I I I
0 20 40 60 80 100

Percent symptomatic shoots per vine

Figure 3.5. Regression analysis between the number of shoots per vine as a percentage of
healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’ vineyards
located in Southwest Michigan. Data was collected in 2003.

90

 

 

 

180
O Vineyard A

160 - ' V Vineyard B
I Vineyard C

14 '-

0 . I
I
120 - 8 ‘
' y = 93.7 — 0.60x
10° ' R2 = 0 40

 
   

Number of shoots per vine (% of healthy)

 

 

 

80 -
60 -
40 -
o
20 - ' I
o 0
o r I I I I I
0 20 40 60 80 100

Percent symptomatic shoots per vine

Figure 3.6. Regression analysis between the number of shoots per vine as a percentage of
healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’ vineyards
located in Southwest Michigan. Data was collected in 2004.

91

 

160

 

 

O VineyardA

140 q V Vineyard B

I I Vineyard C

8
120 - . V

V O V I I
100 - . g V y = 92.5 - 0.57x

:7 R2 = 0.42

    

Number of shoots per vine (% of healthy)

 

 

 

80 -
60 - I
O
40 -
20 . I .
O
0 I I I I I I
0 20 40 60 80 100

Percent symptomatic shoots per vine

Figure 3.7. Regression analysis between the number of shoots per vine as a percentage of
healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’ vineyards
located in Southwest Michigan. Data was collected in 2005.

92

 

 

 

 
 

160
I VineyardA
I I V VineyardB
.J
140 V v I Vineyard C
I
120- IV .V I I
I
' I u I y=lOO.8—O.61x
100‘ R2=0.51

  

Number of shoots per vine (% of healthy)

 

 

 

30-
604
404
20-
I
0 I I l I I

0 20 40 60 80 100
Percent symptomatic shoots per vine
Figure 3.8. Regression analysis between the number of shoots per vine as a percentage of

healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’ vineyards
located in Southwest Michigan. Data was collected in 2006.

93

 

 

 

160

I VineyardA
140 . . V Vineyard B

V I VineyardC ‘
120 - g '
i v- ~I V
100 ‘ y = 92.3 — 0.77x
R2 = 0.53

 

Number of clusters per vine (% of healthy)

 

 

 

80 -
60 -

V
40 - .

I
20 -

I L
0 I I I I I
0 20 4O 60 80 100

Percent symptomatic shoots per vine

Figure 3.9. Regression analysis between the number of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2003.

94

 

 

 

      

 

 

 

I Vineyard A
A 140 -
E‘ 8 v Vineyard B
E 120 I Vineyard C
.c F .
“5 . .
«E 100 a V
g ' . . y = 98.9 — 0.78x
's o R2 = 0.61
2 .0. l 4'
2 I
9.2 60 - V ..
‘3 v
8
“5 40 -
E
20 -
:1 I I
Z
O I I I I I 1—
0 20 40 60 80 100

Percent symptomatic shoots per vine

Figure 3.10. Regression analysis between the number of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2004.

95

 

180

 

 

I Vineyard A
150 . V Vineyard B
V I Wneyard C
140 - . V
12° ‘ I 8 08 y=93.4—0.75x
. O R2 = 0.48
. V
100 I. . V

Number of clusters per vine (% of healthy)

 

 

 

 

0 20 40 60 80 100

Percent symptomatic shoots per vine
Figure 3.11. Regression analysis between the number of clusters per vine as a percentage

of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2005.

96

 

 

 

 

 

 

 

160
A I Wneyard A
>. V Vineyard B
g 140
I V
8 T V v ineyard C
.c
o\° V C. Q
g 100 - ... 0 y=96.5—0.76x
'5 , R2 = 0.72
L-
8 80 a .
l2
0)
'85 6O -
2
o
h
8 4O -
£3
3 20 -
Z
O .
0 20 4O 6O 80 100

Percent symptomatic shoots per vine

Figure 3.12. Regression analysis between the number of clusters per vine as a percentage
of healthy and the percent symptomatic shoots in three Vitis labrusca ‘Concord’
vineyards located in Southwest Michigan. Data was collected in 2006.

97

 

350
300 J
250 -

200 a

Number of clusters per shoot (% of healthy)

 

  

  

 

 

I Vineyard A
V \fineyard B
. I Vineyard C
y = 102.2 — 0.38x °
R2 = 0.07
V V .

  

 

 

100-
504 W ' V v 3 V W I
O
o . l
o If I I I I 4Q
0 20 40 60 80 100

Percent symptomatic shoots per vine

Figure 3.13. Regression analysis between the number of clusters per shoot as a
percentage of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected in 2003.

98

 

 

250

 

 

    

 

 

 

9‘ I VineyardA
g V Vineyard B
3 200 _ g I Vineyard C
.c
9.—
o
3 e
v I O V . .
g 150 ‘ . l . . — 106 1 0 58
_c V y — . — . x
2 ‘ ? o R2=O.25
a 100 - 0
g
g 50 - .
... . I
.3 o l V V '
g e
O 4 I
E
=
Z
0 20 40 60 80 100

Percent symptomatic shoots per vine

Figure 3.14. Regression analysis between the number of clusters per shoot as a
percentage of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected in 2004.

99

 

 

 

    

 

 

 

200
37 I VineyardA
g 180 - I V Vineyard B
8 I Vineyard C
‘8 I
\° 140 a v .
° I
§ J V v . y = 104.4 - 0.43x
V I v
5 12° 8 v .‘V e- V R2=0.20
I- I
1 - I
8. 00
E 80 4
‘5’
B 60 ' l
“5
E 40 _ I I I
I
E
20 - I
a
2 L
o f I f I I
0 20 4O 60 80 100

Percent symptomatic shoots per vine

Figure 3.15. Regression analysis between the number of clusters per shoot as a
percentage of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected in 2005.

100

250

 

 

 

I Wneyard A
V Vineyard B
I I Vineyard C
200 4
I
. _
' I I R = 0.14

 

Number of clusters per shoot (% of healthy)

 

 

I
9
I

 

0 20 40 60 80 100
Percent symptomatic shoots per vine
Figure 3.16. Regression analysis between the number of clusters per shoot as a

percentage of healthy and the percent symptomatic shoots in three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected in 2006.

101

Evaluation of percent symptomatic shoots on yield components

Analysis of covariance was conducted to determine the effect of vineyard location
and percent symptomatic shoots on the number of shoots per vine, number of clusters per
vine, and the number of clusters per shoot per vine by year (Table 3.3). Prior to analysis,
these variables were adjusted to a percentage using the average of the healthy vines as
100%. There was no significant interaction between the vineyard locations and the
percent symptomatic shoots for any of the variables in any year. In all four years of the
study, the percent symptomatic shoots had a significant effect on the percent number of
clusters per vine, making it an ideal component for a crop loss model as there was no
vineyard significant vineyard effect. In 2003 and 2006, there was a significant effect of
vineyard location on the percent number of shoots per vine. In 2004, the percent
symptomatic shoots had a significant effect on the percent number of clusters per shoot
per vine, but not in any other year. Vineyard location had a significant effect on the
percent number of shoots in 2004 and the percent number of clusters per shoot per vine in

2006.

102

Table 3.3 Analysis of covariance for the effects of vineyard (Vin) and the percent

symptomatic shoots (%S S) on the percent number of shoots per vine (%Sh), percent
number of clusters per vine (%Clu), and the percent number of clusters per shoot per vine
(%Clu/Sh) by year. Data was collected from three Vitis labrusca ‘Concord’ vineyards
located in Southwest Michigan.

 

%Sh
Vin
%SS
Vin*%SS

%Clu
Vin
%SS
Vin*%SS

%Clu/ Sh
Vin
%SS
Vin*%SS

%Sh
Vin
%SS
Vin*%SS

%Clu
Vin
%SS
Vin*%SS

%Clu/ Sh
Vin
%SS
Vin*%SS

 

 

 

 

 

 

2003 2004

d.f.“ ssam" MS‘ Fvalue Pr>F d.f.“ ssan)b MS‘ Fvalue Pr>F
2 5932 2966 4.06 0.0231 2 242 121 0.17 0.3457
43 59153 1232 1.69 0.0332 33 41399 1103 1.53 0.0730
21 11627 554 0.76 0.7540 5 336 77 0.11 0.9902
2 523 261 0.40 0.6703 2 460 230 0.50 0.6103
43 53796 1225 1.39 0.0131 33 66349 1759 3.31 0.0001
21 4923 235 0.36 0.9936 5 2314 463 1.00 0.4266
2 3320 4160 2.93 0.0624 2 777 339 0.49 0.6133
43 99293 2069 1.46 0.0930 33 31325 2153 2.69 0.0006
21 44710 2129 1.50 0.1195 5 4259 352 1.06 0.3916

2005 2006

d.f.“ 33011)" MS° Fvalue Pr>F d.f.“ ssan)r MS“ Fvalue Pr>F
2 1221 4223 1.07 0.3557 2 3635 1343 4.32 0.0193
42 33379 926 2.34 0.0054 45 59754 1323 3.12 0.0002
14 7932 567 1.43 0.1399 17 9992 533 1.33 0.1966
2 2343 1 174 1.63 0.2002 2 572 236 1.35 0.2710
42 55426 1320 1.39 0.0267 45 36792 1929 9.09 0.0001
14 9155 654 0.94 0.5307 17 4632 275 1.30 0.2413
2 1397 699 1.17 0.3206 2 10365 5433 5.93 0.0053
42 41996 1000 1.63 0.0573 45 51637 1149 1.26 0.2242
14 13317 937 1.66 0.1099 17 20429 1202 1.32 0.2263

 

a d.f. = degrees of freedom

b SS(III) = type III sum of squares.

° MS = Mean of Squares

103

Evaluation of disease severity scale on disease components

The effect of disease severity, in the form of the DSS, was determined on the
number of shoots per vine, number of clusters per vine, and the number of cluster per
shoot. Due to the imbalance in the number of vines for each severity category, the
Tukey's HSD test was used to conduct mean separation for the variables (Table 3.4). For
nearly all years and vineyards, there were significant differences between the number of
shoots for each DSS level, with healthy vines (DSS value of 0) having the highest
number of shoots and the most severely symptomatic vines (DSS value of 4) having the
lowest number of shoots. The same trend was seen for the number of clusters per vine.
The number of clusters per shoot was significantly affected by Eutypa dieback in only
about half of the vineyards using the DSS scale, but did show a decrease as the DSS

values increased. Analysis of variance tables are presented in Appendix C.

104

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105

Comparison of predicted yield to actual yield

Vines were harvested in 2005 to compare the predicted yield to actual yield.
Linear regression resulted in an R2 value of 0.86, indicating that there was 14% variation
between the actual yield and the predicted yield (Figure 3.17). The intercept of the
regression line was at 8.64, indicating that yield is underestimated in the prediction, most
dramatically when yield levels are low. A slope of 0.88 indicates that the predicted yield
underestimates the actual yield of vines making models developed from estimated yield

conservative for yield loss.

106

 

160

    
  
 

 

 

 

 

 

I Vineyard A
140 - V Vlneyard B I
A I \fineyard C
g 12
TO 120 -
a)
.c
“5 100 -
73'
g 80 -
'o
E 60 4
>
g 40 -
<1: y = 8.64 + 0.88x
20 - R2 = 0.86
0 *~ 1 I I I I I I I
0 20 40 60 80 100 120 140 160 180

Predicted yield (percent of healthy)

Figure 3.17. Regression analysis between the percent predicted yield expressed as percent
of healthy and the actual yield expressed as a percent of healthy for three Vitis labrusca
‘Concord’ vineyards located in Southwest Michigan. Data was collected in 2005.

107

Crop loss model assessment

After converting the number of clusters per vine to estimated percent yield loss,
stepwise linear regression was used to evaluate and develop crop loss models using
percent symptomatic shoots and the disease severity scale. The number of shoots per vine
was also tested as a possible component for the models. Models were developed for
individual vineyards by year. Also, two cumulative models, developed from data pooled
from all years and vineyards, were developed for each measure of severity with
(cumulative) and without (quick) the number of shoots included. Output from the
stepwise regression procedure is presented in Appendix B. When using the percent
symptomatic shoots as the measure of severity, individual vineyard models had R2 values
ranging from 0.50 to 0.86 while the cumulative model had a R2 value of 0.66 and the
quick model had a R2 value of 0.60 ( Table 3.5). When using the disease severity scale,
individual vineyard models had R2 values ranging from 0.59 to 0.90 while the cumulative

model had a R2 value of 0.67 and the quick model had a R2 value of 0.53 (Table 3.6).

108

Table 3.5. Crop loss models based on the number of shoots (Sh) and percent symptomatic
shoots (%SS) for all vineyards and years individually. A cumulative model was
developed from data collected from all vineyards and years. A ‘quick’ cumulative model
was also developed using only %SS data.
Year Model equation R2 d.f.a F value Pr>F
Vineyard A
2003 %YL = 46.5 + 0.56 %SS — 0.58 Sh 0.71 57 67.4 <0.001
2004 %YL = 29.8 + 0.58 %SS — 0.52 Sh 0.67 55 54.3 <0.001
2005 %YL = 71.2 + 0.42 %SS — 0.89 Sh 0.74 37 50.0 <0.001
2006 %YL = 0.3 + 0.71 %SS 0.75 36 104.7 <0.001
Vineyard B
2003 %YL = 43.0 + 0.41 %SS — 0.39 Sh 0.50 44 21.3 <0.001
2004 %YL = 75.4 + 0.61 %SS — 1.46 Sh 0.80 13 22.7 <0.001
2005 %YL = 72.5 + 0.37 %SS — 0.73 Sh 0.71 24 26.9 <0.001
2006 %YL = 68.0 + 0.48 %SS - 1.11 Sh 0.77 28 44.1 <0.001
Vineyard C
2003 %YL = 72.7 + 0.43 %SS — 1.30 Sh 0.79 20 33.2 <0.001
2004 %YL = 34.5 + 0.72 %SS — 1.10 Sh 0.79 25 43.2 <0.001
2005 %YL = 74.0 + 0.28 %SS — 1.23 Sh 0.68 31 31.3 <0.001
2006 %YL = 28.3 + 0.64 %SS - 0.48 Sh 0.86 39 110.4 <0.001

Cumulative %YL = 28.4 + 0.63 %SS — 0.38 Sh 0.66 420 402.3 <0.001
Quick %YL = 4.94 + 0.77 %SS 0.60 420 624.7 <0.001
’ d.f. = degrees of freedom.

109

Table 3.6. Crop loss models based on the number of shoots (Sh) and disease severity
scale (DSS) for all vineyards and years individually. A cumulative model was developed
from data collected from all vineyards and years. A ‘quick’ cumulative model was also
developed using only DSS data.

 

 

Year Model equation R2 d.f.a F value Pr>F
Vineyard A
2003 %YL = 45.1 + 12.4 DSS — 0.65 Sh 0.68 57 59.5 <0.001
2004 %YL = 28.6 + 14.0 DSS - 0.58 Sh 0.67 55 53.0 <0.001
2005 %YL = 76.3 + 8.5 DSS — 1.02 Sh 0.73 37 46.6 <0.001
2006 %YL = 16.3 + 14.6 DSS — 0.39 Sh 0.75 36 52.2 <0.001
Vineyard B
2003 %YL = 33.4 + 9.8 DSS — 0.37 Sh 0.59 44 29.7 <0.001
2004 %YL = 65.3 + 12.2 DSS — 1.28 Sh 0.79 13 20.4 <0.001
2005 %YL = 72.9 + 7.6 DSS — 0.77 Sh 0.72 24 28.5 <0.001
2006 %YL = 65.7 + 12.5 DSS — 1.11 Sh 0.81 28 55.0 <0.001
Vineyard C
2003 %YL = 69.8 + 7.4 DSS - 1.32 Sh 0.77 20 29.4 <0.001
2004 %YL = 38.5 + 15.1 DSS — 1.31 Sh 0.82 25 52.5 <0.001
2005 %YL = 68.0 + 7.2 DSS - 1.18 Sh 0.70 31 34.1 <0.001
2006 %YL = 29.0 + 15.7 DSS - 0.68 Sh 0.90 39 174.8 <0.001
Cumulative %YL = 25.9 + 14.5 DSS - 0.45 Sh 0.67 420 433.5 <0.001
Quick %YL = -2.7 + 17.9 DSS 0.58 420 584.0 <0.001

 

3‘ d.f. = degrees of freedom.

110

Discussion

This research shows a direct correlation between Eutypa dieback disease severity
and yield. Yield loss can partially be explained by a reduction in both the number of
shoots and the number of clusters on symptomatic vines. Munkvold et al. found that the
primary effect of Eutypa dieback on yield was to reduce the number of shoots per vine,
but found little impact from reduced cluster sizes (14). No analysis of the number of
clusters per shoot weight was performed (14).

When comparing the two methods for describing disease severity, there was little
difference in the models generated for either method (Tables 3.5 and 3.6). Based on the
values of the cumulative models, percent symptomatic shoots and the disease severity
scale were not significantly different (R2 values of 0.67 and 0.66, respectively). When
comparing the ‘quick’ models, again, the disease severity scale was significantly different
from the percent symptomatic shoots (R2 values of 0.60 and 0.58, respectively). Even the
models function equally in estimating yield loss, the most useful model generated from
this study is the ‘quick’ model that utilizes the disease severity scale due to its ease of
use. The other models incorporating the number of shoots or the percent symptomatic
shoots would require producers the task of counting the number of shoots. This can often
be difficult when vines are tangled and numerous. The quick model using the DSS could
easily be practiced by extension specialists and producers, who could simply and quickly
count and rate the number of vines in a portion of a vineyard to estimate yield loss. This
‘quick’ loss model could be coupled with economic data to provide producers with an
effective management tool that is custom-tailored to their vineyard. One way that these

models could be used is to help producers decide when a vine is no longer profitable due

110

to yield loss from Eutypa dieback. Using cost of production data provided by Mark
Longstroth (see Appendix D) and the data generated from this study, a “break even
point,” where cost of production equals revenue, can be determined for each vine based
on commodity price, cost of production, and disease severity. Using a return of 250
dollars per ton, a production expense of 1.99 dollars per vine, and the “quick” loss
models developed from this study, the ‘break even point” for crop yields of 6, 8, 10, and
12 tons per acre are determined for both measures of disease severity, percent
symptomatic shoots (Figure 3.18) and the DSS (Figure 3.19). Producers, knowing the
typical yield of their vineyards, can use this information and determine if a vine
symptomatic for Eutypa dieback is no longer profitable and needs to be replaced. For
example, if a producer averages a crop of 10 ton per acre, using the percent symptomatic
shoots as a measure of severity (Figure 3.18), any vine with greater than 52%
symptomatic shoots would no longer be profitable and should be replaced. Under the
same conditions and using the DSS (Figure 3.19), a producer would want to replace any

symptomatic vines with a DSS value of 3 or greater.

111

 

100

6 ton/acre

 

91
80 ~

8 ton/acre

 

69
60 -

1O ton/acre

 

55

 

46
40 -

Yield (% of healthy)

20-

 

 

 

 

12 ton/acre

 

 

 

5.3 I
20

33.8 '
40

52.0 ' 63.7

Percent symptomatic shoots

80

100

Figure 3.18. Relationship between the amount of disease severity measured as percent
symptomatic shoots and the yield as a percentage with healthy vines at 100%. The
‘break-even point’ where cost of production equals crop revenue for different crop yields
(6, 8, 10, and 12 ton/acre) are located where the red lines intersect the black line. Crop
price is set at $250 per ton with an estimated cost of production at $1.99 per vine with

691 vines per acre.

112

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

120
100 —
91 610m
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E 69 8ton/gc_re_
.C
03° 6%5 10mm
; 4c 12ton/ag[§
5 -
->-_ 40
20 —
O I l I l l

0 1 2 3 4
Disease severity scale

Figure 3.19. Relationship between the amount of disease severity measured as a disease
severity scale and the yield as a percentage with healthy vines at 100%. The ‘break-even
point’ were cost of production equals crop revenue for different crop yields (6, 8, 10, and
12 ton/acre) are presented by red lines. Crop price is set at $250 per ton with an estimated
cost of production at $1.99 per vine with 691 vines per acre.

113

The crop loss model developed by Munkvold and colleagues uses the ratio of
diseased shoots to healthy shoots as a means of measuring disease severity. Their model
had R2 values of 0.70 for 1991 and 0.79 for 1992 (14). These values are in line with
values found in our study. It is unlikely to obtain very high R2 values due to variability of
vines within a vineyard and differences between growing seasons. A good example of
this can be seen in the great differences in the number of clusters from year to year in this
study (exemplified in Table 3.3). The growing season in 2003 was short, with some early
spring fiost and a short growing season that caused poor plant vigor and a poor crop in
2004, which can be seen as a fifty percent decrease in the number of clusters from the
previous year. The following year had much better vine vigor and ideal growing
conditions that resulted in an extremely high yield (reflected in the 300% increase in the
number of clusters from the previous year). Frost in the spring of 2006 killed many of the
buds, leaving an extremely low number of clusters that year. This variation in grong
seasons, while extreme, is not uncommon in Michigan, and will always complicate multi-
year studies that examine yield or other growth properties of the vine.

Differences in vine training and pruning techniques also have an effect on yield.
In this study, there were three vineyards with three different pruning strategies. While the
hand-pruned vineyard (C) had fewer shoots, and consequently, lower yield (clusters),
there was not much difference in the effect of Eutypa dieback on the different vineyards
when converting the data to a percentage of healthy.

The model can be improved upon harvesting and weighing clusters from
symptomatic vines. The method used in this study of averaging the weights of randomly

selected clusters from the vineyard, while 86% accurate, could be improved upon by

114

harvesting fruit from vines, especially those that are severely symptomatic. The berry size
and cluster weight of severely infected shoots are significantly lower than those of
healthy shoots (2). Surprisingly, the yield was underestimated for severely infected
vines. By harvesting from symptomatic vines to determine true yield, yield could be
assessed more accurately, especially on the most severely symptomatic vines that are
likely to have reduced cluster weights.

Crop loss models are a common tool used by plant pathologists to determine the
impact of disease, yet they are not without drawbacks. Models are developed under
specific conditions. Changes in these conditions invalidate the models and they
subsequently lose their ability to measure and predict disease (8). Crop loss models
developed for annual crops are likely to be more consistent and accurate over multiple
years as there is no influence from previous years and growing conditions. A consistency
in cultural practices and a well established yield potential leaves only the changing
environment as the major effecter to an annual crop loss model. A perennial crop, like
grape, has a longer list of potential variations that could skew a crop loss model. For
example, winter and spring freeze damage can kill a portion of the buds creating a
reduced number of shoots, lowering the overall yield potential that year. The crop loss
models created for this study are vineyard specific, a quality that will likely be necessary
for evaluating other vineyards.

Despite the shortcomings of these models, they are a useful foundation with
which to advance our knowledge about Eutypa dieback and its role in yield reduction.

By having a better understanding of this process, we can begin to devise better

115

management practices as well as educate producers about the advantages of managing

Eutypa dieback in their vineyards.

116

10.

11.

LITERATURE CITED

Boulay, M., ed. 1991. Lutte contre l’Eutypiose. Programme CEE, Programme
CT91/205: Competitiveness of agriculture and management of agricultural
resources, Camar. 1991-1994: 127.

Butterworth, S. C. 2003. Eutypa dieback: Effects on growth and yield
components, and diagnosis in ‘Concord’ grapevines. M. S. Thesis. Michigan State
University.

Carter, M. V. 1983. Biological control of Eutypa armeniacae V. Guidelines for
establishing routine wound protection in commercial apricot orchards. Australian
Journal of Experimental Agriculture 23:429-436.

Carter, M. V. 1991. The status of Eutypa lata as a pathogen. Monograph.
Phytopathological Paper No. 32. International Mycology Institute UK.

Creaser, M., and Wicks, T. 2001. Yearly variation in Eutypa dieback

symptoms and the relationship to grapevine yield. Aust. N. Z. Grapegrow.
Winemaker 452:50-52.

Deswarte, C., Peyrebrune, S., Canut, H., Roustan, J. P., and F allot, J. 1994.
Purification of plasma membrane vesicles from Vitis vinifera cv Gamay
suspension cells by free-flow electrophoresis. Vitis 33:99-100.

Dumot, V., Me’nard, E., Courlit, Y., Ouvrié, M., Desaché, F., Boursier, N.,
David, S., Dubos, B., and Larignon, P. 2004. Eutypa canker in the

Charentes region: Results of a 10-year study on Ugni blanc. Phytoma
568:4-7.

Guant, R. E. 1995. The relationship between plant disease severity and yield.
Annu. Rev. Phytopath 33:119-144.

Hughes, G. 1988. Spatial heterogeneity in crop loss assessment models.
Phytopathology 78:883-884.

Johnson, D. A., and Lunden, J. D. 1987. Incidence and yield impact of Eutypa
dieback of grapevine in Washington State. Wash. St. Univ. Coll. Agric. Home
Econ. Res. Bull. 0993.

Kleweno, D. D., and Mathews, V. 2007. Michigan Fruit Inventory 2006-2007. N.
A. S. Service, ed. USDA.

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12.

13.

14.

15.

16.

17.

18.

19.

20.

21.

Moller, W. J., and Kasimatis, A. N. 1978. Dieback of grapevines caused by
Eutypa armeniacae. Plant Disease Reporter 62:254-258.

Molyneux, R. J ., Mahoney, N., Bayman, P., Wong, R. Y., Meyer, K., and Irelan,
N. 2002. Eutypa dieback in grapevines: Differential production of acetylenie
phenol metabolites by strains of Eutypa lata. Journal of Agricultural and Food
Chemistry 50:1393-1399.

Munkvold, G. P., Duthie, J. A., and Marois, J. J. 1994. Reductions in yield and
vegetative growth of grapevines due to Eutypa dieback. Phytopathology 84:186-
192.

Pearson, R. C., and Goheen, A. C, ed. 1988. Compendium of grape diseases. APS
Press, St. Paul, MN.

Siebert, J. B. 2001. Eutypa: The economic toll on vineyards. Wines and Vines
April:50-56.

Sosnowski, M. R., Shtienberg, D., Creaser, M. L., Wicks, T. J., Lardner,
R., and Scott, E. S. 2007. The influence of climate on foliar symptoms of
Eutypa dieback in grapevines. Phytopathology 97:1284-1289.

Teyrulh, P., Philippe, 1., Renaud, J. M., Tsoupras, G., Deangelis, P., Fallot, J., and
Tabacchi, R. 1991. Eutypine, a phytotoxin produced by Eutypa lata the causal
agent of dying-arm disease of grapevine. Phytochemistry 30:471-473.

Thanassoulopoulos, C. C., Roumbos, I.C., Tsahouridou, P., Tsoupeis, D., and
Gatzas, A. 1996. A proposed disease index for estimating disease progress and

losses caused by the fimgus Eutypa lata in grapevine. Phtyopath. medit. 35:191-
198.

Weigle, T. H., and Muza, A. J., ed. 1997. 1998 New York and Pennsylvania Pest
Management Recommendations for Grapes. Cornell Cooperative Extension,
Ithaca, NY, and Penn State Cooperative Extension, University Park, PA.

Wicks, T. a. D., K. 1999. Effect of Eutypa on grapevine yield. Grape Grower &
Winemaker Annual Technical issue. 426a: 1 5-16.

118

CHAPTER 4: DETECTION OF EUTYPA LATA AND EUTYPELLA VITIS IN
GRAPEVINE BY NESTED MULTIPLEX PCR.

Abstract

Two fungi were isolated from grapevines in Michigan vineyards with Eutypa
dieback symptoms: Eutypa lata and Eutypella Vitis. These fungi are diffith to
distinguish morphologically but are genetically distinct as determined by sequencing of
the internal transcribed spacer (ITS) regions. The ITS regions of 25 Eutypa lata and 15
Eutypella Vitis isolates were sequenced. Eutypa lata sequences were more variable than
those of Eutypella Vitis. Polymerase chain reaction (PCR) primers were designed for each
species and evaluated against isolates of both fungi as well as 11 closely related
Diatrypaceous fungi and 23 isolates of other fungi representing various pathogenic,
saprophytic, and endophytic genera on grape and other small fruit crops. The primers
were specific for their intended species. A nested multiplex PCR protocol was developed
and used to successfully detect these fungi in wood samples fiom cankers with and
without stromata from naturally infected vines as well as in artificially inoculated, potted
canes. The primers developed in this study will assist in our abilities to diagnose and

study the roles of Eutypa lata and Eutypella Vitis in Eutypa dieback development.

119

Introduction

Eutypa dieback, previously known as “dead-arm”, affects grapevines (Vitis spp.)
around the world, including the United States, Europe, Australia and South Africa (9).
The disease is caused by the ascomycete fungus Eutypa lata Pers. Tul. & C. Tul (syn.
Eutypa armeniacae Hansf. & Carter), which is also a pathogen of other woody plants,
such as almond, apricot, cherry, olive, peach, and walnut (8, 10, 23, 33).

Infection occurs when windbome ascospores of the fungus land on fresh pruning
wounds and invade the xylem vessels. Two to four years later, cankers become apparent
around pruning cuts on trunks and cordons (33, 38). F oliar symptoms are most obvious
early in the season and include yellowing and cupping of newly emerged leaves, stunted
shoots with short intemodes, and shedding of blossom clusters (33, 38). These symptoms
are attributed to one or more toxins produced by the fungus (16, 35, 52). Vines slowly
decline and eventually die (56). After the bark weathers away from the cankered area, the
fungus produces black stromata which contain numerous perithecia with infectious
ascospores (37, 38). In Michigan, ascospores are released throughout the year, except
during hot, dry periods in the summer (49).

Eutypa dieback is difficult to control and may lead to severe economic losses,
primarily due to decreased yields and longevity of infected vines. Additional expenses
include the removal or renewal of infected vines, replanting and re-grafiing, as well as a
delay in the productivity of newly planted vines. Yield losses of 30-62% have been
reported in California (3 6) where the disease is a major constraint to vineyard longevity.
The cost to wine production in California has been estimated to be in excess of $260

million per year (50). In Michigan, many older ‘Concord’ (Vitis labrusca L.) vineyards

120

suffer from Eutypa dieback (7, 48). F oliar symptoms are often used by growers to
identify infected vines for pruning or removal; however, these symptoms may be variable
from one season to the next (7, 48). In addition, healthy growth often obscures
symptomatic shoots by midsummer (33), greatly reducing the ability of growers to
monitor and manage the disease.

Single-ascospore isolations from perithecia found on dead wood of ‘Concord’ vines
in vineyards with Eutypa dieback in Michigan yielded two fungi in the family
Diatrypaceae: E. lata and Eutypella Vitis (Schwein.:Fr.) Ellis & Everh. [syn. Eutypella
aequilinearis (Schwein.:F r.) Starb.] (48). Fruiting structures of these fungi on wood and
their ascospores appeared very similar. In addition, both fungi are white or creamy white
in culture. The fungi were identified based on sequencing of the ITS region (48) and
morphological characteristics (18, 28, 44). Eutypella Vitis was previously reported on
wood of a ‘Concord’ vine in Illinois (22). This particular isolate was also used as an
‘outgroup’ in a study on diversity of E. lata isolates in California (15). The United States
National Fungal Herbarium contains two specimens of El. vitis collected from Vitis sp. in
Paw Paw, Michigan in 1907, and in Lawton, Michigan in 1908. Other specimens of this
frmgus in the herbarium originate from Vitis labrusca in Virginia; Vitis sp. in Maryland,
South Carolina, Virginia, and Italy; Vitis rotundifolia Michx. in Alabama; and Vitis
vinifera L. in Pakistan (information is available online at the Fungal Databases,
Systematic Botany and Mycology Laboratory, Agricultural Research Service, US.
Department of Agriculture). However, most of these specimens were collected in the
early 19005, and no reference was made to any disease symptoms on the plant or the

plant tissues on which they were found.

121

The pathogenicity of El. vitis on grapes is unknown. Preliminary results with
mycelium-inoculated ‘Concord’ cuttings indicate that the fungus is able to colonize
healthy grapevine tissue and cause necrosis similar to that caused by E. lata isolates (29).
Some isolates of this fungus were found to produce phytotoxic compounds (29, 34).
Recently, two other Diatrypaceous fungi, Eutypa leptoplaca (Mont) Rappaz and
Cryptovalsa ampelina Nitschke, were characterized as necrotrophic pathogens on grapes
(35, 55), indicating the possibility that Eutypa dieback symptoms, or at least cankers, can
be caused by fungi other than E. lata. If El. Vitis is found to be a primary pathogen of
grapes, it may differ from E. lata in its symptomology and epidemiology, possibly
requiring different management strategies. If, on the other hand, it is an opportunistic
fungus growing on declining or dead grapevines, its confusion with E. lata could lead to
false positive disease diagnoses. In either case, the ability to rapidly and reliably
distinguish the two fungi in the laboratory as well as in the field becomes vitally
important.

Isolation of the causal fungus from infected vines for diagnostic purposes may be
complicated by the presence of saprophytic fungi in infected wood (33, 54). Antisera
have been developed to distinguish E. lata from other fungi in culture and also in wood,
but specificity has been a problem (21, 43). The use of species-specific primers in the
polymerase chain reaction (PCR) has proved useful in the detection and identification of
fungal pathogens, particularly those that are difficult to isolate or those that cause
symptorrrless infection (6, 32, 45). The sequences of the internal transcribed spacer (ITS)
regions of ribosomal DNA are most commonly utilized to design PCR primers for fungi.

The ITS regions are comprised of two variable non-coding regions (ITSl and ITSZ) that

122

separate the highly conserved 188 (small subunit), 5.88 and 288 (large subunit)
ribosomal RNA genes. The ITS regions can be highly polymorphic among species within
a genus (24, 49) and have been used to identify many important plant pathogens
including F usarium (l 7, 51), Phytophthora (3), and Pythium (3) species.

Multiplex PCR, a technique that utilizes multiple sets of primers, was first developed
to assist in diagnosis of Duchenne muscular dystrophy (11), but has since been used
extensively in the field of plant pathology. While primarily used for the detection of
pathogens directly from infected tissue (4, 13, 20, 26), the technique has also been used
for race determination of pathogens (12, 14) and identification of mating types (25, 53).

A method combining the use of universal PCR primers with restriction fragment
length polymorphisms (RFLP) was used by Rolshausen et al. (46) to distinguish E. lata
from other fungi in culture. Lecomte et al. (31) designed species-specific primer pairs for
E. lata and tested them successfully on numerous isolates from Italy, Spain and France.
However, Rolshausen et a1. (46) found a lack of specificity with several of these E. lata
specific primer sets and were able to amplify several closely related Diatrypaceous fungi.
More recently, a set of primers developed from sequence characterized amplified regions
were used to detect E. lata from inoculated vines in Australia (3 0). While isolates of E.
lata from Australia, New Zealand, Europe and California were used to develop primers,
few Diatrypaceous fungi and no isolates from other areas of the United States were
tested, so it is not known whether they will detect isolates from the eastern United States
or can distinguish E. lata from El. vitis. To our knowledge, no species-specific primers

for El. Vitis have reported in the literature. The objective of this research was to develop a

123

PCR-based method to distinguish E. lata and El. vitis from grapevine, both in culture and

in planta.

124

Materials and methods
Fungal culture collection and maintenance

Eutypa lata and El. Vitis isolates were obtained from three different ‘Concord’
vineyards in southwest Michigan (Lawton, Baroda, and Berrien Springs). Vine trunks and
branches with cankers having visible stromata were collected and brought back to the
laboratory. Pieces of wood with perithecia were soaked in sterile water for 6-8 hours.
Individual perithecia were transferred with sterile forceps to a microscope slide and
crushed in 50 ul water by gently pressing the cover slip. The ascospore suspension was
transferred to a 2-ml centrifuge tube and spore concentration was determined with an
improved Neubauer hemacytometer (American Optical Co., New York, NY). Ascospores
were then diluted to 102 and 103 spores per 100 1.11 and plated onto PDA (potato dextrose
agar) amended with aqueous streptomycin sulfate (20 mg L'l) to prevent bacterial
growth. The plates were incubated at room temperature and checked daily for 4 days for
frmgal growth. Single-ascospore colonies were sub-cultured on the same medium. Fungal
isolates from other geographic regions were obtained for use in this study (Table 4.1). All

isolates were stored in 15% glycerol at -80°C.

DNA extraction fi‘om cultures.

For DNA extraction, fungal isolates were grown for 7-10 days on cellulose
membrane-covered plates. Mycelium was scraped off and placed into 1.5-ml
microcentrifuge tubes. DNA was extracted according to the protocol described by Lee et
a1. (32). Approximately 100 mg of mycelium was suspended in 700 pl lysis buffer (50

mM Tris-HCl, 50 mM EDTA [ethylenediaminetetraacetic acid], 3% SDS pH, 8.0;

125

amended with 1% 2-mercaptoethanol just prior to each use). The mycelium was crushed
with a pestle for 3-5 minutes and incubated for 1 hour at 65°C. Then, 700 pl
phenol:chloroform:isoamyl alcohol (25:24: 1) was added, and the tubes were vortexed
briefly. Phases were separated by centrifugation at 12,000 rpm for 10 min, and the
aqueous top phase was transferred to a new tube. The phenolzchloroformzisoamyl alcohol
treatment and centrifugation steps were repeated. Then 700 pl chloroform:isoamy1
alcohol (24:1) was added to the supernatant, which was vortexed and then spun at 12,000
rpm for 5 min. The aqueous phase was collected, and 20 ul of 3 M sodium acetate and
0.5 volume of isopropyl alcohol were added. DNA was precipitated by inverting the
tubes gently several times and centrifuging for 10 min at 13,000 rpm at 4°C. The
supernatant was decanted off and pellets were resuspended in 100 pl TE buffer (10 mM

Tris-HCI and 0.5 M EDTA), pH 8 and stored at —20°C.

Sampling of naturally infected cankers

Grapevine wood samples were collected from three Eutypa-infected ‘Concord’
vineyards in southwest Michigan (Lawton, Marcellus, and Berrien Springs). Sawdust was
collected by drilling cankered areas showing stromata on selected vines with a cordless
drill (Black and Decker, Towson, MD) with a 3.175-mm drill bit. Precautions were taken
to prevent cross contamination of samples: the drill bit was washed in 70% EtOH and
flame sterilized between extractions. Laboratory film (Pechiney Plastic Packaging,
Chicago, IL) was wrapped tightly around the chuck at the base of the bit to prevent
sawdust from entering the drill and replaced between each extraction. Canker tissue was

drilled to a depth of approximately 5 mm, yielding 25 to 50 mg of sawdust which proved

126

adequate for DNA extraction and the subsequent PCR protocol. Fourteen wood samples
were collected by drilling directly over stromata on suspected Eutypa cankers on
symptomatic vines, and a negative control sample was taken from an apparently healthy
vine. For confirmation, stromata were also brought back to the laboratory and single-
ascospore isolations were performed as previously described. Twelve cankers fiom
symptomatic vines that did not have visible stromata were sampled by drilling, once from
the center and once from the margin of each canker. For conformation, wood chips were
collected next to the sampling sites and cultured on PDA amended with ampicillin (50

mg L'l).

Fungal DNA extraction from woody tissues

The sawdust samples were placed in 1.5-ml microcentrifuge tubes, and DNA
extractions were carried out as described by Hamelin et al. (26, 27) with minor
modifications. Wood samples were soaked in 300 111 CTAB extraction buffer (2%
cetyltrimethylammonium; 1.4 M NaCl; 1% polyethylene glycol 8000; 20 mM EDTA; 1%
2-mercaptoethanol; 100 mM Tris-HCI, pH 9.5) and ground with an acid-treated,
sterilized mortar and pestle. Extracts were incubated at 65°C for 2 hrs. Following the
addition of 300 pl phenol: chloroform: isoamyl alcohol (25:24: 1), extracts were
centrifuged at 10,000 rpm for 5 minutes. The aqueous phase was transferred to a new
tube, precipitated with an equal volume of cold isopropanol and centrifuged at 10,000
rpm for 10 minutes. Pellets were washed with cold 70% ethanol, air dried for

approximately 1 hr, and resuspended in 50 111 TE buffer. The samples were briefly heated

at 65°C to ensure complete suspension.

127

PCR amplification of internal transcribed spacers

DNA extracted from mycelium was diluted 102 and 103 times in sterile water, and the
ITS regions and 5.8 S gene of the nuclear ribosomal RNA operon (ITSI-5.8$-ITSII) were
amplified with the primers ITS 1F (fungus specific: 5’-
CTTGGTCATTTAGAGGAAGTAA-3’) and ITS4 (universal: 5’-
TCCTCCGCTTATTGATATGC-3’) (19, 57). PCR reactions were carried out in 25 ul
total volume consisting of 12.5 r11 DNA dilution (template) and 12.5 111 PCR reaction
mixture. The reaction mixture contained PCR buffer (20 mM ammonium sulfate; 2.0 mM
MgC12; 50 mM Tris-HCI, pH 9.0; Epicentre Technologies, Madison, WI); 0.2 mM each
of dATP, dTTP, dGTP and dCTP; 0.5 uM each of ITSIF and ITS4 primers; and 0.5 unit
of Taq DNA polymerase. The reactions were carried out in a DNA thermal cycler (Model
9600, Perkin-Elmer Cetus, Norwalk, CT). The amplification protocol included an initial

denaturation at 95°C for 3 minutes followed by 30 cycles at 94°C for 1 min, 50°C for 1

min, and 72°C for 1 min. The reaction was completed by a 7-min extension at 72°C. PCR
products were separated on 1.5% agarose (Gibco BRL, Grand Island, NY) in 1% TAE
buffer (100 mM Tris, 12.5 mM sodirun acetate and 1 mM EDTA, pH: 8.0) by gel
electrophoresis. A l-kb plus DNA ladder (GibcoBRL) was included in each gel as a
DNA size standard. The gels were stained with ethidium bromide, visualized by UV
fluorescence and photographed using an AlphaImager imaging system (Alpha Irmotech

Corporation, San Leandro, CA).

128

Sequencing and primer design.

ITSlF- and ITS4-amplified PCR products of E. lata and El. vitis were purified using
Millipore Ultrafree—MC 30,000 NMWL purification filters (Millipore Corporation,
Bedford, MA). PCR products (100-150 ul) were washed with water four times by
spimring at 4,000 rpm at 4°C. Purified PCR products were run on 3% high-melt agarose
gels at 100 V for quantification before sequencing. PCR products of ITS and 5.88 rRN A
were sequenced by using ITS 1F and ITS4 primers in an Applied Biosystems 370A
Sequencer (Applied Biosystems, Foster City, CA) with the Taq DyeDeoxy Terminator
System. Sequencing was carried out by the Genomics Technology Support Facility at
Michigan State University. Primers ITSS, ITS3 and ITS2 were also used for sequencing
to ensure fidelity of the sequences (57). ITS sequences were edited, aligned and corrected
using the programs EditSeq, MegAlign and SeqMan in the DNASTAR software package
(DNASTAR, Inc., Madison, WI). Each sequence was compared with the sequences in
GenBank (N CB1, Bethesda, MD) using the similarity search program BLAST® (1, 2). In
addition to the sequences of E. lata and El. vitis, available sequences of other
Diatrypaceous fungi (Diatrype sp. AY684241, Diatrypella sp. AY684240, Eutypa
astroidea AJ 30245 8, Eutypa consobrina AJ 302447, Eutypa crustata AJ 302448, Eutypa
flavovirens AJ 3 02457, Eutypa laevata AJ 3 02449, Eutypa lejoplaca AY68423 8, Eutypa
leptoplaca AJ 302453, Eutypa maura AJ 302454, Eutypa petrakii var. petrakii AJ302456,
Eutypa sparsa AY684220, Eutypa tetragona AY684223, Eutypella scoparia AF 3 73064,
Eutypella cerviculata AJ 302461 , Eutypella quaternata AJ 302469, Eutypella cerviculata
AJ 302468, Eutypella alsophila AJ 302467, Eutypella vitis AJ 302466, Eutypella scoparia

AJ 302465, Eutypella prunastri AJ 302464, Eutypella leprosa AJ 302463, Eutypella

129

kochiana AJ 302462, and Eutypella caricae AJ3 02460) were included in the alignment
for primer selection. Primers were evaluated for annealing temperature, GC content and
secondary structure with the Primer Select program (DNASTAR). Primers were
synthesized at the Macromolecular Structure and Sequencing Facility (Department of
Biochemistry, Michigan State University) using an 3948 Oligonucleotide Synthesizer

(Applied Biosystems).

Primer specificity

Specificity of specific primer pairs ELl- EL4 for E. lata and EVl-EV4 for El. vitis
was assessed by PCR using purified DNA from isolates of E. lata and El. vitis and other
pathogenic, saprophytic, and endophytic fungi (Table 4.1). Aside from the Diatrypaceous
fungi, the other fungi were chosen because they are ubiquitous in the environment or
represent genera of fungi also found on grapevines (Table 4.1). The primer pair ITSlF
and ITS4 was used to verify that DNA extracts were suitable for amplification. PCR
amplifications using the ITS and species-specific primers were carried out as previously

described.

Nested multiplex PCR on naturally infected vines

To detect E. lata and El. vitis in culture and in planta, a nested multiplex PCR
protocol was developed. This method is based on a two-step approach. In the first round
of amplification, universal primers (ITSlF and ITS4) are used to enrich the amount of
firngal DNA present in the sample. Products from the first round are then used as the

template for the second round, with all of the species-specific primers (ELI, EL4, EVl ,

130

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133

and EV4) included in a multiplex reaction. DNA isolated from woody tissue was diluted
102 times for PCR reactions. The first round of PCR reactions was carried out in 25 [41
total volume consisting of 1 ul DNA dilution (template) and 24 ul PCR reaction mixture
as described above with 10 uM of each of the primers, ITSIF and ITS4. The
amplification protocol for the first step included: an initial denaturation at 94°C for 2
minutes, followed by 30 cycles at 94°C for 1 min, 50°C for 30 s, and 72°C for 1 min. The

reaction was completed by a 5-min extension at 72°C. One microliter of the first round of
PCR was used as the template for the second round. PCR reactions were carried out in 25
ul total volume consisting of the PCR reaction mixture as described above with 5 M of
each of the primers, ELI, EL4, EVI , and EV4. The amplification protocol for the second
step included: an initial denaturation at 94°C for 2 minutes, followed by 35 cycles at
94°C for 1 min, 62°C for 30 s, and 72°C for 1 min. The reaction was completed by a 5-
min extension at 72°C. The positive controls consisted of fungal DNA from a pure
culture of the respective fungus with a negative control consisting of DNA extracted from
wood of apparently healthy vines. Positive and negative controls were used with all PCR
reactions. Gel electrophoresis, ethidium bromide staining, visualization, and
photography of the PCR products were conducted as described above with one exception:
second-step PCR products were separated on 2% agarose gels. To ensure the validity of
the PCR results, all 300 and 350 bp amplicons (a total of 39 amplicons) were sequenced
by the Genomics Technology Support Facility at Michigan State University using the

forward species-specific primers EL] and EVl.

134

Nested multiplex PCR on artificially inoculated vines

Two isolates of El. vitis from Michigan (EV70 and EV339), and 2 isolates of E. lata,
1 from Michigan (EL130) and 1 from California, (E30) were used to inoculate healthy,
dormant, l-year-old V. labrusca ‘Concord’ canes with two intact nodes. Isolates were
cultured for 5 days on PDA prior to inoculation. Four canes were inoculated for each
isolate. Prior to inoculation, canes were modified using a cordless drill to make a shallow,
3-mm diameter hole approximately 2 cm below the upper node of each cane. A plug of
agar with mycelium was inserted into the hole and sealed with parafilm. The bottom node
was cut, wetted, and dipped in Hormodin 2 (E. C. Geiger Inc., Harleysville, PA), and the
canes were planted in a mixture of 2 parts sand and 1 part Baccto Potting Soil (Michigan
Peat Co., Houston, TX) in 8” pots. The negative control consisted of canes inoculated
with a sterile plug of PDA. Following six months of growth in a greenhouse, tissue was
removed from each cane approximately 5 mm below the edge of the site of inoculation.
DNA was extracted and nested multiplex PCR was performed as described previously.
Tissue from the same site was also surface sterilized and plated on ampicillin-amended
PDA (50 mg L4). The presence or absence of E. lata or E1. vitis was determined by
culture morphology characteristics after 1 week of incubation. Gel electrophoresis,
ethidium bromide staining, visualization, and photography of the PCR products were

conducted as described above.

135

Sensitivity of nested multiplex PC R

Extracted DNA from pure cultures of E. lata and El. vitis was used to make
tenfold serial dilutions (3 ng to 30 ag) to determine the sensitivity of the nested muliplex
PCR in comparison to standard PCR. Standard PCR and nested multiplex PCR were
conducted as described above on each dilution series and with both series combined in
the same reaction. Reactions was replicated three times with a water control included in
each reaction. Gel electrophoresis, ethidium bromide staining, visualization, and

photography of the PCR products were conducted as described above.

136

Results
Isolate characteristics

More than 100 single-ascospore isolates each of E. lata and El. vitis were cultured
from wood samples collected from three different vineyards in Michigan. Both fungi
were found in each vineyard, sometimes on the same vine. It was difficult to tell the
stromata of the two fungi apart, although E. lata perithecia appeared to be more deeply
embedded in the wood than El. vitis perithecia. In general, El. vitis cultures grew
somewhat faster than E. lata cultures and tended to have fluffy, aerial mycelium early in
development as well as black streaking or blotches as they aged. However, culture
appearance was variable and some E. lata cultures also developed dark blotches as they

age, making it difficult to distinguish the species based on culture characteristics.

Sequences of internal transcribed spacer region and primer design

The length of the ITS regions of E. lata and El. vitis sequenced in this study was
approximately 503 and 492 bp, respectively. A Blast search was performed for each
sequence to confirm its similarity to existing sequences of E. lata and El. vitis. The
sequences were submitted to GenBank and an accession number was obtained for each
sequence (Table 4.1). Upon alignment, there was variability in the ITS sequences of E.
lata isolates from different locations. Although isolates from Michigan shared a sequence
homology of 99-100%, they had 98 and 95% sequence similarity to the isolates from
Pennsylvania and California, respectively. All the sequences of El. vitis, including the
existing sequences in GenBank, were 99-100% homologous. However, ITS sequences of

E. lata were only 88% similar to sequences of El. vitis. Species-specific primers were

137

designed from the ITS sequences that varied between species in the same genus but were
conserved among isolates of the same species (Figure 4.1). The ITSZ region was more
variable than the ITSl region among the isolates of E. lata. Primer pairs ELI-EL4 and
EVl-EV4 were developed from the most conserved region within each species that was
most variable between species for specific amplification of E. lata and El. vitis,
respectively (Table 4.2). A diagram showing the positions of the nested, species-specific
primers for E. lata (ELI and EL4) and El. vitis (EVl and EV4); and the universal
primers, ITSlF and ITS4, within the internal transcribed spacer (ITS) regions is shown in

Figure 4.2.

PCR amplification for primer specificity

The specific primer pairs designed for E. lata and El. vitis were tested against
isolates of E. lata, El. vitis, and other fungi (Table 4.1). Primer pairs ELI-EL4 and EVl-
EV4 specifically amplified DNA of only their respective targets, E. lata and El. vitis, in
all reactions (Table 4.1). The primers did not cross-react with DNA of any other fungal
species tested. Primer pair ELI-EL4 produced a single, expected PCR amplicon of
approximately 350 bp, while the pair EVl-EV4 yielded a single PCR product of

approximately 300 bp.

138

ITSI

101 170
lL-MI-7-1 GCTACCCTGTAGCCCGCTGCAGGCCTACCCGCCGGTGgAggQgg;§h§§g§22§22222§h§g§h2;2§
lL-MI-3B-1 GCTACCCTGTAGCCCGCTGCAGGCCTACCCGCCGGTGGACGCCT-AAACTCTTGTTTTTIAGTGAI-IA
lL-MI-3-25-1 GCTACCCTGTAGCCCGCTGCAGGCCIACCCGCCGGTGGACGCCT-AAACTCTTGTTTTTCAGTGAT-ta
lL-MI-B-l GCTACCCTGTAGCCCGCTGCAGGCCIACCCGCCGGTGGACGCCT-AAACTCTTGTTTTTCAGTGAT-IA
IL-CA30 GCTACCCTGTAGCCCGCTGCAGGCCTACCCGCCGGTGGACACTT-AAACTCTTGTTTTTEAGTGAE-IA
lL-EAZ GCIACCCTGTAGCCCGCTGCAGGCCTACCCGCCGGTGGACGCCT-AAACTCTTGTTTTTEAGTGAT—IA
E.lqptqplaca GCTACGCTGTAGCCCGCTGCAGGCCARCCCGCCGGTGGACTGTT-AAACTCTTGTTAIAGTGGAAC-T-
IVBMI-4-51-1 GGTACCCTGTA _-_ — - AGGACTACICHTUUAC ‘ -CATTAAACTCT-GTTTTTCTAIGAAACT

      

lV‘MI-Z-l GTTACCCTGTA --------- AGGACTACTCGTCGACGGAC-CATTAAACTCT-GTTTTTCTATGAAACT
lV‘MI-9-24-3 GCTACCCTGTA --------- AGGACTACTCGTCGACGGAC-CATTAAACTCT-GTTTTTCTAIGAAACT
IVFMI-BA-l GCTACCCTGIA --------- AGGACTACTCGTCGACGGAC-CATTAAACTCT-GTTTTTCTATGAAACT
lV-I302466 GCTACCCTGIA --------- AGGAAIACTCGTCGACGGAC-CATTAAACTCT-GTTTTTCTAIGAAACT

Diatrype 8p. GCTACCCTGTAGCCCGCTGCTGGCCGACCCGCCGGTGGACAGTA-AAACTCTTGTTTTTIAGTGAI-IA
Diatrypella lp.GCTACCCTGEAGCCCGCTGCTGGCCGACCCGCCGGTGGACAGTAPAAACTCTTGTTTTIAGTGGAI-IA

 

ITS2

381 440
lL-MI-7-1 TTGGGAGCTT--ATCTTC----GGAI—--AACTCCCCAAAAGCAICGGCGGAGTCGCGGTGGCCCCAAG
lL-MI-BB-l TTGGGAGCCT--ATCTTC--—-GGAI---AACTCCCCAAAAGCAICGGCGGAGTCGCGGTGGCCCCAAG
lL-MI-3-25-1 TTGGGAGCTT--AICTTC----GGA!---AACTCCCCARAAGCAICGGCGGAGTCGCGGTGGCCCCAAG
lL-MI-B-l TTGGGAGCTT--ATCTTC----GGAI---AACTCCCCAAAAGCATCGGCGGAGTCGCGGTGGCCCCAAG
lL-CABO TTGGGAGCCT—-A£CTCC----GGAI---AGCTCCTCAAAAGCAITGGCGGAGTCGCGGTGACCCCAAG
lL-EAZ TTGGGAGCCT--ATCTTC----GGA!---AACTCCCCAAAAGCAECGGCGGAGTCGCGGTGGCCCCAAG
E.lqptqplaca TTGGGAGTTTAC ------ CTGCGGGT---AA£TCCTGAAAAGCAICGGCGGAGTCGTGTTGGCCCCAAG
IVBMI-l-Sl-l TTGGGAGCTIAC-ocrmcnarrmCGGGATAAPchr ~17
IVBMI-Z-l TTGGGAGCTTAC-CCTGCAGTTGCGGGATAACTCCTCAAATAIAITGGCGGAGTCGCGGAGACCCEAAG
IVHMI-9-24-3 TTGGGAGCTTAC-CCTGCAGTTGCGGGAIAACTCCTCAAAIAEAITGGCGGAGTCGCGGAGACCCEAAG
IVBMI-3A-1 TTGGGAGCTTAC-CCTGCAGTTGCGGGAIAACTCCTCAAATAIAITGGCGGAGTCGCGGAGACCCIAAG
lV-1302466 TTGGGAGCTIAC-CCTGCAGTTGCGGGAEAACTCCTCAAATAIAITGGCGGAGTCGCGGAGACCCtAAG

Diatrypc 8p. TTGGGAGCCTGC-CCCCCCAGGGGA--GCAGCTCCTCAAAGCIAITGGCGGAGTCGTAITGGCCCIAAG
Diatrypolla 8p.TTGGGAGCCTGCACCCCCCCGGGGGCTGCAGCTCCTCAAAGCIAITGGCGGAGTCGthmrcGCCCIAAG

ITS2

441 510
lL-MI-7-1 ccrncmnnmrrrr-ccTcccTg;;AggggggggagggggggggggggccccmAAAAcccccmamrrrcr
lL-MI-3B-1 ccrncranmrrrr-ccrcccrr--AGGTGTGCTACGGTCGACGTCcracccmannncccccmarrrrcr
nL-M1-3-25-1 ccwacranrrrTr-ccrcccrr--AccrcrccmacGarccnccrccrccccmnnnacccccmnmrrrcr
nL-M1-8-1 ccrncraarrrrr-ccrcccrr--AccrcrccmacGarccnccrccrccccmanaacccccmnrrrrcr
lL-CABO ccrncrnnrrcrr-crcccrwr--AccrcrcrcnccccrcaccrcTrccccrrnnacccccanmrrrrr
lL-PAZ ccracmnnrrrrr-ccrcccrr--Accrcrcccnccccccaccrccrccccmaanncccccrnrrrrcr

E.1qptqplaca CGTAGTAATTTTT--CTCGCTTCAGGTGGTTCCAGCGCTGGCGTCCAGCCGCTAAACCCCCTAITCTTT
IV‘MI-4-Sl-1 CGIAGTAATTCTT--CTCGCTT-TAGTAGTGTEAACGCTGGCATCTGGCCACTAAACCCCTAAITTTIA

lV-MI-2-1 CGTAGTAATTCTT--CTCGCTT-TAGTAGTGTCAACGCTGGCATCTGGCCACTAAACCCCTAAITTTIA
lV‘MI-9-24-3 CGIAGTAATTCTT--CTCGCTT-TAGTAGTGTTAACGCTGGCATCTGGCCACTAAACCCCTAATTTTIL
lV‘MI-3A-1 CGTAGTAATTCTT--CTCGCTT-TAGTAGTGTCAACGCTGGCATCTGGCCACTAAACCCCTAAITTTIA
lV-I302466 CGTAGTAATTCTT--CTCGCTT-TAGTAGTGTTAACGCTGGCATCTGGCCACTAAACCCCTAAQTTTIA

Diatrypo lp. CGTAGTAAITTTTTCCTCGCTTCTAGTGGTTCCAGTGCTGGCGTCCAGCCGIAAAACCCCTAAITTTCT
Diatrypolla 8p.CGTAGTAATTTTTTCCTCGCTTCTAGTGGTTCCAGTGCTGGCGTCCAGCCGIAAAACCCCTAAITTTCT

Figure 4.1. Alignment of internal transcribed spacers (ITSl and ITSZ) and 5.8S
ribosomal DNA sequences of Eutypa lata and Eutypella vitis used to design species-
specific primers. Sequences selected for Eutypa lata-specific primers EL] and EL4 are
underlined and sequences for El. vitis-specific primers EVl and EV4 are highlighted by
black boxes. Eutypa lata and El. vitis isolates are designated by EL and EV, respectively,
followed by isolate numbers. Other isolates (with GenBank accession numbers) include
EL-WB457 (AF 455427), EL-1302466 (AJ302466), E. leptoplaca (AY684229), Diatrype
sp. (AY684241), and Diatrypella sp.(AY684240).

139

Table 4.2. The sequence, guanine-cytosine percentage (%GC), and calculated melting

temperature (Tm) of the pairs of species-specific primers for Eutypa lata and Eutypella
vitis used in PCR amplifications.

 

 

 

Species Primer Sequence % GC Tm
5' 3'
E utypa lata EL 1 GACGCCTAAACTCTTG I'I'I'I'I’CAGTGATTA 37 57
EL4 AGGACGTCGACCGTAGCACACCTA 58 63
Eutypella vitis EVl CCTGTAAGGACTACTCGTCGAC 55 57

EV4 AGGAGTTATCCCGCAACTGCAG 55 57

140

 

 

 

|I§1fi 41%

Figure 4.2. Diagram showing the positions of the nested, species-specific primers for
Eutypa lata (ELI, EL4) and Eutypella vitis (EVl, EV4); and the universal primers, ITSlF
and ITS4, within the ITS regions. For the nested multiplex PCR, products from the first
round of amplification with the universal ITSlF and ITS4 primers are used as template in
the second round with all of the species-specific primers in a multiplex reaction. Primer
pairs ELI-EL4 and EVl-EV4 yield amplification products of 350 and 300 bp,
respectively.

141

Nested multiplex PCR on naturally infected vines

The species-specific primers were tested on naturally infected vines for their ability
to detect E. lata or El. vitis. The first round of nested multiplex PCR of samples fiom
cankers with visible stromata (Figure 4.3a) resulted in visible products of the ITS
amplicon (approximately 600 bp) in 7 of the 14 samples (lanes 3, 4, 8, and 11-14). The
second round of PCR (Figure 4.3b) resulted in visible products for 13 of the 14 samples:
8 samples (lanes 3-6, 12-15) with single bands indicating El. vitis (approximately 300 bp)
and 5 samples (lanes 7-11) with single bands indicating E. lata (approximately 350 bp).
The lone exception was sample 1 (Figure 4.3, lane 2) where the diagnostic markers for E.
lata and El. vitis were not amplified. Subsequently, we were not able to culture either
fungus fiom the sample. The absence of the fimgi in the sample would explain the
negative PCR result. Sequencing of the PCR products (GenBank accession numbers
DQ65 8371-DQ6583 83) as well as morphological examination and PCR tests of single-
ascospore cultures isolated from stromata next to sampling sites on the vines validated
the results of the PCR technique (data not shown).

The presence of visible stromata was not necessary for a positive detection as every
canker without visible stromata was positive for the presence of either E. lata, El. vitis, or
both (Figure 4.4). Eutypella vitis was present in the margin of four cankers and was found
in a total of five of the twelve cankers. Eutypa lata was present in ten of the twelve
cankers. Both fungi were present in three of the cankers, with both fungi in the margin of
two and El. vitis in the margin and E. lata in the center of one canker. Sequencing of the
amplification products (GenBank accession numbers DQ65 83 84-DQ658409) confirmed

that the identity of the fungi correlated with the size of the amplicon. Of the 24 wood

142

12345678910111213141516171819

600 bp-)

B
600 bp-)

400 bp-)
300 bp-)

 

Figure 4.3. Detection of Eutypa lata and Eutypella vitis in naturally infected grapevine
cankers with visible stromata using nested multiplex PCR, A) PCR products from the
first step using universal primers ITSlF and ITS4; and B) PCR products from the second
step using species-specific primers EV], EV4, EM, and EL4. Lane 1: lKB+ DNA
ladder; lanes 2-15: wood samples from infected vines with visible stromata; lane 16:
wood sample from apparently healthy vine (negative control); lane 17: E. lata DNA
(positive control); lane 18: El. vitis DNA (positive control); lane 19: 1 KB+ DNA ladder.

143

A1234567891011121314151617
600bp-)

400 bp-)
300 bp->

600 bp-)

400 bp-)
300 bp-)

 

Figure 4.4. Detection of Eutypa lata and Eutypella vitis in cankers without visible
stromata from naturally infected grapevine using nested multiplex PCR, A) PCR products
from the centers of the cankers and B) PCR products from the margins of the cankers.
Lane 1: 1KB+ DNA ladder; lanes 2-13: wood samples from grapevine cankers without
visible stromata; lane 14: wood sample from apparently healthy vine (negative control);
lane 15: E. lata DNA (positive control); lane 16: El. vitis DNA (positive control); lane

17: 1 KB+ DNA ladder.

chips cultured, only three yielded pure cultures of E. lata or El. vitis. The morphological
characteristics of the three isolates agreed with the results obtained from the nested
multiplex PCR; one isolate of E. lata (from the center of a canker corresponding to
Figure 4.4A, lane 11) and two isolates of El. vitis (from the margin of cankers
corresponding to Figure 4.48, lanes 3 and 6). Blast searches were performed using all the
sequences obtained from the PCR from the stromata and the PCR from the wood. The
300 bp amplicon sequences had the highest sequence homology with isolates of El. vitis
(99-100% homology), and the 350 bp amplicon sequences had the highest sequence
homology with isolates of E. lata (93-100% homology). Results of the nested multiplex

PCR are summarized in Table 4.3.

Nested multiplex PCR on artificially inoculated vines.

The species-specific primers were tested in a nested multiplex reaction with V.
labrusca ‘Concord’ canes that were inoculated with 2 isolates of El. vitis and 2 isolates of
E. lata. For each inoculated cane, a PCR product was produced from DNA extracted 5
mm below the site of inoculation and was the correct size for the species used (300 bp for
EV7O and EV339, 350 bp for E30 and EL130) [Figure 4.5]. Isolation from the wood
tissue was less successful with 3 of 4 canes yielding EV70, 1 of 4 canes yielding EV339,

2 of 4 canes yielding EL130, and 3 of 4 canes yielding E30.

145

Table 4.3. Comparison of traditional diagnostic techniques to the nested multiplex PCR
for detection of Eutypa lata and Eutypella vitis in naturally infected, symptomatic
grapevines (Vitis labrusca ‘Concord’) in Michigan.

 

Detection of fungal species in

 

 

samplesll

Diagnostic technique Success Ratio E. lata E1. vitis both
Cankers with stromatab

Single ascospore isolation 13/14 5/14 8/14 0/14

PCR on stromatac 13/14 5/14 8/14 0/14
Cankers without stromatad

Isolation from wood from margin of canker 2/12 0/ 12 2/12 0/ 12

PCR of wood from margin of canker" 12/ 12 10/ 12 4/ 12 2/ 12

Isolation from wood from center of canker 1/ 12 1/ 12 0/ 12 O/ 12

PCR of wood from center of canker‘ 12/ 12 8/12 4/ 12 1/12

 

a Morphology of cultures and sequences of PCR products amplified with species-specific
primers were used to determine identity.
Fourteen cankers were sampled, 1 from each of 14 different vines.
° PCR results shown in Figure 4.3.
d Twelve cankers were sampled, 1 from each of 12 different vines
° PCR results shown in Figure 4.4.

146

EV70 EV339 EL130 E30 - control

 

'12 3 4 5 6 7 8 9101112131415161718192021222324

600bp')

400bp->
300bp')

 

Figure 4.5. Detection of Eutypa lata and Eutypella vitis from inoculated Vitis labrusca
‘Concord’ canes using nested multiplex PCR. Lane 1: 1KB+ DNA ladder; lanes 2-5:
PCR product from canes inoculated with El. vitis isolate EV70; lanes 6-9: PCR product
from canes inoculated with El. vitis isolate EV339; lane 10: El. vitis DNA (positive
control); lanes 11-14: PCR product from canes inoculated with E. lata isolate EL130;
Lanes 15-18: PCR product from canes inoculated with E. lata isolate E30; lane 19: E.
lata DNA (positive control); lanes 20-23; PCR product fi'om mock-inoculated canes
(negative controls); lane 24: 1 KB+ DNA ladder.

147

Sensitivity of standard compared to nested multiplex PCR.

Using pure DNA extracted from cultures, nested multiplex PCR was consistently
1000 times more sensitive than standard PCR, with an amplicon obtained with 1
femtogram pathogen DNA (Figure 4.6). In reactions with DNA from both fimgi, the 300
and 350 bp products were both present in all reactions with visible products, indicating

that there was no competition between primers (Figure 4.6).

148

l
y
E

no DNA
9

9

no DNA
‘b 30 pg
°' 3 Pg
0 300 f9
‘1 30 f9
°° 3 to
3 300 ag
o 30 as
N 3 n9
"’ 300 P9
‘5 30 pg
0- 3 P9
fi 300 f9
‘1 30 fg
0° 3 fg
'3 300 a
o 30 a9
2 no DNA

a 300 pg
3
-l

N3ng
#3099
0'3P9
w300fg
~130fg
0°3fg
3300a
o30ag
N3ng
“30099

1

>

600 bp-)

400 bp-)
300 bp-)

600 bp-)

400 bp—> ..
300 bp-) “... ...-.- .....II “'

 

Figure 4.6. Sensitivity of standard PCR and nested multiplex PCR for the detection of
Eutypa lata and Eutypella vitis. A) PCR products from standard PCR and B) PCR
products from nested multiplex PCR. Lane 1: 1 KB+ DNA ladder, Lane 2-10: serial
dilution of genomic DNA; Lane 2: 3 ng, Lane 3: 300 pg, Lane 4: 30 pg, Lane 5: 3 pg,
Lane 6: 300 fg, Lane 7: 30 fg, Lane 8: 3 fg, Lane 9: 300 ag, Lane 10: 30 ag, Lane 11:
water control.

149

DISCUSSION

In this study, we developed molecular tools for detection and identification of E. lata
and El. vitis, two fungi that occur on grapevines and are difficult to separate based on
culture appearance or morphological characteristics. While the role of El. vitis in Eutypa
dieback development is still unclear, preliminary studies indicate that the fungus is
pathogenic to grapes (29). Species-specific primers designed from the ITS regions of
ribosomal DNA clearly distinguished the two species in PCR assays of DNA extracted
from mycelium and stromata on wood. The assay was also able to detect these species in
cankers without visible stromata on naturally infected vines, demonstrating its utility as a
diagnostic tool. This is the first known report of the presence of El. vitis in grapevines
with Eutypa dieback symptoms. The presence of El. vitis in the center and margin of
several cankers tested also suggests that this fungus is acting as a primary pathogen of
grapevines. The nested multiplex PCR approach was also successfully used for the
detection of E. lata and El. vitis mycelium in artificially inoculated, potted grapevine
plants indicating the usefulness of this technique in laboratory and greenhouse
experiments. This also shows that El. vitis is capable of colonizing healthy grapevines
tissue, further lending evidence that El. vitis is pathogenic on grapes. Future work will
need to be carried out to determine the incidence of El. vitis in Eutypa dieback cankers in
Michigan. To date, no adequate survey of the co-colonizers of Eutypa dieback cankers
has been conducted in Michigan. The expression of disease symptoms in ‘Concord’ vines
in Michigan is variable between vines and years. It is possible that shoot symptoms are
affected by the presence of other fungi like El. vitis in cankers. Further research needs to

be done before we can fully understand the complexity of Eutypa dieback in the region.

150

Sequencing of the ITS region of numerous E. lata and El. vitis isolates confirmed
that these two fungi are genetically distinct. Analysis of the sequences showed that there
is almost no variability in the ITS regions of El. vitis and that the isolates clearly
represent one species. In contrast, we observed variability in the ITS regions of isolates of
E. lata from three geographic regions in the United States. This was not surprising since
genetic variation was also found among isolates from different geographic locations by
Peros et al. (3 9, 40). Genetic variation was also common among isolates from the same
vineyard and even from the same stroma (40, 41). Eutypa lata is considered a randomly
mating species with a high degree of genetic diversity as shown with RFLP and RAPD
markers, vegetative compatibility and pathogenicity tests (41 , 42). DeScenzo et a1. (15)
suggests that E. lata is not a single species and may be evolving into different species, but
this has since been refuted by a more recent assessment of the species concept of E. lata
that supports the merging of E. armeniacae and E. lata as a single species (47). More
genetic data may help to clarify the status of this species. However, sequences from
isolates from other parts of the world are needed for phylogenetic analysis of variation in
E. lata, as the sequences available in GenBank are limited.

Possible primer sites were highly conserved among the El. vitis isolates from
different areas, simplifying the design of species-specific primers for this species.
However, it was much more difficult to design species-specific primers for E. lata
because primer sites that were conserved among all isolates of E. lata were rare,
especially within the ITS2 region. This variation among isolates of E. lata could be a
potential problem when using ITS-derived primers. With the primer set that we have

developed, we were able to amplify the expected target from all the isolates tested,

151

including those fi'om California and Pennsylvania. We have also been able to
successfully amplify E. lata from infected vines from Pennsylvania (data not shown). We
did not observe any cross-reaction with DNA of other fungi even when the PCR was
carried out with pure DNA extracted from mycelium. We expect that primers ELI and
EL4 will amplify E. lata found in North America but are not confident that they will
amplify isolates from other grape-growing regions. Some isolates from Europe
(AY787699, AF 09991 1) and Australia (AJ 302446) have indels in the primer annealing
sites that may result in a lack of amplification with the E. lata-specific primers. The
reliability of the primer set needs to be confirmed with isolates from other parts of the
world before being used for diagnostic purposes in these locations.

While the ITS regions provide an excellent site for developing species-specific
primers, there are risks with such a strategy. Whereas Rolshausen et a1. (42) were unable
to distinguish between E lata and E. laevata with PCR-RFLP, our species-specific
primers did not amplify a product from DNA extracted from E. laevata and can be used
to distinguish these two species.

In this study, PCR assays with species-specific primers allowed for the identification
and differentiation of both fungi from mycelial cultures in less than 3 hours without the
need for visual comparison with reference cultures. Furthermore, the primers were highly
effective in detecting their respective target fungi in wood samples using a nested
multiplex PCR approach, thus avoiding the time-consuming process of isolation and
identification that would take weeks. Earlier attempts to amplify DNA directly from
woody tissue using species-specific primers in a standard PCR reaction yielded weak or

no products in the majority of the samples (data not shown). Amplification of the fungal

152

DNA with universal fungal primers before using the species-specific primers greatly
increased the sensitivity and effectiveness of the detection method. Even though the first
round of amplification did not always yield visible bands on a gel, all but one of the
samples yielded a product in the second round. Hamelin et al. (26) proposed that this
increase in efficiency could be due to an increase in the ratio of target DNA to non-target
DNA from the first round of amplification. The first round of amplification might also be
useful when the concentration of fungal DNA is very low or when PCR inhibitors are
present in the plant tissue or extraction buffer, necessitating dilution of the sample. Using
serial dilutions of pure fungal genomic DNA, we found that the multiplex nested PCR
was at least 1000 fold more sensitive than standard PCR. Another advantage of using a
multiplex reaction is a reduction in the number of PCR samples that have to be run,
saving in the cost of PCR and gel components as well as labor.

Since we have found E. lata and El. vitis on the same vine, there was a concern that
the multiplex reaction would not amplify both products if present in the same sample, a
problem that has been reported in multiplex reactions in other species (5, 26). When
DNA of E. lata and El. vitis was present in equivalent amounts in a reaction, both PCR
products were amplified, indicating no competition between the two amplicons. This
work has significantly added to, and enhanced, the growing pool of literature pertaining
to diagnostic techniques developed for Eutypa dieback. As we begin to understand the
complexity of species involved in the co-colonization of grapevines with E. lata, we will
be able to refine and further develop molecular diagnostic techniques to characterize

these interactions.

153

10.

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158

CHAPTER 5: MORPHOLOGY, CULTURE CHARACTERISTICS,
PATHOGENICITY, SECONDARY METABOLITE PROFILE, AND GENETIC
VARIABILITY OF EUTYPELLA VITIS.
Abstract

While Eutypa lata is considered to be the primary cause of Eutypa dieback, a
second fungus, Eutypella vitis, has been frequently isolated from grapevines in Michigan
with Eutypa dieback symptoms. Ascospores of El. vitis were indistinguishable by
appearance from E. lata with lengths for El. vitis ranging between 9.1 to 9.7 pm long and
1.8 to 2.2 urn wide, larger in size than those of E. lata (7.6 to 7.9 mm long and 1.7 to 1.8
pm wide). Conidia produced on artificial media were indistinguishable in both size and
appearance between species. Conidia of El. vitis, produced on artificial media, were
indistinguishable by appearance from E. lata with lengths for El. vitis ranging between
16.1 to 21.5 um long and 0.45 to 0.58 pm wide, larger in size than those of E. lata (15.5
to 17.5 um long and 0.51 to 0.58 um wide). Growth rates of isolates of El. vitis on
artificial media ranged from 12.8 to 27.1 mm per day with an average of 19.7 mm, while
isolates of E. lata ranged from 5.1 to 17.2 mm per day with an average of 12.7 mm. The
pathogenicity of El. vitis was confirmed, using inoculation of both potted canes and
mature vines with mycelia and ascospores as inoculum. Michigan isolates of Eutypella
vitis produced vascular necrosis in the wood similar in size and appearance to Michigan
isolates of E. lata, and were successfully re-isolated fiom necrotic tissue. Grapevine
cultivar screens indicated that ‘Niagara’, followed by ‘Seyval.’ Phylogenetic studies of
the ITS regions and coding region of the B-tubulin gene indicated that the Michigan

population of El. vitis is not clonal, and is likely reproducing sexually.

159

Introduction
The fungal family Diatrypaceae contains nine accepted genera including the
economically important Diatrype, Diatrypella, Cryptosphaeria, Eutypella, and Eutypa
(2). The order contains a number of plant pathogens as well as saprophytic fungi that are
commonly found worldwide on a diverse number of dead or declining woody
angiospenns. Some pathogens in the order are believed to have a very limited host range.
Diatrypella betulina (Peck) Sacc., for example, is reported to cause disease in only birch
tree species (Betula spp.). Other diatrypacous pathogens, like Eutypa lata Pers. Tul. & C.
Tul (syn. Eutypa armeniacae Hansf. & Carter), are known to cause disease on numerous
hosts (4).
A recent phylogenetic study utilizing rDNA-ITS sequences found that the genetic

data from diatrypaceous fimgi did not correlate with current taxonomic schemes (1).
Eutypa formed two phylogenetic clades with one containing E. lata and related species
(E. armeniacae, E. laevata, and E. petrakii), while members of the genus Eutypella
appeared as an unstable monophyletic group which was lost when the sequence
alignment was subjected to neighbor-joining analysis (1). In another study, E. lata
formed a grouped with several taxa including, E. armeniacae, E. laevata, and E. petrakii
var. petrakii (23) Members of this group were all shown to produce phytotoxic secondary
metabolites implicated in the virulence of E. lata. (23).

Single-ascospore isolations from perithecia found on dead wood of ‘Concord’ vines
in vineyards with Eutypa dieback in Michigan yielded two fungi in the family
Diatrypaceae: E. lata and Eutypella vitis (Schwein.:Fr.) Ellis & Everh. (3). Rappaz (22)

listed El. vitis as a synonym of Eutypella aequilinearis (Schwein.: Fr.) Starb. with El.

160

vitis having slightly larger ascospores than El. aequilinearis (>10 um and 6.5-10.5 pm
respectively) (5, 10, 28). Eutypella vitis was previously reported on wood of a ‘Concord’
vine in Illinois (10). The United States National Fungal Herbarium contains two
specimens of El. vitis collected from Vitis sp. in Paw Paw, Michigan in 1907, and in
Lawton, Michigan in 1908. Other specimens of this fungus in the herbarium originate
from Vitis labrusca in Virginia; Vitis sp. in Maryland, South Carolina, Virginia, and Italy;
Vitis rotundifolia Michx. in Alabama; and Vitis vinifera L. in Pakistan (information is
available online at the Fungal Databases, Systematic Botany and Mycology Laboratory,
Agricultural Research Service, US. Department of Agriculture). However, most of these
specimens were collected in the early 19005, and no reference was made to any disease
symptoms on the plant or the plant tissues on which they were found. The pathogenicity
of El. vitis on grapes is unknown.

Recently, two other Diatrypaceous fungi, Eutypa leptoplaca (Mont) Rappaz and
Cryptovalsa ampelina Nitschke, were characterized as necrotrophic pathogens on grapes
(16, 27), indicating the possibility that Eutypa dieback symptoms, or at least cankers, can
be caused by fungi other than E. lata. If El. vitis is found to be a primary pathogen of
grapes, it may differ from E. lata in its symptomology and epidemiology, possibly
requiring different management strategies. The objective of this study was to characterize
several properties of El. vitis such as culture characteristics, growth rate in culture,
pathogenicity, production of secondary metabolites, vegetative compatibility, and genetic

variability.

161

Materials and Methods
Selection of isolates

Eutypa lata and El. vitis isolates were obtained from three different ‘Concord’
vineyards in southwest Michigan (Lawton, Baroda, and Schoolcraft). Vine trunks and
branches with cankers exhibiting visible stromata were collected and brought back to the
laboratory. Pieces of wood with perithecia were soaked in sterile water for 6-8 hours.
Individual perithecia were transferred with sterile forceps to a microscope slide and
crushed in 50 141 water by gently pressing the cover slip. The ascospore suspension was
transferred to a 2-ml centrifuge tube and spore concentration was determined with an
improved Neubauer hemacytometer (American Optical Co., New York, NY). Ascospores
were then diluted to 102 and 103 spores per 100 pl and plated onto potato dextrose agar
(PDA) amended with aqueous streptomycin sulfate (20 mg L'l) to prevent bacterial
growth. The plates were incubated at room temperature and checked for fimgal growth
daily for 4 days. Hyphal tips from single ascospore colonies were subcultured onto PDA.
Fungal isolates from other geographic regions were also obtained for use in this study
(Table 5.1). All isolates were stored in 15% glycerol at -80°C and in sterile water at room
temperature. Isolates used in this study were selected fi'om different individual vines to

maximize variability.

162

Table 5.1. Species, origin, host, and collection date for Eutypa lata and Eutypella vitis
isolates used in this study.

 

 

Date

Isolate Fungal species Origin Host Collected
E303 Eutypa lata California Vitis vinifera -°
E31a E. lata California Vitis vinifera -
E38a E. lata California Vitis vinifera -
ELA4 E. lata Lawton, MI Vitis labrusca 'Concord' 3/15/06
ELA8 E. lata Lawton, MI Vitis labrusca 'Concord' 3/15/06
ELI E. lata Lawton, MI Vitis labrusca 'Concord' 3/05/02
EL5 E. lata Lawton, MI Vitis labrusca 'Concord' 3/05/02
EL19 E. lata Lawton, MI Vitis labrusca 'Concord' 3/10/02
EL45 E. lata Lawton, MI Vitis labrusca 'Concord' 3/15/02
EL55 E. lata Lawton, MI Vitis labrusca 'Concord' 3/04/02
EL64" E. lata Erie, PA Vitis sp. -
EL67b E. lata Erie, PA Vitis sp. -
EL69 E. lata Lawton, MI Vitis labrusca 'Concord' 1/23/03
EL130 E. lata Lawton, MI Vitis labrusca 'Concord' 3/08/03
ELI84 E. lata Lawton, MI Vitis labrusca 'Concord' 3/06/03
EL198 E. lata Lawton, MI Vitis labrusca 'Concord' 3/07/03
EL302 E. lata Baroda, MI Vitis labrusca 'Concord' 3/17/03
EL316 E. lata Baroda, MI Vitis labrusca 'Concord' 3/26/03
EL318 E. lata Baroda, MI Vitis labrusca 'Concord' 3/26/03
EVA] Eutypella vitis Lawton, MI Vitis labrusca 'Concord' 3/15/06
EVA2 El. vitis Lawton, MI Vitis labrusca 'Concord' 3/15/06
EVAS El. vitis Lawton, MI Vitis labrusca 'Concord' 3/15/06
EVA6 El. vitis Lawton, MI Vitis labrusca 'Concord' 3/15/06
EVA9 El. vitis Lawton, MI Vitis labrusca 'Concord' 3/15/06
EVA12 El. vitis Lawton, MI Vitis labrusca 'Concord' 3/15/06
EV66 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/26/03
EV70 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/23/03
EV71 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/26/03
EV72 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/26/03
EV73 El. vitis Lawton, MI Vitis labrusca 'Concord' l/24/03
EV74 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/24/03

 

a Isolates provided by F. Trouillas and D. Gubler, University of California, Davis.

b Isolates provided by B. Hed, Penn State University, Lake Erie Regional Grape Research
and Extension Center.

° Indicates unknown date of collection.

163

Table 5.1 (cont’d)

 

 

Date
Isolate Fungal species Origin Host Collected
EV76 Eutypella vitis Lawton, MI Vitis labrusca 'Concord' 1/23/03
EV77 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/26/03
EV79 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/26/03
EV81 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/26/03
EV85 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/23/03
EV89 El. vitis Lawton, MI Vitis labrusca 'Concord' 1/23/03
EV229 El. vitis Baroda, MI Vitis labrusca 'Concord' 2/21/03
EV231 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/27/03
EV232 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/23/03
EV236 El. vitis Baroda, MI Vitis labrusca 'Concord' 2/23/03
EV238 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 3/03/03
EV239 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 3/03/03
EV257 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/21/03
EV258 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/21/03
EV266 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 3/01/03
EV268 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 3/01/03
EV270 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 3/01/03
EV279 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/23/03
EV29O El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/21/03
EV293 El. vitis Schoolcraft, MI Vitis labrusca 'Concord’ 2/21/03
EV295 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/21/03
EV300 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/21/03
EV325 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/24/03
EV329 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/26/03
EV330 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/24/03
EV331 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/24/03
EV334 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/26/03
EV336 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/24/03
EV337 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/24/03
EV339 El. vitis Baroda, MI Vitis labrusca 'Concord' 2/27/03
EV344 El. vitis Schoolcraft, MI Vitis labrusca 'Concord' 2/23/03
EV346 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/26/03
EV348 El. vitis Baroda, MI Vitis labrusca 'Concord' 3/26/03

 

I64

Stromata! surface characteristics

The stromatal surfaces of E. lata and El. vitis, on wood samples collected from
from the Lawton vineyard in Southwest Michigan on March 3 of 2006, were examined
by scanning electron microscopy (SEM). Samples were prepared for SEM by gold
coating and air drying with an EMSCOPE SC500 Sputter coater (Ashford, Kent, Great
Britain), and SEM was performed on a JEOL 6300F scanning electron microscope

(Oxford EDS, Oxfordshire, UK).

Culture characteristics and growth rate

Isolates of E. lata and El. vitis were grown on ampicillin-amended PDA, and cultures
were observed for production of conidia after 30 days of growth in the dark at 20°C. Fifty
conidia per isolate were measured using an ocular micrometer on a compound
microscope. To determine the growth rate of cultures, a 3-mm diameter mycelial plug
was cut from the edge of 5-day-old, actively growing cultures and placed onto the center
of a Petri plate containing PDA. Three plates of each isolate were grown in the dark at
22°C. The colony diameter was measured after three and five days, and the radial growth
rate per day was calculated as the difference in diameter from day 3 to day 5 divided by
2. The experiment was repeated once and analysis of variance (ANOVA) was conducted

using SAS Version 9.1.3 (SAS Institute, Cary, NC).

165

Greenhouse pathogenicity screening

Forty three isolates of El. vitis and 19 isolates of E. lata were screened for
pathogenicity on potted ‘Concord’ canes. Healthy, l-year-old canes with two intact nodes
per cane were collected from ‘Concord’ vineyards in the winters of 2004-2005 and 2005-
2006. Prior to inoculation, a 3-mm diameter hole approximately 2 cm below the upper
node of each cane was made using a cordless drill (Black and Decker, Towson, MD).
Fungal isolates were grown for 5 days on water agar, and a 3-mm plug of agar with
mycelium was inserted into the hole. Inoculated wounds were then sealed with parafihn.
The bottom portion of the cane was removed at leaf scar below the bottom node of each
cane and then dipped in Hormodin 2 rooting powder (E. C. Geiger Inc., Harleysville, PA)
The cane was then planted in a mixture of 2 parts sand and 1 part potting mix (Bacto soil,
Southwest Fertilizer, Houston, TX) in an 20.3 cm pot. Four canes inoculated with the
same isolate were planted in each pot, with a total of 4 pots (16 canes) for each isolate. A
negative control consisted of canes inoculated with a sterile plug of agar, and an
untreated control included canes that were not drilled with an inoculation hole. The pots
were set up in a random complete block design and maintained under greenhouse
conditions. After six months, the bark was removed fiom the cane, and the length of
tissue necrosis around the site of inoculation was measured. Tissue approximately 5 mm
above and below the edge of the site of inoculation, was removed, surface sterilized, and
plated onto ampicillin-amended PDA. The presence or absence of E. lata or El. vitis was
determined after 1 week of incubation based on morphological characteristics. The trial
was repeated once, and statistical analyses were performed using the GLM procedure and

Tukey’s HSD test in SAS.

166

Vineyard pathogenicity screening

Two separate experiments were conducted to determine the pathogenicity of El. vitis
on 8-year-old Concord grapevines in 2006 at the Clarksville Horticultural Experiment
Station in Clarksville, MI. Four isolates of El. vitis (EV70, EV89, EV257, EV339) and 2
isolates of E. lata (EL45 and EL130) were selected for the first experiment based on
virulence in greenhouse trials. Eight-year-old vines were inoculated by drilling a 3-mm
diameter hole in one arm of the vine. Three mm Plugs of agar with mycelium were
inserted into the hole and sealed with parafilm. Six vines were inoculated per isolate. A
negative control treatment consisted of vines inoculated with a sterile plug of agar. After
1 year, the vines were observed for symptom development, cut down, and returned to the
lab for further study. In the lab, the length of vascular necrosis was measured and
necrotic tissue was removed, surface sterilized, and plated on ampicillin-amended agar.
Statistical analyses were performed using the GLM procedure and Tukey’s HSD test in
SAS.

For the second experiment, ascospore inoculum was collected from fi'uiting
bodies by using the technique described by Carter (4). Stromata of El. vitis and E. lata
were attached to the underside of the lid of a glass Petri dish using removeable adhesive
putty (3M, St. Paul, MN) and then soaked in distilled water for 1 hr. The distilled water
was poured off, and the Petri dish was kept at 4°C overnight. Ascospores discharged onto
the bottom of the plate were collected in sterile water in a microcentrifuge tube.
Ascospores were diluted to a concentration of 5 x 103 ascospores per millileter. The
treatments included six isolates of El. vitis (consisting of ascospores from the same

fruiting bodies as EVA], EVA2, EVA5, EVA6, EVA9, and EVA12), two isolates of E.

167

lata (ELA4, ELA8), 2 mixed suspensions of El. vitis and E. lata ascospores
(EVAl/ELA4, EVA5/ELA8). Water inoculation was used as a negative control. For each
treatment one l-year-old shoot on an 8-year-old vine (six vines per treatment) was pruned
and inoculated with 20 ul of ascospore suspension (approximately 100 ascospores).
Wounds were left unwrapped to mimic natural conditions. After 1 year, the vines were
cut down and returned to the lab for further study. In the lab, the length of vascular
necrosis was measured and necrotic tissue was removed, surface sterilized, and plated
onto ampicillin-amended PDA. Plates were checked for the presence of either El. vitis or
E. lata using morphological characteristics. Statistical analyses were performed using the

GLM procedure and Tukey’s HSD test in SAS.

C ultivar resistance screening

Four isolates of El. vitis (EV70, EV73, EV257, EV339) and 3 isolates of E. lata
(EL45, EL55, E30) were selected, based on virulence displayed on potted cuttings in the
greenhouse pathogenicity screens, for screening grapevine cultivars for disease resistance
and symptom expression. Cultivars were selected based on their popularity in Michigan
The following seven cultivars were selected; Concord, Niagara, Seyval, Vignoles,
Chardonel, Chardonnay, and White Riesling. Cuttings were taken in the winters of 2005-
2006 and 2006-2007 from the vineyards at the Southwest Michigan Research and
Extension Center in Benton Harbor, MI. Inoculations were conducted with mycelial
plugs of the above-mentioned isolates on potted cuttings with the same experimental
design and technique as previously described. Statistical analyses were performed using

the GLM procedure and Tukey’s HSD test in SAS.

168

Analysis of secondary metabolites in growth media

Secondary metabolites were collected from grth media and prepared for
GC/MS analysis with a protocol modified from Molyneux et al. (15). Isolates of E. lata
and El. vitis were grown on potato dextrose agar for 5 days prior to inoculation of grape
cane broth amended with sucrose. Grape cane broth was made by grinding ‘Concord’
grape canes in a blender (Waring Products, Torrington, CT) . One hundred grams of
ground cane was steeped in 1 liter of boiling water until it reached room temperature. The
liquid was then filtered through a Whatman no. 1 filter paper and 10 g of sucrose was
added prior to autoclaving. For each isolate, five plugs (6 mm diameter) of agar
containing mycelium were placed into a 250 mL Erlenmeyer flask with 50 mL of grape
cane broth amended with sucrose. Cultures were grown at 20°C in the dark on a shaker at
90 rpm. After 50 days, cultures were filtered through Whatman no. 1 paper and extracted
twice with 50 ml diethyl ether. The extracts were combined and placed into a hood
overnight to dry. The residue was dissolved in methanol (1 mL) and filtered through a
0.45 um syringe filter (Millipore, Billerica, MA).

Trimethylsily (TMS) derivatives of samples were prepared by adding 0.8 ml of
extracted metabolites in methanol to 100 uL dry pyridine (Sigma-Adrich) in a 2.0 mL
amber, crimp-seal vial (Supelco, Bellefonte, PA), to which was added 100 uL N-Methyl-
N-trifluoroacetamide (MSTFA) (Sigma-Aldrich). Vials were sealed, and the reaction
mixture was heated at 60°C for 1 h with periodic shaking to ensure complete dissolution
of all reactants. Analyses were performed on a Hewlett-Packard 5890 Series II instrument
equipped with a 5971 mass selective detector (MSD), and a 60-0.32 mm id 8E-30 (0.25

pm) fused Si capillary column (J&W Scientific, Folsom, CA). The column was held at an

169

initial temperature of 105°C for 0.2 min, ramped at 30°C/min for 0.5 min, programmed
from 120 to 300°C at 10°C/min, and held at the final temperature for 10 min. Helium was
used as the carrier gas with a head pressure of 60 psi. Derivatized samples (0.1-0.2 uL)
were introduced through an SGE model OC1-3 on-column injector held at ambient
temperature. The MSD was operated at 70 eV in the El mode with scanning fiom 75 to
600 amu at a sampling rate of 1.5 scans/sec. A post-injection delay of 7.0 min was set in
order to avoid MS data acquisition during elution of the solvent and derivatization
reagent.

Five secondary metabolites (eulatinol, siccayne, eutypine, eutypinol, and
eulatachromene) were screened for with following ion profiles:
eulatinol (mono-TMS derivative): RT 12.95 min, m/z 260 [M+] (100), 245 (45), 215 (4),
187 (4), 115 (5); siccayne (di-TMS derivative): RT 14.03 min, m/z 318 [M+] (100), 303
(46), 287 (5), 263 (6); eutypine (mono-TMS derivative): RT 13.77 min, m/z 258 [M+]
(100), 243 (83), 227 (8), 199 (41), 185 (26), 141 (11), 128 (12), 115 (13); eutypinol (di-
TMS derivative): RT 14.92 min, m/z 332 [M+] (100), 317 (50), 243 (76), 227 (11), 147
(11); eulatachromene (mono-TMS derivative): RT 12.08 min, m/z 262 [M+] (8), 247

(100), 173 (14), 158(5).

Vegetative compatibility

Isolates of E. lata and El. vitis were tested for vegetative compatibility. Isolates
were paired in all possible combinations on PDA for 2 weeks at room temperature in the
dark. Determination of vegetative compatibility was based on the formation of barrage

zones between isolates. Barrage zones indicate genetic differences between individual

170

isolates. Isolates that produced barrage zones were placed in different vegetative
compatibility groups (V CGs) (9). If no barrage was observed, isolates were placed in the

same VCG. Isolates were also paired with themselves as a control.

PCR amplification of the IT SI-5. SS-IT 81] region and coding region for the fl-tubulin gene
For DNA extraction and subsequent PCR, fungal isolates were grown for 7-10
days on PDA. Mycelium was scraped off and placed into 1.5-ml microcentrifirge tubes
and DNA was extracted according to the protocol described by Lee et al. (31). Details of
the DNA extraction are presented in chapter 4. DNA extracted from mycelium was
diluted 102 and 103 times in sterile water in preparation for PCR. The ITS regions and 5.8
8 gene of the nuclear ribosomal RNA operon (ITSI-5.8S-ITSII) were amplified with the
primers ITSlF (fungus specific: 5’-CTTGGTCATTTAGAGGAAGTAA-3’) and ITS4
(universal: 5’-TCCTCCGCTTATTGATATGC-3’) (7, 31). A partial sequence of the
coding region for the B-tubulin gene was amplified using the primer pairs Bt2a (5’-
GGTAACCAAATC GGTGCTGCT'ITC-3’) and Bth (5’-
ACCCTCAGTGTAGTGACCCTTGGC-3’) (8). PCR reactions were carried out in 25 ul
total volume consisting of 12.5 pl DNA dilution (template) and 12.5 pl PCR reaction
mixture. The reaction mixture contained PCR buffer (20 mM ammonium sulfate; 2.0 mM
MgC12; 50 mM Tris-HCI, pH 9.0; Epicentre Technologies, Madison, WI), 0.2 mM each
of dATP, dTTP, dGTP and dCTP; 0.5 uM each of primer, and 0.5 unit of Taq DNA
polymerase. The reactions were carried out in a DNA thermal cycler (Model 9600,
Perkin-Ehner Cetus, Norwalk, CT). The amplification protocol included an initial

denaturation at 95°C for 2 minutes followed by 30 cycles at 94°C for 1 min, 50°C for 30

171

3 min for the ITS primers (58 °C for the B-tubulin primers) , and 72°C for l min. The

reaction was completed by a 5-min extension at 72°C. PCR products were separated on
1.5% agarose (Gibco BRL, Grand Island, NY) in 1% TAE buffer (100 mM Tris, 12.5
mM sodium acetate and 1 mM EDTA, pH: 8.0) by gel electrophoresis. A l-kb plus DNA
ladder (GibcoBRL) was included in each gel as a DNA size standard. The gels were
stained with ethidium bromide, visualized by UV fluorescence, and photographed using
an AlphaImager imaging system (Alpha Innotech Corporation, San Leandro, CA). PCR
products were sequenced in both directions with the amplification primers by the
Genomics Technology Support Facility at Michigan State University. Sequences can be
found in Appendix F

Phylogenetic analyses were conducted using MEGA version 4.0 (25).
Phylogenetic trees were constructed using the maximum parsimony (UPGMA) (24). All
positions containing gaps and missing data were eliminated from the dataset. Bootstrap
analysis was performed with 1000 replicates. Consensus bootstrap trees were constructed
with branches corresponding to partitions reproduced in less than 60% bootstrap

replicates collapsed.

172

Results
Stromata] surface characteristics

Perithecia of El. vitis were erumpent through the grapevine bark, with 3-7 in a
valsoid arrangement (Figure 5.1). Ostioles were sulcate, with 3-4 lobes, and were much
longer than the roundish ostioles of E. lata. Ascospores of El. vitis were allantoid, slightly
curved, with small oil bodies near the ends, and indistinguishable in appearance from E.
lata (Figure 5.2). Ascospore size for El. vitis ranged from 9.1 to 9.7 um long and 1.8 to
2.2 um wide, larger in size than those of E. lata (7.6 to 7.9 pm long and 1.7 to 1.8 um

wide) (Table 5.2). These results agree with previous descriptions of El. vitis (10, 29).

Culture characteristics and growth rate

After one month of growth on PDA, cultures of El .vitis were typically white,
with thin, aerial mycelia, unlike E. lata, which produces white to cream-colored
mycelium that is appressed to the medium (Figure 5.3). Cultures of El. vitis produce dark
yellow to black areas in the media which can be seen on the underside of plates as dark
areas. Black subconical pycnidia were numerous in most cultures and produced orange
conidial masses. The conidia produced by El. vitis ranged in size from 16.0-21.5 um in
length and 0.45 -0.58 um in width, while conidia produced by E. lata ranged from 15.5-
17 .5 pm in length and 0.51-0.54 pm in width (Table 5 .2). Some abnormal culture variants
of El. vitis produced slick, appressed mycelium with no conidiomata (isolate EV270,
EV74, EV344) or excessive darkening of mycelium and medium (isolates EV300 and

EV232) greater than typical cultures (Figure 5.3).

173

Isolates of El. vitis and E. lata were grown on PDA to determine mycelial growth
rates (Table 5.2). Growth rates of isolates of El. vitis ranged from 12.8 to 27.1 mm per
day (EV344 and EV238, respectively) with an average of 19.7 mm per day for all El. vitis
isolates, while isolates of E. lata ranged from 5.1 to 17.2 mm per day (EL5 and EL198,
respectively) with an average of 12.7 mm per day for all isolates of E. lata. While growth
rates overlapped, there was a significant difference in the growth rates between isolates of

E. lata and El. vitis as determined by AN OVA (P _<_ 0.05) (Appendix E).

174

 

 

Figure 5.1. Fruiting bodies of Eutypa lata and Eutypella vitis on the surface of grapevine
wood. A) Stromata and perithecia of E. lata, B) Stromata and perithecia of El. vitis, C)
Scanning electron microscope image of the ostioles of E. lata, D) Scanning electron
microscope image of erLunpent, 3-4 sulcate ostioles of El. vitis. Images in this dissertation
are presented in color.

175

 

 

Figure 5.2. Compound microscope images of asci, ascospores, and conidia of Eutypa lata
and Eutypella vitis from grapevine. A) Ascus of E. lata with eight ascospores, B) Ascus
of El. vitis with eight ascospores, C) Ascospores of E. lata, D) Ascospores of El. vitis, E)
Hyaline conidia of E. lata, F) Hyaline conidia of El. vitis.

176

 

Figure 5.3. Culture characteristics of Eutqua lata and Eutypella vitis isolated from Vitis
labrusca ‘Concord’ vineyards in Southwest Michigan after 1 month of growth on PDA.
A) Typical culture of E. lata (EL198), B) Typical culture of El .vitis (EVA9), C) Cross-
section of E. lata on PDA, note the appressed mycelium D) cross-section of El. vitis on
PDA, note the aerial mycelium and subconical pycnidia indicated by black arrow, B)
Isolate of El. vitis EV270 with slick, appressed mycelium, F) Isolate of El. vitis EV300
with darkened mycelium.

177

Table 5.2. Growth rate, conidial length, and ascospore length of isolates of Eutjpa lata

and Eutypella vitis collected from Vitis labrusca ‘Concord’ vineyards in Southwest

 

 

Michigan.
Isolate Species Growth Rate Conidia length x width Ascospore length x
(mm mt day)‘ (11111)” width (rlm)°‘l
E30 Eutypa lata 11.6 i 2.0 -° nd
E31 E. lata 10.3 :t 2.0 - nd
E38 E. lata 12.2 :t 4.9 - nd
ELA4 E. lata 15.2 :1: 3.7 - 7.6 x 1.7
ELA8 E. lata 11.4 i 2.6 - 7.9 x 1.8
EL] E. lata 13.13: 3.4 - nd
EL5 E. lata 5.1 :1: 0.9 - nd
EL19 E. lata 13.3 i 1.9 - nd
EL45 E. lata 13.6 i 2.4 - nd
EL55 E. lata 15.1 t 3.3 - nd
EL64 E. lata 13.6 i 1.4 - nd
EL67 E. lata 12.1 i 3.6 - nd
EL69 E. lata 11.8 i 3.9 16.3 x 0.51 nd
EL130 E. lata 13.8 :1: 2.5 - nd
ELI84 E. lata 17.1 :t 2.4 - nd
EL198 E. lata 17.2 i 4.5 15.5 x 0.54 nd
EL302 E. lata 14.7 d: 4.0 - nd
EL316 E. lata 13.6 i 4.2 - nd
EL318 E. lata 6.4 :1: 2.4 17.5 x 0.52 ml
E. lata“ 12.7 d: 4.1 16.4 x 0.52 7.8 x 1.8
EVA] Eutypella vitis 23.6 i 4.2 - 9.2 x 2.0
EVA2 El. vitis 19.2 i 2.2 20.8 x 0.53 9.7 x 1.9
EVA5 El. vitis 20.2 :1: 4.4 16.8 x 0.54 9.3 x 1.9
EVA6 El. vitis 22.7 :t 3.7 17.5 x 0.50 9.6 x 2.2
EVA9 El. vitis 22.3 :1: 2.3 20.1 x 0.53 9.1 x 2.1
EVA12 El. vitis 21.1:t 3.3 17.3 x 0.51 9.4 x1.9
EV66 El. vitis 26.5 :t 4.2 - nd
EV70 El. vitis 24.4 :1: 1.5 18.8 x 0.51 nd
EV71 E1. vitis 22.7 :1: 4.5 - nd
EV72 El. vitis 18.4 :1: 3.3 - nd
EV73 El. vitis 20. 4 :t 2. 9 - nd
EV74 El. vitis 13.2 i 2. 7 nd

 

a Growth rate measured as radial growth per day (mm) with standard deviation.

b Mean conidia length and width measured from 50 conidia produced in cultures grown

on PDA.

° Mean ascospore length and width measured from 50 ascospores released from wetted
perithecia.

Ascospores measured from stromata from which the isolate was collected.
° fConidia not produced after 30 days.
fnd=not determined.

3 Average of isolates of E. lata or El. vitis.

Table 5.2. (cont’dL

 

 

Isolate Species Growth Rate Conidia length x width Ascospore length x width
(mmrer day)‘ mel’ (11111)“

EV76 Eutypella vitis 15.9 :1: 3.5 - nd
EV77 E1. vitis 19.5 :1: 4.6 - nd
EV79 El. vitis 16.1 :t 3.8 - nd
EV81 E1. vitis 16.6 :1: 4.3 - nd
EV85 El. vitis 19.7 :h 6.4 - nd
EV89 El. vitis 21.2 i 1.7 - nd
EV229 El. vitis 14.8 :1: 3.7 16.0 x 0.55 nd
EV231 E1. vitis 21.0 i 5.5 - nd
EV232 E1. vitis 19.8 :t 3.5 18.0 x 0.45 nd
EV236 El. vitis 19.4 :t 2.7 16.0 x 0.50 nd
EV238 El. vitis 27.1 d: 3.5 20.0 x 0.53 nd
EV239 El. vitis 25.8 i 2.7 18.3 x 0.50 nd
EV257 El. vitis 23.1 :h 4.5 - nd
EV258 El. vitis 16.8 :t 3.4 16.3 x 0.58 nd
EV266 El. vitis 25.6 :t 5.6 16.0 x 0.50 nd
EV268 El. vitis 16.4 :1: 3.0 - nd
EV270 E1. vitis 24.1 i 2.3 - nd
EV279 El. vitis 16.8 :i: 4.1 18.8 x 0.54 nd
EV290 El. vitis 21.9 i 3.8 - nd
EV293 E1. vitis 20.8 i 1.2 18.0 x 0.52 nd
EV295 El. vitis 22.7 :1: 4.0 18.8 x 0.51 nd
EV300 El. vitis 14.1 :1: 3.4 - nd
EV325 E1. vitis 20.9 i 2.6 19.8 x 0.53 nd
EV329 E1. vitis 18.8 :t 4.0 18.5 x 0.57 nd
EV330 El. vitis 19.8 :t 5.3 21.5 x 0.58 nd
EV331 E1. vitis 21.3 :1: 2.5 - nd
EV334 E1. vitis 19.9 :i: 2.7 - nd
EV336 El. vitis 19.3 :t 5.6 - nd
EV337 E1. vitis 18.4 at 4.4 19.3 x 0.53 nd
EV339 El. vitis 19.4 d: 4.5 18.3 x 0.52 ml
EV344 El. vitis 12.8 :l: 3.7 - nd
El. vitis"g 19.7 :1: 5.1 18.3 x 0.53 9.4 x 2.0

 

179

Greenhouse pathogenicity screening

Greenhouse pathogenicity screens were conducted on potted cuttings of
‘Concord’ grapevine with 43 isolates of El. vitis and 19 isolates of E. lata. Isolates of El.
vitis produced a wide range of lesion lengths (5.11 to 12.50 mm) as did isolates of E. lata
(5.15 to 18.33 m) (Table 5.3) with xylem necrosis characteristic of Eutypa dieback
(Figure 5.4). The most virulent isolate was E30, an isolate of E. lata from California,
which produced significantly more vascular necrosis than all other isolates (18.33 mm in
trial 1, 14.59 mm in trial 2) (P S 0.05). Michigan isolates of E. lata and El. vitis fell
within the same range of lesion sizes (AN OVA table presented in Appendix D). While
most isolates did not produce significantly more vascular necrosis than the control, seven
isolates of El. vitis produced significant vascular necrosis (EV70, EV73, EV76, EV79,
EV85, EV89, and EV329). When viewed in cross section, isolates of El. vitis displayed
wedge-shaped areas of necrosis similar to isolates of E. lata (Figure 5.4), a characteristic
seen in cankers formed by E. lata in naturally infected vines. Recovery of the pathogen
from necrotic tissue was successful more than half of the time for all of the isolates, with

the exception of two (EV74 and EV344) that were not recovered.

180

Table 5.3. Lesion lengths on Vitis labrusca ‘Concord’ cuttings inoculated with isolates of
Eutypa lata and Eutypella vitis. Control treatment consisted of inoculation with a sterile
agar plug. Data was collected six months post-inoculation (lesion length measured in
mm). Isolates Eutypa lata and Eutypella vitis were isolated fiom vines from Vitis
labrusca ‘Concord’ vineyards in: Southwest Michigan ‘Concord’ Each treatment was
replicated four times for each trial.

 

 

 

Lesion length (mm)a
Isolate Fungal species Trial 1 Trial 2
Control 4.69 :t 0.33 4.11 :1: 0.65
E30“ Eutypa lata 18.33 i 5.78*b 14.59 1: 4.02*
E31‘1 E. lata 7.28 :1: 1.38 7.50 :1: 1.35”
E38“ E. lata 7.71 :1: 1.63 7.56 a: 156*
EL] E. lata 6.93 :1: 0.79 7.16 :1: 095*
EL5 E. lata 5.94 :1: 1.10 5.89 i 0.79
EL19 E. lata 6.38 a: 0.65 6.59 a: 0.90
EL45 E. lata 7.00 :1: 1.30 6.17 :1: 0.62
EL55 E. lata 7.13 a: 1.51 5.93 a 0.74
EL64° E. lata 6.60 :1: 3.18 5.45 1: 0.85
EL67b E. lata 7.22 a 2.36 6.07 :1: 1.20
EL69 E. lata 6.40 :t 1.30 5.15 :1: 0.74
EL130 E. lata 6.47 a: 2.11 6.41 d: 1.25
EL184 E. lata 5.77 i 0.87 6.00 a: 1.00
EL198 E. lata 6.27 :h 0.99 6.63 :t 0.66
EL302 E. lata 5.60 a 0.52 6.47 a: 1.13
EL316 E. lata 6.25 :1: 1.03 6.00 :1: 1.04
EL318 E. lata 6.08 i 1.62 6.40 d: 1.07
EV66 El. vitis 6.46 a 2.37 6.95 :1: 2.18
E. lata° 7.08 6.69
EV70 El. vitis 8.90 2: 3.15* 7.81 :1: 220*
EV71 El. vitis 6.33 :l: 1.90 9.44 :1: 3.67*
EV72 El. vitis 7.45 a: 3.37 6.44 :1: 2.07
EV73 El. vitis 9.50 :1: 265* 12.50 :1: 434*
EV74 El. vitis 6.20 i: 2.98 10.20 :1: 4.69*
EV76 El. vitis 8.50 a 3.73* 8.28 :h 3.71*
EV77 El. vitis 6.63 «_L 1.97 8.89 a 4.16*
EV79 El. vitis 9.08 a: 235* 12.35 i 5.17*
EV81 El. vitis 7.91 1: 4.85 10.00 :1: 420*
EV85 El. vitis 8.50 :1: 295* 9.13 fl: 350*
EV89 El. vitis 8.91 :t 1.75* 10.65 :1: 652*
EV229 El. vitis 7.41 :1: 2.71 6.67 :1: 0.97
EV231 El. vitis 7.50 :1: 1.68 6.10 :1: 0.38
EV232 El. vitis 6.44 :1: 0.90 6.75 :t 1.10

 

“ Average length and standard deviation of necrotic tissue.

b Significantly greater than the negative control, significance determined using
Tukey’s HSD test (P S 0.05).

c Average of isolates of E. lata or El. vitis.

181

Table 5.3 (cont’d)

 

 

 

Lesion length (mm)8
Isolate Fungal species Trial 1 Trial 2
EV236 El. vitis 6.44 i 2.56 7.07 :1: 1.66
EV238 El. vitis 6.27 d: 0.71 6.69 :1: 1.39
EV239 El. vitis 6.36 i 1.07 6.75 :1: 1.10
EV257 El. vitis 7.45 :1: 2.00 6.72 :1: 1.29
EV258 El. vitis 6.18 :1: 0.64 6.38 i 1.19
EV266 El. vitis 6.80 a; 0.79 6.71 :1: 1.83
EV268 El. vitis 6.31 :h 0.50 6.50 d: 1.04
EV270 El. vitis 6.50 :h 0.93 6.58 :1: 0.96
EV279 El. vitis 6.08 d: 0.60 6.41 :1: 0.79
EV290 El. vitis 7.54 i 3.70 6.41 :h 1.00
EV293 El. vitis 6.38 :h 1.21 6.69 :t 1.08
EV295 El. vitis 7.47 i 2.22 7.26 i 1.04*
EV300 El. vitis 5.27 d: 0.46 6.87 :1: 1.10
EV325 El. vitis 6.00 :t 1.49 6.83 :1: 1.22
EV329 El. vitis 8.85 :t 2.80* 8.73 :1: 1.71*
EV330 El. vitis 6.44 d: 2.05 7.06 i 1.56*
EV331 El. vitis 6.83 i 2.81 6.93 d: 1.60
EV334 El. vitis 6.92 :1: 2.26 7.83 i 1.95*
EV336 El. vitis 6.69 i 3.44 7.29 :1: 1.16*
EV337 El. vitis 7.56 i: 3.55 8.00 :1: 1.55“
EV339 El. vitis 7.76 :1: 1.71 10.80 i 3.64”“
EV344 El. vitis 7.50 :h 3.14 7.19 :L- 1.42*
EV346 El. vitis 7.56 i 2.66 7.41 :t 1.62*
EV348 El. vitis 6.38 i 1.48 6.64 :1: 0.82
El. vitisc 7.18 7.81

 

182

 

Figure 5.4. Longitudinal and cross sections of lesions caused by isolates of Eutypella vitis
and Eutypa lata in inoculated, l-year-old Vitis labrusca ‘Concord’ canes. Longitudinal
sections are to the left and cross sections are to the right. Note the wedge-shaped necrosis
in the cross sections indicated by black arrows. A.) El. vitis EV229, B) El. vitis EV232,
C) E. lata E30 D) control mock-inoculated with an agar plug.

183

Vineyard pathogenicity screening

In the first experiment, mature ‘Concord’ grapevines were inoculated with
mycelium of four isolates of El. vitis (EV70, EV89, EV257, EV339) and two isolates of
E. lata (EL45 and EL130). After one year, the extent of vascular necrosis for all isolates
was significantly greater than the control (Tukey’s HSD test, P S 0.05), and ranged from
6.8 mm (for isolate EV257) to 12.4 mm (isolate EL55) (Figure 5.5). The ANOVA table is
presented in Appendix D. The control vines had an average of 3 mm of vascular necrosis.
No foliar symptoms were seen in any of the treatments. Re-isolation fiom the necrotic
tissue resulted in recovery of all of the isolates in varying amounts; EV70 50%, EV257
50% , EV339 66% , EL55 66% , and EL130 100%.

In the second experiment, ascospores of both El. vitis and E. lata were used to
inoculate mature ‘Concord’ grapevines. The extent of vascular necrosis for all isolates,
with the exception of EVA9, was significantly greater than the control (Tukey’s HSD
test, P S 0.05), and ranged from 22.3 mm (EVA9) to 33.3 mm (ELA8) (Figure 5.6). The
control vines averaged 17.3 mm of vascular necrosis. Inoculation with ascospores of El.
vitis resulted in vascular necrosis that was similar in extent and appearance to the necrosis
seen in vines inoculated with ascospores of E. lata. Co-inoculation treatments showed
vascular necrosis that was not significantly different from either of the individual
inoculations of E. lata and El. vitis. No foliar symptoms were seen in any of the
treatments. Re-isolation from the vines resulted in recovery of all of the isolates at the
following rates; EVA] 33%, ELA4 33%, ELA8 33%, EVA5 50%, EVA2 66%, EVA9
66%, EVA6 66%, and EVA12 100%. For the EVA1/ELA4 co-inoculation, both isolates

were recovered from one vine, EVA] was recovered by itself from 2 of 6 vines, and

184

ELA4 by itself from 1 of 6 vines. For the EVA5/ELA8 co-inocualtion, both were

recovered from two vines, and ELA8 was recovered from one vine.

185

 

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Control EV257 EV89 EV70 EV339 EL130 EL55

Isolate

Figure 5.5. Mean length of lesions caused by isolates of Eutypa lata and Eutypella vitis
on mature grapevines. Eight-year-old Vitis labrusca ‘Concord’ grapevines were
inoculated with mycelial plugs of agar, and measurements were taken one year post-
inoculation. Vines were maintained at the Clarksville Horticultural Experiment Station at
Clarksville, MI. The control treatment was mock-inoculation with a sterile plug of agar.
Columns followed by the same letter are not significantly different according to Tukey’s
HSD test (P S 0.05).

186

 

  

    

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Isolate

Figure 5.6. Mean length of lesions caused by isolates of Eutypa lata and Eutypella vitis

on mature grapevines. Eight-year-old Vitis labrusca ‘Concord’ grapevines were

inoculated with ascospores, and measurements were taken one year post-inoculation.

Vines were maintained at the Clarksville Horticultural Experiment Station at Clarksville,

MI. The control treatment was mock-inoculation with a sterile plug of agar. Columns

followed by the same letter are not significantly different according to Tukey’s HSD test

(P s 0.05).

187

C ultivar resistance screening

After six months, no foliar symptoms were visible in any of the cultivars. Vines
were destructively sampled, and the amount of xylem necrosis was measured (Table 5.4).
Results from both trials are presented separately, as there were significant differences
between trials according to AN OVA (Appendix E). In both trials, E30 was more virulent
than any other isolate of E. lata or El. vitis. The other isolates of E. lata and El. vitis
caused very similar amounts of vascular necrosis. Analysis of variance showed that there
was a significant difference between cultivars in both trials (P S 0.05). The cultivars with
the least vascular necrosis were Niagara followed by Seyval. The rest of the cultivars

showed similar responses to isolates of both E. lata and El. vitis.

188

Table 5.4. Lesion length (mm) on potted grape canes six months after inoculation with
isolates of Eutypella vitis and Eutypa lata. (Lesion length measured in mm). The control
treatment consisted of inoculation with a sterile agar plug.

 

 

 

 

 

Cultivar“
Trial 1
Isolate N C11 8 V R Ch Cy Avg‘
Control 4.1 usb 4.7 a° 4.6 a 4.5 a 45 a 4.7 a 4.4 a 4.5
E30 6.0 ns 21.8 c 7.3 c 14.5 b 14.5 d 14.6 b 15.7 c 13.5
EL45 4.4 ns 6.4 ab 5.3 ab 9.0 ab 77 be 5.5 a 5.2 a 6.2
EL55 5.0 ns 5.4 a 6.0 b 6.3 ab 3,7 c 6.5 a 5.9 ab 6.3
EV257 5.6 ns 7.6 ab 5.2 ab 7.8 ab 6.0 ab 5.4 a 7.5 b 6.4
EV339 4.3 ns 6.3 ab 6.4 be 8.0 ab 75 be 6.8 a 6.1 ab 6.5
EV70 5.3 ns 8.7 b 6.3 bc 5.8 ab 73 be 4.5 a 5.5 a 6.3
EV73 5.0 ns 7.5 a 7.6 c 4.0 a 7.1 ab 5.3 a 5.8 a 6.0
Avg“ 5.0 8.6 6.1 7.5 8.0 6.7 7.0
Trial 2

Isolate N C11 8 V R Ch Cy Avgc
Control 5.4 a 5.1 a 5.7 a 5.1 a 4.8 a 5.3 a 5.1 a 52
E30 7.4 b 17.0 c 9.3 c 13.4 c 16.9 c 12.5 c 12.6 c 12.7
EL45 5.3 a 7.0 ab 6.7 ab 7.9 b 6.9 b 6.8 ab 6.4 ab 6.7
EL55 5.3 a 7.8 b 7.0 ab 8.4 b 6.3 b 8.3 b 6.1 ab 7.0
EV257 6.1 ab 7.1 ab 7.] ab 7.8 b 6.4 b 8.2 b 6.2 ab 7.0
EV339 5.8 ab 7.2 b 7.8 b 7.9 b 6.2 b 7.6 b 6.8 b 7.0
EV70 5.9 ab 8.6 b 7.2 ab 8.3 b 6.3 b 6.6 ab 7.2 b 7.2
EV73 6.0 ab 6.7 ab 7.3 b 7.9 b 6.3 b 7.5 b 7.0 b 7.0
Avg" 5.9 8.3 7.3 8.3 7.5 7.9 7.2

 

3‘ Cultivars; N=Niagara, Cn=Concord, S=8eyval, V=Vignoles, R=White Riesling,
Cl=Chardonel, Cy=Chardonnay.

b ns = not significant.

° Column means followed by the same letter are not significantly different according to
Tukey’s HSD test (P5005).

d Mean lesion length for all cultivars for each isolate.

° Mean lesion length for all isolates for each cultivar.

189

Analysis of secondary metabolites in growth media
E30 was the only isolate that produced any secondary metabolites in the growth

media, of which only eulatachromene was detected.

Vegetative compatibility

Isolates of El. vitis and E. lata were plated on PDA in groups to determine
vegetative compatibility. In every interaction, a barrage zone developed, indicating that
all isolates are genetically distinct. No barrage zones were observed when isolates were

paired with themselves.

Phylogenetic analyses

Polymerase chain reaction amplifications using primers ITSlF and ITS4 gave
products of approximately 0.6 kb while primers Bt2a and [3th amplified a product of 0.4
kb. Sequence homology for the ITSl-5.88-IT82 region was 94% among isolates of El.
vitis and 90% for isolates of E. lata. Sequence homology for the partial sequence of the B-
tubulin gene was 78% for El. vitis isolates and and 85% for E. lata isolates. When
conducting phylogenetic analysis with either the ITS or B-tubulin sequences, El. vitis fit
into a well well-defined clade, supported with bootstrap values of 99% (Figures 5.7 and
5.8). Eutypa lata fell into a less defined clade, with a bootstrap value of 59% (ITS) and
99% (B-tubulin). For the ITSl-5.88-IT82 region, there were a total of 474 positions in the
final dataset, 75 of which were parsimony informative. For the B-tubulin gene, there were
a total of 360 positions in the final dataset, 98 of which were parsimony informative.

Sequences used in this study can be found in Appendix F.

190

EV66 El. vitis
EV76 El. Vitis Lawton M

EV79 El. vitis Lawton Ml
EV238 El. .vitis (Schoolcratt Ml)
EV70 El. vrtrs (Lawton MI}

gm...

EV81 El. vitis Lawton Ml
EVA2 El. vitis (Lawton M)
EV279 El. vgtrs (Schoolcralt MI)
EV348 El. vrtls Baroda Ml)
EVA1 El. u‘tis Lawton MI
EVA6 El. vitis Lawton Ml
EV231 El. vitis Schoolcraft MI
EV232 El. Vitis Schoolcratt MI
EV257 El. vitis Schoolcratt Ml
EVA9 El. vitis (Lawton Ml
EV331 El. u‘tis Baroda I
EV337 El. ' ' Baroda Ml

 

EV330 El.
EV329 El.
EV336 El.
EV229 El. ' '
EV339 El.
EV325 El.

EV334 El

Baroda Ml
Baroda M
Baroda Ml
Baroda Ml
Baroda Ml
Baroda Ml
Baroda Ml

EVA12 El.
EV268 El.

u‘tis

«us “am" M

choolc Ml;
EV270 El. vitis Schoolcratt MI
EV346 El. utis Baroda Ml)
EVA5 El. vitis (Lawton Ml)
EV293 El. vrtrs Schoolcraft Ml
EV344 El. vitis Schoolcraft Ml
EV295 El. u‘tis Schoolcraft Ml
EV290 El. vitis Schoolcratt Ml
EV300 El. vitis Schoolcratt Ml
EV85 El. vitis Lawton Ml
EV74 El. Lawton MI
EV72 El. Lawton Ml

EV71 El. Lawton Ml
EV73 El. Lawton Ml
EV77 El. vitis Lawton Ml
EV89 El. u‘tis Lawton MI
EV258 El. \ttis (Schoolcratt M1)
E31 E. lata gCalrfomlag

vitis
ujtis
vitis
vrtls

E38 E. lata California
E30 E. lata California

 

EL316 E. lata (Baroda Ml)
EL45 E. lata Lawton Ml
EL19 E. lata Lawton Ml
EL55 E. lata Lawton MI
EL198 E. lata (Lawton I)
EL1 E. lata Lawton M
EL5 E. lata Lawton Ml
EL130 E. lata Lawton I
EL184 E. lata Lawton Ml
EL302 E. lata Baroda MI
EL69 E. lata (Lawton MI)

 

73

Figure 5.7. Bootstrap consensus tree constructed using the UPGMA method from the
alignment of the ITSl-5.8S-IT82 region of 42 isolates of Eutypella vitis and 14 isolates
of Eutjma lata fiom Southwest Michigan and California. Branches corresponding to
partitions reproduced in less than 60% bootstrap replicates are collapsed. The percentage
of replicate trees in which the associated isolates clustered together in the bootstrap test
(1000 replicates) are shown next to the branches.

191

EV330 El. vitis
EV334 El. vitis
EV331 El. vitis
EV336 El. vitis
EV329 El. vitis
EV337 El. vitis
EV73 El. \itis
EV77 El. vitis
EV85 El. vitis
EV270 El. vitis
EV348 El. vitis
EV232 El. vitis
EV290 El. vitis
EV344 El. vitis
EV300 El. vitis
EV293 El. vitis
EV295 El. vitis
EV258 El. vitis
EV279 El. vitis
EV346 El. vitis
EV238 El. vitis
EV268 El.

EVA1 El. vrtrs
EVA2 El. vitis
EV231 El. vitis
EV257 El. vitis
EVA5 El. vitis
EV325 El. vitis

EV72 El. vitis
EV89 El. vitis
EV70 El. vitis
EV79 El. vitis
EV229 El. vitis
EV339 El. vitis

E38 E. lata
E30 E. lata

 

EL198 E. lata

 

EVA12 El. vitis

 

Baroda Ml
Baroda Ml
Baroda Ml
Baroda Ml
Baroda Ml
Baroda Ml

Lawton Ml
Lawton Ml
Lawton Ml

Schoolcratt MI)
Baarod MI
Schoolc M
Schoolcrafl M
Schoolcratt M
Schoolcralt M
Schoolcratt M
Schoolcralt M
Schoolcraft MI
Schoolcraft M
Baroda Ml
Schoolc M

viits Schoolcratt M
EVA6 El. vitisg

Lawton MI
Lawton Ml
Lawton Ml
(Schoolcraft Ml
Schoolcratt M

(Lawton MI

(Baroda I)
(Lawton M)

Lawton M
Lawton Ml
Lawton Ml
Lawton M

132%: MB

EV66 El. vitis Lawton M
EV74 El. vitis Lawton M
EV76 El. vitis Lawton M
EV71 El. vitis Lawton M
EV81 El. vitis Lawton M
EVA9 El. vitis (Lawton M)
E31 E. lata California
Califomia
California
EL302 E. lata (Baroda M
EL 316 E. lata (Baroda M)
EL5 E. lata (Lawton Ml')
EL45 E. lata (Lawton
EL1 E. lata (Lawton Ml:
EL19 E. lata (Lawton
EL184 E. lata (Lawtonl
EL55 E. lata (Lawton MI)”
EL69 El. vitis (Lawton MI
EL130 E. lataé

Lawton M l
Lawton Ml

Figure 5.8. Bootstrap consensus tree constructed using the UPGMA method from the
alignment of the B-tubulin gene of 42 isolates of Eutypella vitis and 14 isolates of Eutypa
lata from Southwest Michigan and California. Branches corresponding to partitions
reproduced in less than 60% bootstrap replicates are collapsed. The percentage of
replicate trees in which the associated isolates clustered together in the bootstrap test

(1000 replicates) are shown next to the branches.

192

Discussion

Historically, mycologists have discriminated between genera and species within the
Diatrypaceae using traits such as stromatal features (6), morphology and disposition of
perithecia (17), type of anamorph (32), and asci and ascospore morphology (29). The
primary way to separate Eutypa and Eutypella is still through stromatal configuration;
Eutypa having eutypoid perithecia with ostioles that individually break through the
surface of the stromata while Eutypella have valsoid perithecia with ostiolar necks that
break through stromata in convergent groups (1, 11). Even with these defining
characteristics, differentiating between Eutypa and Eutypella has been difficult. For this
reason, members of both species have often been grouped into the same genus, Eutypa
(26, 28).

Perithecia of El. vitis were erumpent through the grapevine bark, with three to seven
perithecia in a valsoid arrangement. Ostioles were sulcate, with three to four lobes, and
were much longer than the roundish ostiolar necks of E. lata. To the naked eye, the
differences between fruiting bodies of El. vitis and E. lata are not easily seen, especially
when the stromata age. Over time, the perithecia of El. vitis become numerous and, as
they weather, appear shorter and less erumpent from the grapevine tissue, making them
more similar in appearance to E. lata. Ascospores of El. vitis were allantoid, slightly
curved, with small oil bodies near the ends, and are indistinguishable fiom E. lata by
appearance. Ascospores of El. vitis tend to be larger than those of E. lata, but the size
ranges overlap, making it impossible to distinguish the two species using this
characteristic along. Misidentification as E. lata would explain why no previous effort

has been made to determine pathogenicity of El. vitis.

193

Anamorph characteristics have been, by and large, problematic for distinguishing
genera and species within Diatrypaceae due to the nearly indistinguishable conidia (11,
22). Three forrn-genera are typically used to describe anamorphs of diatrypaceous fungi:
Cytosporina for fungi with enclosed pycnidia and filiform conidia; Libertella for those
with unenclosed conidiomata and filiform conidia; and Naemospora for fungi with
unenclosed conidiomata and allantoid conidia (11, 22, 28). Many authors choose not to
assign names to anamorphs in culture due to the ability of many anamorphs to produce
both pycnidial and acervular conidiomata (28), as is the case with E. lata (14). Eutypella
vitis isolates in this study produced subconical pycnidia in culture. Given the anamorphic
characteristics of El. vitis, the anamorph should probably be given the name Cytosporina
vitis.

Results of this study show that El. vitis is capable of colonizing and killing woody
tissue of grapevine, but its true ability as a pathogen is unknown. Long-terrn studies of
several years need to be conducted to determine if El. vitis infections can produce foliar
symptoms similar in appearance to E. lata. Pathogenicity screens of El. vitis isolates on
‘Concord’ cuttings and mature vines indicate a range in virulence, a phenomenon also
seen with isolates of E. lata (4, 13, 18, 19, 20, 21). Isolates of El. vitis were similar in
virulence to Michigan isolates of E. lata, but none were as virulent as E30, an E. lata
isolate from California. Interestingly, E30 was the only isolate to produce detectable
toxin (eulatachromene) in ‘Concord’ grape cane broth, a property that has been
associated with virulence (13). The results from the cultivar screen are consistent with

previous studies (4, 13, 30). The presence of El. vitis in Michigan vineyards will probably

194

not require a revision of the recommendations for cultivar selection for resistance to
Eutypa dieback.

Due to the similarity of these two fungi, it is likely that environmental conditions that
favor E. lata will also favor El. vitis. Epidemiological studies using spore trapping will
also need to be reconsidered, as spores from El. vitis and E. lata are indistinguishable and
fruiting bodies of both fungi can be found on the same vines. Management strategies
recommended for controlling Eutypa dieback will probably be the same for El. vitis, but
any future studies involving fungicide efficacy will likely need to include both E. lata
and El. vitis for evaluation.

The question of whether isolates of El. vitis can produce phytotoxic secondary
metabolites still remains unanswered. In this study, the E. lata isolate E30 was the only
one to produce a phytotoxic secondary metabolite, but previous studies using this isolate
along with E3] and E3 8, showed production of a broader range of phytotoxic secondary
metabolites in grape cane broth made from ‘Cabernet Sauvignon’ (23). It was previously
shown that isolates of E. lata grown in grape cane broth made from ‘Cabernet
Sauvignon’ produce a greater concentration and a wider number of phytotoxic secondary
metabolites than when grown in artifical media (12). It was proposed that a natural
substrate provides optimal conditions of E. lata and therefore results in a more
representative profile of the metabolites produced in the natural disease state (12). This is
why grape cane broth made from ‘Concord’, the host cultivar of the El. vitis isolates used
in this study, was chosen as the growth substrate used to characterize the secondary
metabolite production of El. vitis. It is unclear as to why grape cane broth made from

‘Concord’ was not as effective as one made from ‘Cabernet Sauvignon’ in eliciting the

195

production of metabolites. To truly determine if El. vitis can produce the same suite of
phytotoxic secondary metabolites, a wider range of media types must be tested.

Sequencing of the ITS l-5.8S-IT82 region and partial region of the B-tubulin gene,
phylogenetic analysis, and information from vegetative compatibility grouping, indicates
that the Michigan population of El. vitis is not clonal, and is likely reproducing sexually.
This is not surprising as sexual fruiting bodies are more commonly found on vines than
pycnidia. There was no significant grouping by location in the phylogenetic study.

The results from the phylogenetic study for E. lata indicate that isolates from Michigan
are genetically distinct from isolates in California. This is not surprising, considering the
geographical barriers and distance that separate California from Michigan. It would be
interesting to see if E. lata isolates from other areas in the Eastern United States are
genetically distinct from those from the west coast of the United States and other grape-
growing regions of the world.

The role of El. vitis in the expression of Eutypa dieback symptoms is unclear. To
date, no adequate survey of the co-colonizers of Eutypa dieback cankers has been
conducted in Michigan. The expression of disease symptoms in ‘Concord’ vines in
Michigan is variable between vines and years. It is possible that shoot symptoms are
affected by the presence of other fungi like El. vitis in cankers. Further research needs to

be done before we can fully understand the complexity of Eutypa dieback in the region.

196

10.

11.

12.

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199

CHAPTER 6: TRANSFORMATION OF EUTYPA LATA AND EUTYPELLA VITIS
BY RESTRICTION ENZYME-MEDIATED INTEGRATION.
Abstract

The role of phytotoxins in Eutypa dieback disease development is unknown. To
enable the study of the role of phytotoxins as either pathogenicity or virulence factors,
putative toxin-deficient mutants were generated. Eutypa lata isolate E3 0, a highly
virulent, phytotoxin-producing isolate, was chosen for transformation. A restriction
enzyme-mediated integration (REMI) protocol was developed using gGF P, a plasmid
which expresses green fluorescent protein (GFP) in the fungus. Transformation
efficiencies of ~15 transformants per 11g of plasmid DNA were obtained. Transformation
was also successful with Eutypella vitis. Using fluorescence confocal microscopy,
visualization of GFP-expressing transformants of E. lata and El. vitis was possible in
inoculated Concord grapevine wood, confirming the role of El. vitis as a pathogen of
grapevine. This is the first successful genetic transformation of E. lata and El. vitis.
Through REMI transformation, 2184 transformants were generated and screened for
toxin production. Twenty two possible toxin-deficient candidates were identified.
Southern analysis indicated single, random insertion events for all 22 candidates. Putative

toxin-deficient transformants await further characterization.

200

Introduction

Eutypa dieback, a perennial canker disease of grapevine, is caused by the
ascomycetous fungus Eutypa lata (Pers.:Fr.) Tul. & C. Tul. (syn. E. armeniacae Hansf. &
Carter, anamorph Libertella blepharis A. L. Smith [syn. Cytosporina sp.]). This disease
affects grapevines around the world, including the United States, Europe, Australia and
South Afiica (23). The fungus is also known to infect other important woody plants, such
as almond, apricot, cherry, olive, peach, and walnut (4, 21). Eutypa dieback is
progressive over many years, is difficult to control, and may lead to severe economic
losses, primarily due to decreased yields and longevity of vineyards. Yield losses of 30-
83% have been reported in California, where the disease is the major constraint to grape
production (20). The cost to wine production alone in California has been estimated to be
in excess of $260 million per year (30). In Michigan, many older Concord vineyards are
showing Eutypa dieback (36), with as many as 50% of the vines infected.

The involvement of a phytotoxin involved in the symptoms of Eutypa dieback of
grapevine was proposed in 1981, because symptoms were detected at various distances
away from cankers (18). A toxin was discovered in filtrates of E. lata cultures (17), and
was later named eutypine, which has the chemical structure of 4-hydroxy-3 -(3-
methylbut-3-en-1-ynyl) benzylaldehyde (3 5). Eutypine is believed to be conducted
through the sap to the shoots where symptoms develop. The mitochondrion is the likely
biological target of the toxin, as it has been shown to modify the rate of respiration and
the energy balance of the grapevine cells (6, 7, 9). The activity of eutypine is
concentration-dependent (7), which may explain, in part, why symptoms observed in a

single vine over the years may vary. Detoxification of eutypine by a eutypine-reductase

201

enzyme (V r-ERE) from Vigna radiata (L.) has been studied, and overexpression of the
protein in grape has conferred resistance to the toxin (1 1, 13). It is as yet unclear if the
gene can confer resistance to the fungus.

While the emphasis of Eutypa phytotoxin research has been on eutypine,
Tabacchi (33) isolated a family of phenylacetylenic compounds from E. lata culture
media. In a study by Molyneux et al. (19), eutypine production by E. lata cultures was
found to be variable. Other secondary metabolites, including eulatachromene and
eulatinol, were detected in culture filtrates. Smith et al. (3]) concluded that
eulatachromene was more toxic to grape leaves than eutypine. It is still unclear if these
other secondary metabolites are involved in pathogenicity. If the fungus is utilizing a
number of toxins, it is likely that Vr-ERE will be inadequate in detoxifying all toxins.

Over the last couple of decades, the ability to transform filamentous fungi with
foreign DNA has allowed for the discovery of genes involved in pathogenicity, and has
allowed for the study of genes in organisms that are poorly characterized at the molecular
level. One of the most popular techniques for the integration of DNA is restriction
enzyme-mediated integration (REMI). This technique involves adding a restriction
enzyme during transformation, and typically results in an increase in transformation rate
and single-copy integration events (3). A number of pathogenicity genes have been
discovered in fungi using REMI (1-3, 8, 14, 24, 27, 28, 32, 39). REMI has also been
successfully used to generate toxin-deficient transformants, allowing for the recovery of
genes involved in toxin biosynthesis (1, 5, 34, 39).

To unequivocally determine the role of toxins in disease development, either as

pathogenicity or virulence factors, toxin-deficient mutants have to be made. In this study,

202

a REMI transformation protocol was developed and optimized for the efficient
transformation of E. lata with the intent to generate toxin-deficient transformants. A
secondary goal was to transform E. lata and Eutypella vitis (Schwein.:F r.) Ellis & Everh.
(syn. Eutypella. aequilinearis), another diatrypaceous fungus that infects grapevine, with
a gene for the green fluorescent protein. This will serve as a vital stain in studying the

growth of these fungi in planta.

203

Materials and Methods
Isolates and plasmid

Eutypa lata isolates E3 0, a highly virulent isolate known to produce
eulatachromene and eutypinol (25), EL130, and El. vitis isolate EV330 were used in this
study. More information on these isolates can be found in Chapter 4. Isolates were stored
in sterile water and cultured on potato dextrose agar (PDA) prior to use. The plasmid
gGFP (16) [Figure 6.1] was obtained from the Fungal Genetics Stock Center (University
of Missouri, Kansas City). The plasmid gGFP contains an enhanced green fluorescent
protein (SGF P) gene with a chromophore mutation for enhanced fluorescence (S65T), a
human codon usage, and a Kozak sequence for mammalian translational initiation (3 7).
The SGFP gene is under the control of the Aspergillus nidulans glyceraldehyde-3-
phosphate dehydrogenase promoter. The plasmid also contains the Escherichia coli
ampicillin resistance gene (AMP) for selection of bacteria, and hph, the hygromycin
phosphotransferase gene under the Aspergillus nidulans glyceraldehyde-3-phosphate
dehydrogenase promoter that confers resistance to hygromycin B for selection of
transformed fungi. The plasmid gGF P was propagated in E. coli strain DH501 (Promega
Corporation, Madison, WI). Plasmid DNA was prepared using the QIAGEN Plasmid

Maxi Kit (QIAGEN Co., Mississauga, ON).

204

 

m gGFP

9240 bp — _ Clll23BO

      

xmr 5272
mum 5248

Figure 6.] Circular map of transformation vector gGFP. ngd: Aspergillus nidulans
glyceraldehyde-3-phosphate dehydrogenase promoter, hph: E. coli hygromycin
phosphotransferase gene for resistance to hygromycin B, TtrpC: Aspergillus nidulans
tryptophan C transcriptional termination signals, SGFP: synthetic green fluorescent
protein gene, Amp: ampicillin resistance gene.

205

Isolate sensitivity to hygromycin-B

To determine the concentration of hygromycin-B needed to inhibit growth of E.
lata, isolate E30 was tested across a range of concentrations of hygromycin-B. Isolate
E30 was grown on PDA for 5 days prior to subcultured onto PDA amended with
hygromycin B (Bioworld, Dublin, OH) at the following concentrations; 0, 5, 10, 50, 100,
and 200 mg/L. Three plates were cultured for each concentration. After 7 days, plates

were checked for growth. The experiment was repeated for isolates EL130 and EV339.

Cultivation prior to transformation

To prepare mycelium for transformation, isolates were grown on PDA for 5 days.
Five, 6mm, mycelial plugs were taken fiom the leading edge of the cultures for
inoculation into 50 m1 potato dextrose broth (PDB). Cultures were grown for 5 days at
22°C on an orbital shaker at 90 rpm. Using a wide-tip pipette, 4 ml of the mycelium was
drawn from the culture and added to 50 ml PDB. Three flasks were prepared for each
isolate. After three days of, mycelium was collected by filtering with Whatman no. 1
filter paper, followed by a wash with 50 ml sterile distilled, deionized water. Mycelium

was collected, weighed, and 2.5 to 3 grams of each isolate was used for transformation.

Preparation of protoplasts

The protoplast preparation and transformation protocol was adapted from Walz
and Ktich (3 8). Protoplasts were prepared fiom mycelium in buffer containing 500 mg
Driselase (Sigma-Aldrich Corp., St. Louis, MO), 2 mg chitinase (Sigma-Adrich Corp),

and 400 mg lysing enzyme (Sigma-Aldrich Corp.) dissolved in 40 ml of 1.8 M potassium

206

chloride. The protoplasting buffer and mycelium were placed in a ISO-ml Erlenmeyer
flask in a shaker-heater at 30°C at 120 rpm. The digestion mix was checked periodically
for development of protoplasts; 3 hrs was typically needed for sufficient digestion. The
digestion mix was then filtered through a 30-um nylon net filter (Millipore, Billerica,
MA) into a 50-ml centrifuge tube and centrifuged at 800 g at 4°C in a spinning bucket
rotor for 10 minutes with the brake off. The supemantant was removed and the pellet
resuspended in 10 ml cold STC buffer (1.2 M sorbitol, 10 mM Tris-HCl (pH 8.0) 50 mM
CaClz) and centrifuged at 800 g in a spinning bucket rotor for 10 minutes with the brake
off. This last step was repeated once and the pellet was resuspended in 1 ml cold STC
buffer. Protoplast concentration was determined with an improved Neubauer
hemacytometer (American Optical Co., New York, NY) and adjusted to the appropriate

concentration.

Transformation conditions

Four hundred microliters of protoplasts, plasmid DNA, restriction enzyme
(HindIII), and 100 uL of 30% polyethylene glycol (4000) [PEG] dissolved in STC buffer
were combined in a 15-ml conical tube and incubated for 20 min at room temperature.
Four millileters of 30% PEG was added to the transformation mix in a dropwise fashion
while gently shaking the tube, followed by incubation for 5 min at room temperature.
Eight ml of STC buffer was added to the transformation mix which was then added to
250 ml warm regeneration media (1 g yeast extract, 1 g casein enzyme hydrosylate, and
7.5 g phytagar [Gibco BRL, Grand Island, NY]) kept at 47°C. The regeneration media

and protoplasts were poured into Petri plates, incubated at room temperature for 24 hr,

207

followed by addition of a thin top-layer of regeneration media containing 100 mg/L
hygromycin B. Plates were monitored for 7-10 days for the emergence of transfonnants.
The concentrations of protoplasts, DNA, and restriction enzyme were optimized by
conducting experiments twice for all variables. Once transformation conditions were

optimized for E30, they were tested on isolates EL130 and EV339.

Selection and storage of hygromycin B resistant transformants

As transformants began to emerge through the top agar, hyphal tips were taken
from each colony and plated onto plates containing PDA amended with 100 mg/L
hygromycin B. After seven days of growth, transformants were included in the toxicity

screen described later and stored in sterile water at 4°C.

Growth on PDA to check stability

To check the stability of transformants of E30, 30 transformants (10 transformed
with undigested gGFP, 10 transformed with HindIII-digested gGF P, and 10 transformed
with HindIII-digested gGFP with HindIII in the transformation mix), were grown for 4
weeks on PDA without hygromycin B. Transformants were transferred once a week and
then, at the end of the 4 weeks, were transferred onto PDA with 100 mg/L hygromycin B.

The growth of hyphae on hygromycin B-amended media indicated mitotic stability.

PCR of select transformants

Twelve transformants of E30 were chosen at random for PCR analysis using primers

specific for the hph gene. For DNA extraction, transformants were grown for 7 days on

208

hygromycin B-amended PDA. Approximately 100 mg of mycelium was scraped off and
placed into 1.5-ml microcentrifuge tubes and DNA was extracted according to the
protocol of Lee et a1. (12). Details of the DNA extraction can be found in Chapter 4 of
this dissertation.

DNA extracted from mycelium was diluted 103 times in sterile water in preparation
for PCR. A 500-bp sequence of the hph gene was amplified with the primers Hptl (5’-
GGC GAA GAA TCT CGT GCT TTC A-3’) and Hpt2 (5’-CAG GAC ATT GTT GGA
GCC GAA A-3’). PCR reactions were carried out in 25 111 total volume consisting of 12.5
ul DNA dilution (template) and 12.5 ul PCR reaction mixture. The reaction mixture
contained PCR buffer (20 mM ammonium sulfate; 2.0 mM MgC12; 50 mM Tris-HCI, pH
9.0; Epicentre Technologies, Madison, WI), 0.2 mM each of dATP, dTTP, dGTP and
dCTP, 0.5 11M each of primer, and 0.5 unit of Taq DNA polymerase. The reactions were
carried out in a DNA thermal cycler (Model 9600, Perkin-Elmer Cetus, Norwalk, CT).
The amplification protocol included an initial denaturation at 95°C for 2 minutes
followed by 30 cycles at 94°C for 1 min, 58°C for 30 3 min for the ITS primers (58 °C for
the B-tubulin primers), and 72°C for l min. The reaction was completed by a 5-min
extension at 72°C. PCR products were separated on 1.5% agarose (Gibco BRL, Grand
Island, NY) in 1% TAE buffer (100 mM Tris, 12.5 mM sodium acetate and 1 mM EDTA,
pH: 8.0) by gel electrophoresis. A l-kb plus DNA ladder (GibcoBRL) was included in
each gel as a DNA size standard. The gels were stained with ethidium bromide,
visualized by UV fluorescence and photographed using an AlphaImager imaging system

(Alpha Innotech Corporation, San Leandro, CA).

209

Southern analysis of toxin-deficient candidates

Genomic DNA was isolated from mycelium as described previously and DNA
concentration was determined spectrophotometrically. Fifty ug of each sample was
digested with Xbal (Promega, Madison, WI) and loaded onto a 0.7%-agarose gel. The
EL30 wild-type (50 ug) and gGF P plasmid (10 ng) digested with Xbal were loaded onto
the gel as controls. Standard protocols were followed for gel electrophoresis and
Southern blotting (26).

A DIG-labeled (digoxigenin-labeled) 500 bp fragment of the hph gene was
generated through PCR using the reaction conditions described previously, primers Hptl
and Hpt2, gGFP plasmid as a template, and the DIG-High Prime DNA Labeling and
Detection Kit (Roche, Basel, Switzerland). Hybridization and washing (0.5X SSC, 0.1%
SDS) of the membrane was performed at 65°C according to the DIG System User’s
Guide (Roche) and the chemiluminescence detected using X-ray film (Eastman Kodak

Co., Rochestor, NY).

Inoculation of plants

One transformant of El. vitis, EV339-12H, and one transformant of E. lata, E30-
13H, were used to inoculate healthy, dormant, l-year-old V. labrusca ‘Concord’ canes
with two intact nodes. Isolates were cultured for 5 days on PDA amended with
hygromycin B (100 mg/L) prior to inoculation. Four canes were inoculated with each
isolate. Prior to inoculation, a cordless drill was used to make a shallow, 3-mm diameter
hole approximately 2 cm below the upper node of each cane. A plug of agar with

mycelium was inserted into the hole and sealed with parafilm. The bottom node was cut,

210

wetted, and dipped in Hormodin 2 (E. C. Geiger Inc., Harleysville, PA ), and the canes
were planted in a mixture of 2 parts sand and 1 part Baccto Potting Soil (Michigan Peat
Co., Houston, TX) in 15.2 cm pots. The negative control consisted of canes inoculated
with a sterile plug of PDA amended with hygromycin B (100 mg/L). Plants were grown
in a grth chamber at 22°C with 16-hr days/ 8 hr nights. After six months, cane tissue

was exarrrined using confocal microscopy.

Confocal microscopy

Fungal samples were prepared for microscopy by lifting a small amount of
hyphae with a sterile needle fiom the surface of a 5-day culture plated on hygromycin B-
amended PDA. Thin slices of grapevine wood inoculated with transformants of E. lata
and El. vitis were cut longitudinally with a sterile razor scalpel. The thin tissue sections
were placed on a glass slide in a water droplet, covered with a cover slip, and observed
without further manipulation.

The confocal images were collected using a Zeiss LSM 5 Pascal Laser Scanning
Microscope (Carl Zeiss MicroImaging, Inc., Thomwood, NY). The images were
collected using either a 20X Plan Neofluar dry objective (NA 0.5), a 40X Plan Neofluar
dry objective (NA 0.75), or a 40x Plan Neofluar oil objective (NA 1.3). The fluorescence
was excited with a 488 nm Argon ion laser line, and emission was collected using the 505

nm long pass emission filter.

211

Toxicity screen

Culture medium from transformants of E30 was screened for phytotoxicity using
tobacco (Nicotiana tabacum L. cv Bright Yellow 2 [BY2]) cells. Tobacco cells were
provided by Linda Danhof and MSU-DOE Plant Research Laboratory. Transformants
were grown for 30 days in the dark in 6-well culture dishes in 6 ml malt extract/yeast
extract broth (MYB) amended with 1% sucrose. Tobacco BY2 cells were grown and
maintained according to Nagata et a1 (22). Mycelial mats that formed in the culture dishes
were pushed aside with the tip of a pipet and 400 11L of media was drawn out and added
to an equal volume of 5-day-old BY2 cells in 24-well culture dishes. The E30 wild-type
and uninoculated media containing 100 mg/ml hygromycin B were included as controls.
Culture media and BY2 cells were incubated for 3 days in the dark at room temperature
on an orbital shaker at 90 rpm. Plant cell viability was assessed using trypan blue (Sigma-
Aldrich Corp.). Eighty microlitter of 0.4% Trypan blue was added to each sample and
incubated for 15 min. Cell viability was observed using a microscope and could be seen
as a lack of blue stain in the cells. The cell viability assay works on the principle of
membrane dye exclusion by the plasma membrane, by which healthy cell retain the
ability to exclude uptake of the dye or stain while dead or dying cells with damaged

membranes will take up the dye and stain blue.

212

Results
Isolate sensitivity to hygromycin B

Isolates E30, EL130, and EV339 were tested for sensitivity to hygromycin-B. All
three isolates were extremely sensitive to hygromycin-B with severe inhibition of growth
at 5 mg/L and complete inhibition at 50 mg/L (Figure 6.2) (data for EL130 and EV339
not shown). To lower the risk of false-positive transformants, the next higher

concentration, 100 mg/L, was used to screen all of the transformants.

Optimization of plasmid integration

Transformation reagents were optimized for transformation of E3 0. Protoplast
concentration was extremely important with reliable transformation occurring only at the
highest concentration of protoplasts (1x108). A protoplast concentration of 1x107
produced transformants in only one experiment and 1x10‘S was not sufficient for any
transformations (Table 6.1). DNA concentration also had an effect on transformation
rates (Table 6.2). The higher number of transformants occurred with 10 ug DNA. The
highest transformation rates occurred with the lowest DNA concentration (2.5 pg per
transformation). Using circular, undigested gGF P resulted in an approximate IO-fold
reduction in the number of transformants compared to linear gGFP. The concentration of
HindIII used in the transformation mix had a positive effect at the lowest concentration
tested (25 U per transformation), increasing the transformation rate by ~50% over linear
DNA with no restriction enzyme, but higher concentrations (50 U and 75 U) decreased
the transformation rate (Table 6.3). The optimal transformation used a protoplast

concentration of 1x108 with 10 pg linearized gGFP and 25 U of HindIII. Testing these

213

optimized concentrations on EL130 and EV339 resulted in transformation rates

consistent with those for E30 (Table 6.4).

214

If

50 rug-‘1. 100 1111291, 201.) mg I,

 

Figure 6.2. The effect of varying concentrations of hygromycin B on the growth of
Eutypa lata isolated from grapevine. Isolate E30 was grown on PDA amended with 0, 5,
10, 50, 100, and 200 mg/L hygromycin B. Growth was completely inhibited at 50 mg/L.

215

Table 6.1 The effect of protoplast concentration on the transformation rate of Eutypa lata
isolate E30 using 10 ug of HindIII-digested gGFP per transformation.

 

 

 

 

Experiment 1 Experiment 2
Protoplast Number of Transformation Number of Transformation
concentration per ml“ transformants rateb transformants rateb
1x106 0 0 0 0
1x107 0 0 2 0.4
11108 113 22.6 87 17.4

 

a Number of protoplasts per millileter.
b Number of transformants per microgram DNA.

216

Table 6.2. The effect of DNA concentration on the transformation rate of Eutypa lata
isolate E30 using linear, HindIII-digested gGFP and protoplasts at a concentration of
1x108 protoplasts per ml.

 

 

 

Experiment 1 Experiment 2
DNA concentration Number of Transformation Number of Transformation
(ug/ml) transformants rate‘I transformants rate‘I
2.5 41 16.4 36 14.4
5 75 15.0 80 16
10 122 12.2 114 11.4
20 1 15 5.8 96 4.8
5b 10 2 8 1.6

 

a Total number of 11g of gGFP.
b Five ug of undigested DNA.

217

Table 6.3. The effect of the concentration of restriction enzyme in the transformation mix
on transformation rate of Eutypa lata isolate E30 using 1011g HindIII-digested gGFP and
protoplasts at a concentration of 1x108 protoplastSJer ml.

 

 

 

 

Experiment 1 Experiment 2
HindIII Number of Transformation Number of Transformation
concentration (U) transformants rate' transformants rate"
0 112 11.2 126 12.6
25 170 17.0 189 18.9
50 54 5.4 84 8.4
75 12 1.2 27 2.7

 

a Number of transformants per ug DNA.

218

Table 6.4. Number of transformants and transformation rate (transformants per 11g DNA)
of Eutypa lata isolate EL130 and Eutypella vitis isolate EV3 39 using 10 ug HindIII-
digested gGFP and protoplasts at a concentration of 1x108.

 

 

 

 

Experiment 1 Experiment 2
HindIII Number of Transformation Number of Transformation
concentration (U) transformants rate‘I transformants rate‘I
EL130
0 1 10 1 1.0 125 12.5
25 166 25.0 170 17.0
EV339
0 104 10.4 162 16.2
25 97 9.7 140 14.0

 

a Number of transformants per microgram of DNA.

219

PCR of select transformants and growth on PDA to check stability

To confirm the presence of the gGFP insert in the genome of the hygromycin B-
resistant transformants, 12 putative transformants were screened by PCR analysis. Using
Hptl and Hpt2 primers, the expected 500-bp fragment was detected in all transformants.
The gGFP plasmid served as a positive control. No band was detected for in wild-type
E30 or negative water control (Figure 6.3). After four transfers on PDA without
hygromycin B, all transformants grew on PDA amended with hygromycin B indicating

that they all had rrritotically stable integrations.

Southern analysis of toxin-deficient candidates

Southern analysis of 22 toxin-deficient candidates generated through REMI
transformation indicated that the hph gene sequence was integrated into the genome of all
transformants (Figure 6.4). All transformants harboured a single copy insert and the
fragments on the blot were of a higher molecular weight than the digested gGF P plasmid,
indicating complete integration. Also, the fiagments were of differing sizes, indicating
that integration occurred in different locations within the genome. The E30 wild-type

strain did not contain any hph gene fragments.

220

2 3 4 5 6 7 8 91011121314151617

E
u
u
out
and
=

 

Figure 6.3. PCR conformation of transformation of Eutypa lata isolate E30 with gGFP
using primers Hptl and Hpt2. A 500 bp band indicates that the hgh gene on the gGFP
plasmid was successfully integrated into the genome of a transformant. A 500 bp band is
present in lanes 2-13 and 15. Lane 1 and17: l Kb+ DNA Ladder, Lanes 2-13: putative
hygromycin-resistant transformants, Lane 14: wild-type E30 isolate, Lane 15: gGFP
plasmid, Lane 16: negative control.

221

1_ 2 3 4 5 8 7 8 9101112131415161718192021222324

 

9.24 kb

Figure 6.4. Southern blot analysis of E30 transformants . Lane 1: linear gGFP, Lane 2:
Genomic DNA from Xbal digested E30 wild-type, Lanes 3-24: Xbal-restricted genomic
DNA of 22 putative toxin-deficient transformants generated through REMI. The blot was
probed with a 500 bp digoxigenin-labeled fragment of the hph gene.

222

Expression of gGFP in culture and in planta

Over 15 transformants were examined for GF P fluorescence using a confocal
microscope. All of the transformants had the typical emerald green fluorescence
associated with expression of GP P in the cytoplasm. Transformation of the El. vitis
isolate, EV339, also produced transformants expressing strong GFP fluorescence.
Transformants E30-13H and EV339-12H are shown in Figure 6.4 along with the E. lata
E30 wild-type and El. vitis EV339 wild-type.

Grapevine cuttings inoculated with transformants E30-13H and EV339-12H had
vascular necrosis typically seen with infections caused by these pathogens. Using
confocal microscopy, hyphae expressing GFP were easily seen colonizing the tracheary
elements six months after inoculation (Figure 6.5). Green autofluorescence was seen in

the mock-inoculated wood, however, hyphae were not seen.

223

 

_
’n'lum 1. 1 1,-»

Figure 6.5. Light, fluorescent, and overlay micrographs of mycelia of gGFP
transformants of Eutypella vitis and Eutypa lata. Mycelium of wild-type El. vitis. EV339,
A) light micrograph, B) fluorescent micrograph,C) overlay of A and B. Mycelium of El.
vitis transformant EV339-12H, D) light micrograph, E) fluorescent micrograph, F)
overlay of A and B. Mycelium of wild-type E. lata, E30, G) light micrograph, H)
fluorescent micrograph, l) overlay of A and B. Mycelium of E. lata transformant E30-
13H, I) light micrograph, K) fluorescent micrograph, L) overlay of A and B.

224

 

Figure 6.6. Light, fluorescent, and overly micrographs of gGF P transformants of Eutypa
lata and Eutypella vitis colonizing wood of ‘Concord’ grapevines 6 months after
inoculation. Steril agar-inoculated grapevine wood, A) light micrograph, B) fluorescent
micrograph, and C) overlay of of image A and B. Grapevine wood inoculated with E. lata
transformant E3 0-13H, D) light micrograph, E) fluorescent micrograph F) overlay of
image D and E. Grapevine wood inoculated with El. vitis transformant EV339-12H G)
light micrograph, H) fluorescent micrograph, J) overlay of image D and E.

225

Toxicity screen

The culture media of 2184 transformants of E30 were screened for toxicity to
BY2 cells. Twenty two did not induce cell death based on Trypan blue staining of BY2
cells. The E30 wild-type caused complete cell death in every screen whereas the MYB

amended with hygromycin B was not toxic to BY2 cells (Figure 6.7)

226

 

Figure 6.7. Cell viability assay using tobacco BY2 cells stained with 0.04% Trypan blue.
A) Untreated cells, B) Cells incubated with malt extract/yeast extract broth. C) Cells
incubated for 3 days with an equal volume of culture filtrate from Eutypa lata isolate
E30, Note blue staining resulting fi'om sell death, D) Cells incubated for 3 days with and
equal volume of culture filtrate from gGFP transformant E30-928B.

227

Discussion

Protoplast transformation by electroporation and Agrobacterirun-mediated
integration were initially tested without success. Plasmid integration into protoplasts
using PEG and RED/fl proved more successful, consistently achieving ~150 transformants
per transformation experiment. With the REMI method, linearized plasmid DNA is
integrated into the fungal genome in the presence of a restriction enzyme. The restriction
enzyme generates compatible ends that typically serve as the site of integration. In this
study, the addition of a restriction enzyme to the transformation mix increased
transformation rates by ~50%, but only at the lower restriction enzyme concentration.
Higher concentrations of HindIII decreased transformation rates, a phenomenon seen in
other studies (10, 32). This is likely due to double strand breakage that cannot be fixed
due to presence of excessive amounts of restriction enzyme (15). Toxicity of restriction
enzymes is another possible explanation, but concentrations up to 600U have been used
with no effect on protoplast regeneration (29).

The PCR and Southern analysis confirmed that the gGFP plasmid had
successfully been integrated into the E. lata fungal genome, and that integrations were
mitoticaly stable. The 22 putative toxin-deficient transformants all had single insertions
into different areas of the genome. This will make sequencing the flanking DNA, either
through plasmid rescue or Thermal Assymetric InterLaced PCR (TAIL-PCR), much
easier if any of these transformants are determined to be actually deficient in toxin
production.

For this study, a plasmid with a GFP gene was chosen to allow for easy detection

and visualization in the host plant. The main advantage of using GFP is its property as a

228

vital stain. This enabled able visualization of the pathogen inside infected wood tissue,
without having to stain with a chemical, a process that typically kills the pathogen and
causes some deterioration of the plant material. The ability to see El. vitis colonizing
plant tissue also lends support to its classification as a pathogen.

This is the first report of integration of foreign DNA into E. lata, a major
pathogen of grapevines, and El. vitis, a new pathogen of grapevines. Development of a
transformation system is the first step in discovering and understanding the function of
pathogenicity and virulence genes. The ultimate goal of this research is to characterize
genes involved in toxin production to determine the role of toxins in pathogenesis. A
better understanding of the role of toxins in the development of Eutypa dieback may lead
to novel approaches in the management of this widespread and serious disease.
Determination of the role of specific toxins in pathogenicity or virulence will also allow
further studies of the mechanism of action and environmental effects on symptom
expression. If toxins are critical to disease development or virulence, they may also be
used to rapidly and reliably screen grape genotypes for resistance and aid the
development of marker-assisted selection in grape breeding programs. Improved
resistance and the ability to manage Eutypa dieback more effectively will reduce annual
losses from the disease as well as prolong vineyard longevity. Both of these will translate

into increased economic viability and sustainability of grape and wine production in areas

afflicted by the disease.

229

10.

ll.

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233

APPENDICES

234

APPENDIX A

SUPPLEMENTARY FIGURES FOR CHAPTER 2

235

2003 2004 2005

 

Figure A. 1. Map of Eutypa dieback incidence for vineyard A. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

236

2003 2004 2005

 

Figure A.2. Map of Eutypa dieback incidence for vineyard B. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows rtm in the direction
of the y axis.

237

2003 N>+

2006

 

2004

 

2007

 

2005

   

Figure A.3. Map of Eutypa dieback incidence for vineyard C. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

238

2006 2007
N—>

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure A.4. Map of Eutypa dieback incidence for vineyard D. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

239

2006

2007

 

Figure A.5. Map of Eutypa dieback incidence for vineyard E. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

240

2006 2007

 

Figure A.6. Map of Eutypa dieback incidence for vineyard F. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

241

2006 2007

 

Figure A.7. Map of Eutypa dieback incidence for vineyard G. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

242

2006 2007

 

Figure A.8. Map of Eutypa dieback incidence for vineyard H. The locations of individual
symptomatic vines are represented as black rectangles. Vineyard rows run in the direction
of the y axis.

243

(108

 

04MB-

(104-

 

 

1102 -

Moran's l

(100

 

4102 -

 

4104..

 

 

4106 r r 1 4r
0 10 20 30 40 50 60 7O 80

 

Separation distance (m)

0.10
B —o— 2003

(108 . ...(Du... 2004
——+-- 2005

 

- '-Il-- 2007

 

 

0.04 4 3
r-

1102 4

C)

. ' I" -
-o.02- \ . \sf ‘ \
v ‘ /
1
~004- w

4106 -

 

Moran's l

 

 

4108 r r r r
0 10 20 30 40 50 60 70 80

 

Separation distance (m)

Figure A.9. Isotropic Moran’s I correlograms derived from Vitis labrusca ‘Concord’
vineyards in Souhwest Michigan. Moran’s I values are plotted by separation distances of
symptomatic vines in 2x2 quadrats for each year of the study. A) Vineyard A, B)
Vineyard B, C) Vineyard C, D) Vineyard D, E) Vineyard E, F) Vineyard F, G) Vineyard
G, H) Vineyard H.

244

(125

 

C —o— 2003

C120

.40

 

0155-

 

1105 ~

Moran's I

(100

 

 

 

4105 4

 

'0.10 I r I I I
O 10 20 3O 40 50 60

 

Separation distance (m)

010

 

(108 -

 

 

 

(106 *

(104 -

(102 -

Moran's I

(100

 

 

4102 -

4104 -

 

 

’0.06 l I I l l
0 10 20 30 40 5O 60

 

Separation distance (m)

Figure A.9. (cont’d).

245

 

0.12

 

0.10 1
0.08 e
0.06 -
0.04 -

0.02 4

Moran's I

0.00

 

+2006

 

 

-o.o2 -
-o.04 -

-0.06 J

 

 

 

-0.08 r I

I I

0 1O 20 3O 40

Separation distance (m)

0.04

 

0.03 -

0.02 -

0.01 -

Moran's I

0.00 ~

 

 

 

-0.01 -

 

-0.02 1 r

 

 

Separation distance (m)

Figure A.9. (cont’d).

246

50

 

60

 

0.06

 

G

0.04 -

0.02 -

0.00

 

+ 2006
- -O - 2007

 

 

Moran's I

-0.02 a

 

 

 

 

-0.06 r

0.03

I I I

20 30 40

Separation distance (m)

50

 

60

 

0.02 ~

0.01 .

0.00

 

+ 2006
- -O - 2007

 

 

 

-0.01 *

Moran's I

-0.02 -

-0.03 r

-0.04 4

 

-0.05 r

 

 

 

Figure A.9. (cont’d).

I I I

20 30 40

Separation distance (m)

247

50

60

 

 

 

 

 

 

 

 

 

0.15 -
A
0.10 -
:07, 0.05-
C
i!
o
2
0.00
-0.054
’0.10 l I I I I I
o 10 2o 30 4o 50 60 70
Separation distance (m)
0.2
B —o— 2003
-o- 2004
——+-— 2005
— —A-—-- 2006
0.1 - —-o— 2007

 

 

Moran's I

 

 

-0-2 I I I I T T
O 10 20 3O 40 50 60 70

Separation distance (m)

Figure A.10. Isotropic Moran’s I correlograms derived from Vitis labrusca ‘Concord’
vineyards in Souhwest Michigan. Moran’s I values are plotted by separation distances of
symptomatic vines in 3x3 quadrats for each year of the study. A) Vineyard A, B)

Vineyard B, C) Vineyard C, D) Vineyard D, E) Vineyard E, F) Vineyard F, G) Vineyard
G, H) Vineyard H.

 

 

248

C125

 

C —o— 2003
020— .. .0”... 2004

' ___';_-Q ---v——- 2005

l? \ —- —A-—- - 2006

015 -

 

 

040 ~

005-

Moran's I

000

 

l

-005

 

-010 ‘

 

‘0.15 l I r I I

 

0 ' 10 20 30 40 5O 60

Separation distance (m)

020

 

 

045 -

 

 

0.10-4

005-

000

 

Moran's I

4105*

 

-OJO'*

 

 

-015 r r u r
0 1O 20 30 4O 50

Separation distance (m)

Figure A.10. (cont’d).

249

 

0.15

 

E —o— 2006

 

0.104

 

0.05 -

0.00

 

Moran's I

-0.05 1

 

-0.10 -

 

”0.1 5 I I I I
O 10 20 30 40 50

 

Separation distance (m)

0.10

+ 2006
F 0.08 . - ~O - 2007

 

 

 

 

0.06 1

0.04 -

0.02 -

Moran's I

0.00

 

-0.02 1

-0.04 1

-0.06 -

 

 

 

 

-0.08 r r 1 r
0 10 20 30 4O 50

Separation distance (m)

Figure A.10. (cont’d).

250

I108

 

G 0.06 1

 

 

(104 1

(102 -

(100

 

4102 1

Moran's I

-0.04 1
-0.06 -

4108 1

 

4110 1

 

 

4112 . l I I
0 10 20 30 40 50

Separation distance (m)

(106

H -o— 2006
- -o - 2007
00m ‘

 

 

 

 

(102 1

 

(100

 

Moran's I

4102 1

4104 1

 

4106 1

 

 

 

Separation distance (m)

Figure A. 1 0. (cont’d).

251

Semivariance

Semivariance

0.88

 

0.86 1

+ 2006
-O - 2007

 

 

0.84 1

0.82 1

0.80 1

0.78 1

0.76 1

0.74 1

0.72 1

0.70 1

 

0.68

 

 

 

2.6

2.4 1

2.2 1

2.0 1

1.81

1.6

10

I I I I

20 30 40 50

Separation distance (m)

60

 

 

+ 2006
-O- 2007

 

 

 

 

 

I I I I

1 0 20 30 4O

Separation distance (m)

50

Figure A.11. Semivariograms of vines symptomatic for Eutypa dieback from Vitis
labrusca ‘Concord’ vineyard D located in Souhwest Michigan. Moran’s I values are
plotted by separation distances of symptomatic vines. A) 2 X 2 quadrats, B) 3 X 3
quadrats.

252

 

(185
A + 2006
0.80 . --O-- 2007

 

 

0.75 - 3' " -. ,, ..o "

0.65 ~ 0 “ 0

Semivariance

0.60 1
0.55 1

0.50 I

 

 

 

0.45 I I 1— I I
0 10 20 30 40 50 60

Separation distance (m)

 

 

 

 

 

 

2.2
B —o— 2006
--o 2007 ....o,
o
2.01 .0.....0
8 1.81
..o- .. .-
" -o
g o
'5 16-
.2. '
E
d)
a)
1.4 -
1.2 -
1.0 l I I I I
0 10 20 30 40 50 60

Separation distance (m)

Figure A.12. Semivariograms of vines symptomatic for Eutypa dieback from Vitis
labrusca ‘Concord’ vineyard E located in Souhwest Michigan. Moran’s I values are
plotted by separation distances of symptomatic vines. A) 2 X 2 quadrats, B) 3 X 3
quadrats.

253

APPENDIX B

STATISTICAL OUTPUT FROM STEPWISE REGRESSION PROCEDURES

254

 

year=2003 vineyard=A

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard

Variable Estimate Error Type II 88 F Value Pr > F
Intercept 46.45368 9.92087 8656.69439 21.93 <.0001
% symp shoots 0.55621 0.09373 13904 35.21 <.0001
shoot number -0.58351 0.13537 7336.14643 18.58 <.0001

Bounds on condition number: 1.5882, 6.3527

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

1 %symptomatic shoots 1 0.6125 0.6125 19.5805 88.51 <.0001
2 shoot number 2 0.0979 0.7103 3.0000 18.58 <.0001

 

year=2003 vineyard=B

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard

Variable Estimate Error Type II 88 P Value Pr > F
Intercept 43.01654 12.29299 3878.93200 12.24 0.0011
% sym shoots 0.40600 0.13990 2667.96767 8.42 0.0059
shoot number -0.39167 0.13575 2637.12548 8.32 0.0061

Bounds on condition number: 1.5826, 6.3306

 

255

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
1 %sym 1 0.4050 0.4050 9.3248 29.27 <.0001
2 shoot number 2 0.0984 0.5035 3.0000 8.32 0.0061

 

year=2003 vineyard=C

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard

Variable Estimate Error Type II 88 F Value Pr > F
Intercept 72.65826 11.21794 9524.25698 41.95 <.0001
%sym shoots 0.42945 0.17312 1397.02334 6.15 0.0232

shoot number -1.30071 0.21480 8325.03749 36.67 <.0001

Bounds on condition number: 1.2024, 4.8096

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
I shoot number 1 0.7135 0.7135 7.1534 47.32 <.0001

2 %symptomatic shoots 2 0.0730 0.7865 3.0000 6.15 0.0232

 

year=2004 vineyard=A

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

256

Parameter Standard

Variable Estimate Error Type II 88 F Value Pr > F

Intercept 29.88427 11.05334 2715.44118 7.31 0.0092
% sym shoots 0.57613 0.08935 15445 41.58 <.0001
shoot number 0.52145 0.18838 2846.53745 7.66 0.0077

Bounds on condition number: 1.5413, 6.1652

 

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
l % sym shoots 1 0.6244 0.6244 8.6626 89.76 <.0001
2 shoot number 2 0.0474 0.6718 3.0000 7.66 0.0077

 

year=2004 vineyard=B

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 75.42777 22.66833 2702.91738 1 1.07 0.0067
% sym shoots 0.60956 0.18422 2672.94373 10.95 0.0070
shoot number -1 .45896 0.43597 2733.87828 11.20 0.0065

Bounds on condition number: 1.3555, 5.4219

 

All variables left in the model are significant at the 0.1500 level.

All variables have been entered into the model.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

257

1 shootnumber 1 0.6108 0.6108 11.9492 18.83 0.0010
2 percent symptomatic shoots 2 0.1942 0.8049 3.0000 10.95 0.0070

 

year=2004 vineyard=C

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 P Value Pr > F

Intercept 34.52226 13.65735 1763.71641 6.39 0.0188
% sym shoots 0.71554 0.13541 7707.63471 27.92 <.0001
shoot number 09581 0.42195 1861.72403 6.74 0.0161

Bounds on condition number: 1.6631, 6.6524

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
l %symptomatic shoots 1 0.7283 0.7283 7.7445 64.33 <.0001
2 shoot number 2 0.0616 0.7899 3.0000 6.74 0.0161

 

 

year=2005 vineyard=A

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter tandard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 71.24192 16.99686 6553.79295 17.57 0.0002
% sym shoots 0.41770 0.14078 3284.25374 8.80 0.0054
shoot number -0.89010 0.22629 5771.53449 15.47 0.0004

Bounds on condition number: 2.3776, 9.5103

258

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
I shoot number 1 0.6754 0.6754 9.8040 74.92 <.0001
2 %sym shoots 2 0.0652 0.7407 3.0000 8.80 0.0054

 

 

year=2005 vineyard=B

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error ype II 88 F Value Pr > F

Intercept 72.45036 17.91382 5146.79436 16.36 0.0005
% sym shoots 0.36909 0.18556 1244.85776 3.96 0.0593
shoot number -0.73147 0.16241 63 82.28589 20.28 0.0002

Bounds on condition number: 1.5432, 6.1728

 

All variables left in the model are significant at the 0.1500 level.

All variables have been entered into the model.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
I shoot number 1 0.6576 0.6576 4.9563 44.18 <.0001
2 %sym shoots 2 0.0522 0.7098 3.0000 3.96 0.0593

 

 

year=2005 vineyard=C

259

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard

Variable Estimate Error Type II 88 F Value Pr > F

Intercept 74.02853 12.08354 10092 37.53 <.0001
% sym shoots 0.28457 0.11554 1631.18265 6.07 0.0200
shoot number -1.22974 0.23409 7420.25470 27.60 <.0001

Bounds on condition number: 1.341, 5.3641

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr> F
I shoot number 1 0.6174 0.6174 7.0667 48.40 <.0001
2 %sym shoots 2 0.0662 0.6836 3.0000 6.07 0.0200

 

year=2006 vineyard=A

 

The REG Procedure
Dependent Variable: percent yield loss

Number of Observations Read 37
Number of Observations Used 37

Analysis of Variance

Sum of Mean
Source DF Squares Square FValue Pr>F
Model 1 23804 23804 104.71 <.0001
Error 35 7956.96320 227.3418]

Corrected Total 36 31761

Root MSE 15.07786 R-Square 0.7495
Dependent Mean 29.83784 Adj R-Sq 0.7423

260

Coeff Var 50.53268

 

Parameter Estimates

Parameter Standard
Variable DF Estimate Error tValue Pr > M Tolerance

Intercept 1 0.29955 3.8049] 0.08 0.9377 .
%sym shoots 1 0.71339 0.06972 10.23 <.0001 1.00000

 

 

 

year=2006 vineyard=B

The REG Procedure
Dependent Variable: percent yield loss

 

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 68.00362 16.93967 4454.43251 16.12 0.0005
% sym shoots 0.48060 0.14563 3010.34504 10.89 0.0028
shoot number -1 . 10623 0.29680 3839.78220 13.89 0.0009

Bounds on condition number: 2.0751, 8.3003

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
I shoot number 1 0.6769 0.6769 11.8913 56.56 <.0001
2 %sym shoots 2 0.0954 0.7723 3.0000 10.89 0.0028

 

 

year=2006 vineyard=C

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

261

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 28.17048 9.33587 1379.83851 9.10 0.0046
% sym shoots 0.64090 0.07543 10942 72.20 <.0001
shoot number -0.47808 0.18049 1063.24287 7.02 0.01 18

Bounds on condition number: 1.9894, 7.9576

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) P Value Pr> F

l %sym shoots 1 0.8292 0.8292 8.0159 184.52 <.0001
2 shoot number 2 0.0272 0.8564 3.0000 7.02 0.0118

262

All Years and Vineyards
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F
Intercept 28.43024 3.03548 3185] 87.72 <.0001
% sym shoots 0.62767 0.03300 131378 361.83 <.0001
shoot number -0.38037 0.04457 26439 72.82 <.0001

Bounds on condition number: 1.3436, 5.3745

 

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Number Partial Model
Vars In R-Square R-Square C(p)

Variable

Step Entered F Value Pr > F

0.5985 73.8153 624.71 <.0001
0.6581 3.0000 72.82 <.0001

0.5985
0.0596

1 % sym shoots 1
2 shoot number 2

 

year=2003 vineyard=A

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F
Intercept 45.08634 1 1.09607 71 1 1.09148 16.51 0.0002
DSS 12.37014 2.35040 11930 27.70 <.0001
shoot number -0.64843 0.13811 9493.80084 22.04 <.0001

Bounds on condition number: 1.5155, 6.062

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

263

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

1 D88 1 0.5574 0.5574 23.0422 70.52 <.0001
2 shoot number 2 0.1266 0.6840 3.0000 22.04 <.0001

 

 

year=2003 vineyard=B

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 33.42160 11.21765 234479486 8.88 0.0048
DSS 9.79070 2.27828 4878.29630 18.47 0.0001
shoot number -0.37183 0.11549 2738.13151 10.37 0.0025

Bounds on condition number: 1.3737, 5.495

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F
1 D88 1 0.4838 0.4838 11.3657 40.30 <.0001

2 shoot number 2 0.1022 0.5860 3.0000 10.37 0.0025

 

year=2003 vineyard=C

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

264

Intercept 69.75207 13.4701 1 6682.18192 26.81 <.0001
DSS 7.41001 3.70274 998.01574 4.00 0.0607
shoot number -1.31517 0.22918 8206.30244 32.93 <.0001

Bounds on condition number: 1.2471 , 4.9882

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

I shoot number 1 0.7135 0.7135 5.0049 47.32 <.0001
2 D88 2 0.0521 0.7657 3.0000 4.00 0.0607

 

year=2003 vineyard=A

 

The REG Procedure
Dependent Variable: percent yield loss
Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 69.75207 13.4701 1 6682.18192 26.81 <.0001
DSS 7.41001 3.70274 998.01574 4.00 0.0607
shoot number -1.31517 0.22918 820630244 32.93 <.0001

Bounds on condition number: 1.2471, 4.9882

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

1 shootnumber 1 0.7135 0.7135 5.0049 47.32 <.0001
2 D88 2 0.0521 0.7657 3.0000 4.00 0.0607

 

year=2004 vineyard=B

 

265

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type 11 88 F Value Pr > F

Intercept 65.29253 26.24961 1642.61194 6.19 0.0302
DSS 12.23238 4.03676 2437.87191 9.18 0.0114
shoot number -1 .28429 0.49332 1799.38559 6.78 0.0245

Bounds on condition number: 1.5958, 6.3833

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr> F
1 D88 1 0.6572 0.6572 7.7775 23.00 0.0004

2 shoot number 2 0.1307 0.7879 3.0000 6.78 0.0245

 

year=2004 vineyard=C

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 38.46958 11.76870 2523.03089 10.69 0.0034
DSS 15.09026 2.49676 8625.51402 36.53 <.0001
shoot number -l.31476 0.36093 3133.12203 13.27 0.0014

Bounds on condition number: 1.4226, 5.6903

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

266

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

1 D88 1 0.7166 0.7166 14.2688 60.69 <.0001
2 shoot number 2 0.1037 0.8203 3.0000 13.27 0.0014

 

year=2005 vineyard=A

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 76.29920 17.36336 7588.65462 19.31 <.0001
DSS 8.46355 3.29955 2585.76764 6.58 0.0148
shoot number -1 .01742 0.21231 9024.96439 22.96 <.0001

Bounds on condition number: 1.9865, 7.9462

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

I shoot number 1 0.6754 0.6754 7.5796 74.92 <.0001
2 D88 2 0.0514 0.7268 3.0000 6.58 0.0148

 

year=2005 vineyard=B

 

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 72.94184 16.54264 5867.13000 19.44 0.0002

267

 

DSS 7.57822 3.36758 1528.19866 5.06 0.0348
shoot number -0.76966 0.14507 8494.31104 28.15 <.0001

Bounds on condition number: 1.2837, 5.1349

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

I shoot number 1 0.6576 0.6576 6.0640 44.18 <.0001
2 D88 2 0.0641 0.7217 3.0000 5.06 0.0348

 

year=2005 vineyard=C

 

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 67.95221 12.60536 73 72.82650 29.06 <.0001
DSS 7.19090 2.51687 2071 .01674 8.16 0.0078
shoot number -1 . 17919 0.22985 6677.76207 26.32 <.0001

Bounds on condition number: 1.3701, 5.4805

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr > F

I shoot number 1 0.6174 0.6174 9.1629 48.40 <.0001
2 D88 2 0.0840 0.7014 3.0000 8.16 0.0078

 

year=2006 vineyard=A

 

The REG Procedure

268

Dependent Variable: percent yield loss
Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 16.32915 14.28430 299.75285 1.31 0.2610
DSS 14.56864 2.58265 7298.94453 31.82 <.0001
shoot nmnber -0.39187 0.23187 655.17713 2.86 0.1002

Bounds on condition number: 2.1487, 8.5948

 

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Number Partial Model
Step Entered Vars In R-Square R-Square C(p) F Value Pr> F

1 D88 1 0.7338 0.7338 3.8563 96.49 <.0001
2 shoot number 2 0.0206 0.7545 3.0000 2.86 0.1002

 

year=2006 vineyard=B

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard

Variable Estimate Error Type II 88 F Value Pr > F
Intercept 62.69667 15.01918 4043.67556 17.43 0.0003
DSS 12.45622 2.94068 4163.46994 17.94 0.0003

shoot number -1.ll389 0.25049 4588.74001 19.77 0.0001

Bounds on condition number: 1.7605, 7.0421

 

All variables left in the model are significant at the 0.1500 level.

Summary of Stepwise Selection

269

Variable Variable Nrunber Partial Model
Step Entered Removed Vars In R-Square R-Square C(p) F Value Pr > F

I shoot number 1 0.6769 0.6769 18.9422 56.56 <.0001
2 D88 2 0.1319 0.8088 3.0000 17.94 0.0003

 

year=2006 vineyard=C

 

The REG Procedure
Dependent Variable: percent yield loss

Stepwise Selection: Step 2

Parameter Standard
Variable Estimate Error Type II 88 F Value Pr > F

Intercept 29.00371 7.17222 1652.66316 16.35 0.0003
DSS 15.67936 1.39266 12810 126.75 <.0001
shoot number -0.68394 0.13027 2785.67406 27.56 <.0001

Bounds on condition number: 1.554, 6.2161

All variables left in the model are significant at the 0.1500 level.
Summary of Stepwise Selection

Variable Variable Number Partial Model
Step Entered Removed Vars In R-Square R-Square C(p) F Value Pr > F

1 D88 1 0.8330 0.8330 28.5643 189.48 <.000
2 shoot nrunber 2 0.0713 0.9043 3.0000 27.56 <.0001

Overall Model for DSS for all Years and Vineyards

Dependent Variable: percent yield loss

Number of Observations Read 421
Number of Observations Used 42]

Analysis of Variance

270

Sum of Mean

Source DF Squares Square F Value Pr > F
Model 2 299515 149757 433 .51 <.0001
Error 418 144399 345.45226

Corrected Total 420 443914

Root MSE 18.58635 R-Square 0.6747
Dependent Mean 28.76152 Adj R-Sq 0.6732
Coeff Var 64.62227

Parameter Estimates

Parameter Standard
Variable DF Estimate Error tValue Pr> |t| Tolerance

Intercept 25.94024 3.00921 8.62 <.0001

1 .
D88 1 14.54189 0.72560 20.04 <.0001 0.81629
shoot number 1 -0.45255 0.04152 -10.90 <.0001 0.81629

271

APPENDIX C

AN OVA OUTPUT FROM CHAPTER 3

272

 

 

 

year=2003 vineyard=A

 

The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 15856.17628 3964.04407 11.44 <.0001
Error 53 18363.20303 346.47553

Corrected Total 57 34219.37931

R-Square Coeff Var Root MSE shoot Mean
0.463368 36.42387 18.61385 51.10345

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 15856.17628 3964.04407 11.44 <.0001
year=2003 vineyard=B
The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 9992.84563 2498.21141 5.81 0.0009
Error 40 17213.73214 430.34330

Corrected Total 44 27206.57778

R-Square Coeff Var Root MSE shoot Mean
0.367295 30.14247 20.74472 68.82222

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 9992.845635 2498.211409 5.81 0.0009
year=2003 vineyard=C
The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 3 1861.421429 620.473810 2.60 0.0858
Error 17 4055.150000 238.538235

Corrected Total 20 5916.571429

R-Square Coeff Var Root MSE shoot Mean
0.314612 35.21589 15.44468 43.85714

Source DF Anova 88 Mean Square F Value Pr> F
D88 3 1861.421429 620.473810 2.60 0.0858

273

 

 

year=2004 vineyard=A

The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of
Source DF Squares Mean Square P Value Pr > F
Model 4 6760.7611] 1690.19028 9.20 <.0001
Error 51 9374.36389 183.81106

Corrected Total 55 16135.12500

R-Square Coeff Var Root MSE shoot Mean
0.419009 29.23492 13.55769 46.37500

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 6760.761111 1690.190278 9.20 <.0001
year=2004 vineyard=B
The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 960.553571 240.138393 2.77 0.0941
Error 9 780.375000 86.708333
Corrected Total 13 1740.928571

R-Square Coeff Var Root MSE shoot Mean
0.551748 19.78213 9.311731 47.07143

 

 

Source DF Anova 88 Mean Square P Value Pr > F
D88 4 960.5535714 240.1383929 2.77 0.0941
year=2004 vineyard=C
The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 1154.782751 288.695688 4.26 0.01 12
Error 21 1423.678788 67.794228

Corrected Total 25 2578.461538

R-Square Coeff Var Root MSE shoot Mean
0.447857 32.33789 8.233725 25.46154

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 1154.782751 288.695688 4.26 0.0112

274

 

 

year=2005 vineyard=A

The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 9674.63759 2418.65940 10.44 <.0001
Error 33 7645.25714 231.67446

Corrected Total 37 17319.89474

R-Square Coeff Var Root MSE shoot Mean
0.558585 26.26669 15.22086 57.94737

 

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 9674.637594 2418.659398 10.44 <.0001
year=2005 vineyard=B
The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 5286.13095 1321.53274 2.01 0.1312
Error 20 13121.86905 656.09345

Corrected Total 24 18408.00000

R-Square Coeff Var Root MSE shoot Mean
0.287165 27.90231 25.61432 91.80000

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 5286.130952 1321.532738 2.01 0.1312

year=2005 vineyard=C

The AN OVA Procedure
Dependent Variable: shoot number per vine

Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 2521.141369 630.285342 4.19 0.0091
Error 27 4058.827381 150.326940

Corrected Total 31 6579.968750

R-Square Coeff Var Root MSE shoot Mean
0.383154 29.56633 12.26079 41.46875

Source DF Anova 88 Mean Square F Value Pr> F
D88 4 2521.141369 630.285342 4.19 0.0091

275

 

 

 

year=2006 vineyard=A

The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 5463.478282 1365.869570 11.80 <.0001
Error 32 3704.089286 115.752790

Corrected Total 36 9167.567568

R-Square Coeff Var Root MSE shoot Mean
0.595957 25.68241 10.75885 41.89189

 

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 5463 .478282 1365.869570 11.80 <.0001
year=2006 vineyard=B
The AN OVA Procedure
Dependent Variable: shoot number per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Error 24 2761.922619 115.080109

|
Model 4 3749042898 93 7.260725 8.14 0.0003 i
Corrected Total 28 65 10.965517

R-Square Coeff Var Root MSE shoot Mean
0.575804 23.33823 10.72754 45.96552

 

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 3749.042898 937.260725 8.14 0.0003

year=2006 vineyard=C

The AN OVA Procedure
Dependent Variable: shoot number per vine

Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 5202.420635 1300.605159 1 1.23 <.0001
Error 35 4051.954365 115.770125

Corrected Total 39 9254375000

R-Square Coeff Var Root MSE shoot Mean
0.562158 28.40832 10.75965 37.87500

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 5202.420635 1300.605159 11.23 <.0001

276

 

 

 

year=2003 vineyard=A

The ANOVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 80442.7305 20110.6826 19.38 <.0001
Error 53 55003.6833 1037.8053

Corrected Total 57 135446.4138

R-Square Coeff Var Root MSE clusters per vine Mean
0.593908 39.13844 32.21499 82.31034

 

 

Source DF Anova 88 Mean Square P Value Pr > F
D88 4 80442.73046 20110.68261 19.38 <.0001
year=2003 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square P Value Pr > F
Model 4 60638.7167 15159.6792 9.29 <.0001
Error 40 65275.5944 1631.8899

Corrected Total 44 125914.311]

R-Square Coeff Var Root MSE clusters per vine Mean
0.481587 25.46364 40.39666 158.6444

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 60638.71671 15159.67918 9.29 <.0001
year=2003 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of

Source DF Squares Mean Square P Value Pr > F
Model 3 10326.26667 3442.08889 3.87 0.0281
Error 17 15117.73333 889.27843

Corrected Total 20 25444.00000

R-Square Coeff Var Root MSE clusters per vine Mean
0.405843 33.13419 29.82077 90.00000

Source DF Anova 88 Mean Square F Value Pr > F
D88 3 10326.26667 3442.08889 3.87 0.0281

277

 

 

year=2004 vineyard=A

The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square P Value Pr > F
Model 4 54179.93929 13544.98482 27.22 <.0001
Error 51 25377.77500 497.60343

Corrected Total 55 79557.71429

R-Square Coeff Var Root MSE clusters per vine Mean
0.681014 25.76719 22.30703 86.57143

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 54179.93929 13544.98482 27.22 <.0001
year=2004 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 9539.48214 2384.87054 2.71 0.0986
Error 9 7916.87500 879.65278

Corrected Total 13 17456.35714

R-Square Coeff Var Root MSE clusters per vine Mean
0.546476 35.21842 29.65894 84.21429

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 9539.482143 2384.870536 2.7] 0.0986
year=2004 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 7580.026340 1895.006585 16.66 <.0001
Error 2] 2388.012121 113.714863

Corrected Total 25 9968.03 8462

R-Square Coeff Var Root MSE clusters per vine Mean
0.760433 24.91075 10.66372 42.80769

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 7580.026340 1895.006585 16.66 <.0001

278

 

year=2005 vineyard=A

 

The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 372038.857] 93009.7143 11.59 <.0001
Error 33 264824.6429 8024.9892

Corrected Total 37 636863.5000

R-Square Coeff Var Root MSE clusters per vine Mean
0.584174 37.09412 89.58230 241.5000

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 372038.857] 93009.7143 11.59 <.0001
year=2005 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 136632.648] 34158.1620 1.91 0.1483
Error 20 357867.5119 17893.3756

Corrected Total 24 494500.1600

R-Square Coeff Var Root MSE clusters per vine Mean
0.276305 35.3355] 133.7661 378.5600

Source DF Anova 88 Mean Square F Value Pr > F

 

 

D88 4 136632.648] 34158.1620 1.91 0.1483
year=2005 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 45542.96280 11385.74070 8.20 0.0002
Error 27 37500.25595 1388.89837
Corrected Total 31 83043.21875

R-Square Coeff Var Root MSE clusters per vine Mean
0.548425 29.49725 37.26793 126.3438

Source DF Anova 88 Mean Square P Value Pr > F
D88 4 45542.96280 11385.74070 8.20 0.0002

279

 

 

year=2006 vineyard=A

The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 8968.40106 2242.10027 26.73 <.0001
Error 32 2684.51786 83.89118

Corrected Total 36 1 1652.91892

R-Square Coeff Var Root MSE clusters per vine Mean
0.769627 21.50323 9.159213 42.59459

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 8968.401062 2242.100265 26.73 <.0001
year=2006 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 22028.22763 5507 .0569] 12.46 <.0001
Error 24 10605.56548 441.89856

Corrected Total 28 32633.79310

R-Square Coeff Var Root MSE clusters per vine Mean
0.675013 29.49299 21.02138 71.27586

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 22028.22763 5507.05691 12.46 <.0001
year=2006 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per vine
Sum of

Source DF Squares Mean Square F Value Pr > F
Model 4 26884.14563 6721.03641 48.12 <.0001
Error 35 4888.82937 139.68084

Corrected Total 39 31772.97500

R-Square Coeff Var Root MSE clusters per vine Mean
0.846132 20.04013 11.81866 58.97500

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 26884.14563 6721.03641 48.12 <.0001

280

year=2003 vineyard=A

 

 

The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 13.36632706 3.34158177 4.05 0.0061
Error 53 43.73712121 0.82522870

Corrected Total 57 57.10344828

R-Square Coeff Var Root MSE clusters per shoot Mean
0.234072 54.88377 0.908421 1.655172

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 13.36632706 3.34158177 4.05 0.0061

year=2003 vineyard=B

The AN OVA Procedure
Dependent Variable: clusters per shoot

Stun of
Source DF Squares Mean Square F Value Pr > F
Model 4 5.7537385] 1.43843463 2.00 0.1136
Error 40 28.82403926 0.72060098

Corrected Total 44 34.57777778

R-Square Coeff Var Root MSE clusters per shoot Mean
0.166400 32.37263 0.848882 2.622222

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 5.7537385] 1.43843463 2.00 0.1136
year=2003 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 3 1.83809524 0.6126984] 0.82 0.5016
Error 17 12.73333333 0.7490196]

Corrected Total 20 14.57142857

R-Square Coeff Var Root MSE clusters per shoot Mean
0.126144 40.38810 0.865459 2.142857

Source DF Anova 88 Mean Square F Value Pr> F
D88 3 1.83809524 0.6126984] 0.82 0.5016

281

 

 

year=2004 vineyard=A

The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 22.70158730 5.67539683 15.94 <.0001
Error 51 18.15555556 0.35599129

Corrected Total 55 40.85714286

R-Square Coeff Var Root MSE clusters per shoot Mean
0.555633 32.12731 0.596650 1.857143

 

 

Source DF Anova 88 Mean Square P Value Pr > F
D88 4 22.70158730 5.67539683 15.94 <.0001
year=2004 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per shoot
Smn of
Source DF Squares Mean Square F Value Pr > F
Model 4 3.35714286 0.8392857] 5.04 0.0208
Error 9 1.50000000 0.16666667

Corrected Total 13 4.85714286

R-Square Coeff Var Root MSE clusters per shoot Mean
0.691176 23.81448 0.408248 1.714286

 

 

Source DF Anova 88 Mean Square P Value Pr > F
D88 4 3.35714286 0.8392857] 5.04 0.0208
year=2004 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square P Value Pr > F
Model 4 7.64219114 1.91054779 3.92 0.0158
Error 21 1024242424 0.48773449

Corrected Total 25 17.88461538

R-Square Coeff Var Root MSE clusters per shoot Mean
0.427305 42.22762 0.698380 1.653846

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 7.64219114 1.91054779 3.92 0.0158

282

 

year=2005 vineyard=A

 

The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square P Value Pr > F
Model 4 37.4689223 9.3672306 4.32 0.0064
Error 33 71.5047619 2.1668110

Corrected Total 37 108.9736842

R-Square Coeff Var Root MSE clusters per shoot Mean
0.343835 36.55970 1.472009 4.026316

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 37.4689223] 9.36723058 4.32 0.0064
year=2005 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 4.81238095 1.20309524 1.17 0.3533
Error 20 20.54761905 l .0273 8095

Corrected Total 24 25.36000000

R-Square Coeff Var Root MSE clusters per shoot Mean
0.189763 24.36534 1.013598 4.160000

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 4.81238095 1.20309524 1.17 0.3533
year=2005 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 4.0892857] 1.02232143 1.07 0.3904
Error 27 25.78571429 0.95502646

Corrected Total 31 29.87500000

R-Square Coeff Var Root MSE clusters per shoot Mean
0.136880 31.91035 0.977255 3.062500

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 4.0892857] 1.02232143 1.07 0.3904

283

 

 

 

year=2006 vineyard=A

 

The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 0.72104247 0.18026062 0.96 0.4450
Error 32 6.03571429 0.18861607

Corrected Total 36 6.7 5675676

R-Square Coeff Var Root MSE clusters per shoot Mean
0.106714 40.17271 0.434300 1.081081

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 0.72104247 0.18026062 0.96 0.4450
year=2006 vineyard=B
The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 5.07717570 1.26929392 7.44 0.0005
Error 24 4.09523810 0.17063492

Corrected Total 28 9.17241379

R-Square Coeff Var Root MSE clusters per shoot Mean
0.553527 26.62070 0.413080 1.551724

 

 

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 5.07717570 1.26929392 7.44 0.0005
year=2006 vineyard=C
The AN OVA Procedure
Dependent Variable: clusters per shoot
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 4 6.36587302 1.59146825 5.19 0.0022
Error 35 10.73412698 0.30668934

Corrected Total 39 17. 10000000

R-Square Coeff Var Root MSE clusters per shoot Mean
0.372273 33.56336 0.553795 1.650000

Source DF Anova 88 Mean Square F Value Pr > F
D88 4 6.36587302 1.59146825 5.19 0.0022

284

 

APPENDIX D

ESTIMATED EXPENSES FOR A MATURE CONCORD VINEYARD

285

Table D. 1. Estimated expenses for a mature Vitis labrusca ‘Concord’ vineyard.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Activity Labor Equipment Other Total
hours cost 1 hours cost costs costs costs
Pruning $151.25 $151.25
Chopping vines 0.40 $2.50 $2.50
40 hp tractor 0.40 $4.00 0.40 $3.60 $7.60
Trellis maintenance $0.00 $143. $143.45
45
40 hp tractor 3.00 $30.00 1.00 $9.00 $39.00
Trailer 1.00 $4.10 $4.10
Tying (piece work) $42.35 $37.5 $79.85
0
Fertilizer (spring) 0.50 $0.96 $65.0 Urea @ $65.96
0 200#lA
40 hp tractor 1.00 $10.00 1.00 $9.00 $22.0 0-0-60 @ $41.00
0 100#/A
Lime (1/5 acerage) 0.20 $2.00 0.20 $1.80 $8.00 $11.80
Weed Control (1/3 area) $0.00 $0.00
40 hp tractor 1.38 $13.75 1.38 $12.38 $45.6 $71.80
8
Weed sprayer 1.38 $4.14 $4.14
Suckering 2.00 $20.00 $0.00 $20.00
Tillage $0.00 $0.00
60 hp tractor 1.00 $10.00 1.00 $13.77 $23.77
Rototiller 1 .00 $6.50 $6.50
Tillage 3x $0.00
40 hp tractor 1.50 $15.00 1.50 $13.51 $28.51
Disc/drag 1 .50 $5.23 $5.23
Cover Crop 0.50 $5.00 0.50 $0.96 $2.00 $7.96
40 hp tractor $0.00 0.50 $4.50 $4.50
Spraying (6x) $0.00 $0.00
60 hp tractor 5.50 $55.00 5.50 $75.74 $256. $386.99
26
Sprayer 5.50 $66.77 $66.77
Pickup operation $0.00 $47.50 $47.50
Miscellaneous $0.00 $50.00 $50.00
Real Estate Tax $0.00 $15.00 $15.00
Management 6.00 $0.00 $90.00 $90.00
(hours)
Total 17.88 $118.75 19.75 $203.4 $303. $202.50 $1,375.
8 93 18

286

APPENDIX E

AN OVA OUTPUT FROM CHAPTER 5

287

 

F
I

 

growth rate
The AN OVA Procedure

Dependent Variable: growth

Sum of
Source DF Squares Mean Square F Value Pr > F
Model 1 3358.58420 3358.58420 142.11 <.0001
Error 382 9028.35069 23.63443

Corrected Total 383 12386.93490

R-Square Coefi‘ Var Root MSE growth Mean
0.271139 29.00149 4.861525 16.76302

 

Source DF Anova SS Mean Square P Value Pr > F
genus 1 3358.584201 3358.584201 142.11 <.0001

greenhouse pathogenicity screen

The GLM Procedure
Dependent Variable: lesion
Sum of
Source DF Squares Mean Square F Value Pr > F
Model 233 5549.91031 23.81936 4.25 <.0001
Error 1078 6041.15067 5.60404

Corrected Total 1311 1 1591 .06098

R-Square Coeff Var Root MSE lesion Mean
0.478810 54.33655 2.367284 4.356707

Source DF Type I SS Mean Square F Value Pr > F
exp 1 8.380677 8.3 80677 1.50 0.2216
trial 1 70.203765 70.203765 12.53 0.0004
isolate 56 4525.749871 80.816962 14.42 <.0001
block 3 4.273369 1.424456 0.25 0.8584
trial*isolate 4 73.908745 18.477186 3.30 0.0107

isolate*block 168 867.393879 5.163059 0.92 0.7463

288

 

Source DF Type III SS Mean Square F Value Pr > F

exp 1 2.912046 2.912046 0.52 0.4712
trial 1 1.775149 1.775149 0.32 0.5737
isolate 56 4503.801551 80.425028 14.35 <.0001
block 3 8.409257 2.803086 0.50 0.6822
trial*isolate 4 60.615087 15.153772 2.70 0.0292

isolate*block 168 867.393879 5.163059 0.92 0.7463

Vineyard pathogenicity screen with mycelium
The AN OVA Procedure

Dependent Variable: lesion

Sum of
Source DF Squares Mean Square F Value Pr > F
Model 6 330.3428571 55.0571429 33.22 <.0001
Error 28 46.4000000 1.6571429

Corrected Total 34 3767428571

R-Square Coeff Var Root MSE lesion Mean
0.876839 15.17021 1.287301 8.485714

Source DF Anova SS Mean Square P Value Pr > F
isolate 6 330.3428571 55.0571429 33.22 <.0001

Vineyard pathogenicity screen with Ascospores

The AN OVA Procedure

Dependent Variable: lesion

Sum of
Source DF Squares Mean Square F Value Pr > F
Model 10 980.181818 98.018182 10.99 <.0001
Error 33 294.250000 8.916667

Corrected Total 43 1274.431818

R-Square Coeff Var Root MSE lesion Mean
0.769113 10.43586 2.986079 28.61364

289

DF Anova SS

10

Source
isolate

Cultivar pathogenicity screen

The GLM Procedure

Dependent Variable: lesion

980.1818182

Mean Square
98.0181818

F Value Pr > F

10.99

<.0001

Sum of

Source DF Squares Mean Square F Value Pr > F
Model 80 6807.51678 85.09396 26.16 <.0001
Error 1040 3383.19509 3.25307
Corrected Total 1120 10190.71186

R-Square Coeff Var Root MSE lesion Mean

0.668012 44.71177 1.803628 4.033898
Source DF Type I SS Mean Square P Value Pr > F
trial 1 173 .087909 173.087909 53.21 <.0001
cultivar 6 586.962363 97.827061 30.07 <.0001
isolate 7 5096.081576 728.01 1654 223.79 <.0001
block 3 15.763573 5.254524 1.62 0.1841
cultivar*isolate 42 882.3 53279 21.00841 1 6.46 <.0001
isolate*block 21 53.268076 2.536575 0.78 0.7471
Source DF Type III SS Mean Square F Value Pr > F
trial 1 100.550130 100.550130 30.91 <.0001
cultivar 6 731 .542939 121 .923 823 37.48 <.0001
isolate 7 4500.779381 642.968483 197.65 <.0001
block 3 19.653563 6.551188 2.01 0.1103
cultivar*isolate 42 871.391710 20.747422 6.38 <.0001
isolate*block 21 53.268076 2.536575 0.78 0.7471

290

APPENDIX F

DNA SEQUENCES FROM CHAPTER 5

291

Final Compiled Sequences ITS

E30 E. lata (California)
GenBank DQ00006934
555 bp

agggatcattacagagttacctaactccaaacccatgtgaactttacctatgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagctcgctgcaggcctacccgccggtggacacttaaactcttgtttttttagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcga
gcgtcatttcgaccttcaagccctagctgcttggtgttgggagcctatctccggatagctcctgaaaagcattggcggagtcgcgg
tgaccccaagcgtagtaattcttctcgctttaggtgtgtcacggctgacgtcttgccgttaaacccccaattttttaaatggttgacctc
ggatcaggtaggaatacccgctgaacttaa

E31 E. lata (California)
GenBank DQ00006933
553 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctatgttgcctcggcggggaagcctacccggtacctacc

ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacacttaaactcttgttttttagtgatt

atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcga
gcgtcatttcgaccttcaagccctagctgcttggtgttgggagcctatctccggatagctcctcaaaagcattggcggagtcgcgg
tgaccccaagcgtagtaattcttctcgctttaggtgtgtcacggctgacgtcttgccgttaaacccccaattttttaaatggttgacctc
ggatcaggtaggaatacccgctgaacttaa

E38 E. lata (California)
GenBank DQ006935
553 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctatgttgcctcggcggggaagcctacccggtacctacc

ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacacttaaactcttgttttttagtgatt

atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcga
gcgtcatttcgaccttcaagccctagctgcttggtgttgggagcctatctccggatagctcctcaaaagcattggcggagtcgcgg
tgaccccaagcgtagtaattcttctcgctttaggtgtgtcacggctgacgtcttgccgttaaacccccaattttttaaatggttgacctc
ggatcaggtaggaatacccgctgaacttaa

292

 

EL1 E. lata (Lawton, MI)
541 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
cgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgttttttagtgattatctgagtgttt
atacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaa
ttgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcaa
ccctcaagccctagttgcttggtgttgggagcttatcttcggataactcgccaaaagcatcggcggagtcgcggtggccccaagc

gtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacctcggatcaggtag
gaatacccgctgaacttaa

EL5 E. lata (Lawton, MI)
541 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
cgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgttttttagtgattatctgagtgttt
atacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaa
ttgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggcatgcctgttcgagcgtcatttca
accctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcggtggccccaag

catagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacctcggatcaggta
ggaatacccgctgaacttaa

EL19 E. lata (Lawton, MI)
541 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
cgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgttttttagtgattatctgagtgttt
atacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaa
ttgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcaa
ccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcggtggccccaagc

gtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacctcggatcaggtag
gaatacccgctgaacttaa

EL45 E. lata (Lawton, MI)
541 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
cgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgttttttagtgattatctgagtgttt
atacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaa
ttgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggaatgcctgttcgagcgtcatttca
accctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcggtggccccaag

cgtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccttattttctagtggttgacctcggatcaggta
ggaatacccgctgaacttaa

293

EL55 E. lata (Lawton, MI)
540 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
cgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgttttttagtgattatctgagtgttt
atacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaa
ttgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcaa
ccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcggtggccccaagc

gtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctatttctagtggttgacctcggatcaggtag
gaatacccgctgaacttaa

EL130 E. lata (Lawton, MI)
552 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgtttttcagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggcatgcctgttcga
gcgtcatttcaaccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcgg

tggccccaagcgtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacctc
ggatcaggtaggaatacccgctgaacttaa

EL184 E. lata (Lawton, MI)
552 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgtttttcagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagaaggcatgcctgttcga
gcgtcatttcaaccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcgg
tggccccaagcgtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacctc
ggatcaggtaggaatacccgctgaacttaa

EL198 E. lata (Lawton, MI)
552 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgtttttcagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcga
gcgtcatttcaaccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcgg

tggccccaagcgtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacctc
ggatcaggtaggaatacccgctgaacttaa

294

EL302 E. lata (Baroda, MI)
552 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgtttttcagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattccagagggcatgcctgttcg

agcgtcatttcaaccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcg

gtggccccaagcgtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacc
tcggatcaggtaggaatacccgctgaacttaa

EL316 E. lata (Baroda, MI)
552 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgttttttagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggcatgcctgttcga
gcgtcatttcaaccctcaagccctagttgcttggtgttgggagcctatcttcggataactccccaaaagcatcggcggagtcgcgg
tggccccaagcgtagtaatttttcctcgcttaggtgtgccacggcctacgtcctgccgtaaaaccccctatcttctaatggttgacct
cggatcaggtaggaatacccgctgaacttaa

EL69 E. lata (Lawton, MI)
552 bp

agggatcattacagagttacctaactccaaacccatgtgaacttacctacgttgcctcggcggggaagcctacccggtacctacc
ctgtagctacccgggagcgagctaccctgtagcccgctgcaggcctacccgccggtggacgcctaaactcttgtttttcagtgatt
atctgagtgtttatacttaataagttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgata
agtaatgtgaattgcagaattcagtgaatcatcgaatctttgaacgcacattgcgcccattagtattccagagggcatgcctgttcg

agcgtcatttcaaccctcaagccctagttgcttggtgttgggagcttatcttcggataactccccaaaagcatcggcggagtcgcg

gtggccccaagcgtagtaatttttcctcgcttaggtgtgctacggtcgacgtcctgccgtaaaaccccctattttctagtggttgacc
tcggatcaggtaggaatacccgctgaacttaa

EVA1 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa
attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggcatgcctgttcgagcgtcatttcgaccatcaagc
cctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgt
agtaattcttctcgctttagtagtgttaacgctggcatctggccactaaacccctaatttttataggtttgacctcggatcaggtagga
atacccgctgaacttaa

295

EVA2 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataacttctcaaatatattggcggagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EVA5 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa
attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggcatgcctgttcgagcgtcatttcgaccatcaagc
cctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcagagtcgcggagaccctaagcgt
agtaattcttctcgctttagtagtgttaacgctggcgtctagccactaaacccctaatttttataggtttgacctcggatcaggtagga
atacccgctgaacttaa

EVA6 E1. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagagggcatgcctgttcgagcgtcatttcgaccatcaagc

cctatttgcttagcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgt
agtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataagtttgacctcggatcaggtaggaa
tacccgctgaacttaa

EVA9 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

296

 

EVA12 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcagagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcgtctagccactaaacccctaattttataggttttgacctcggatcaggtaggaat
acccgctgaacttaa

EV66 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctggccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV70 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactccttaaatatattggcggagtcgcggagaccctaagcgta

gtaattcttctcgctttagcagtgttaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV71 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgtcaacgctggcatctggccactaaacccctaattttataggttttgacctcggatcaggtaggaata
cccgctgaacttaa

297

 

‘

 

EV72 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag

ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgtcaacgctggcatctggccactaaactcttaatttttataggtttgacctcggatcaggtaggaata

cccgctgaacttaa

EV73 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgtcaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV74 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattgtagtgggcatgcctgttcgagcgtcatttcgaccatcaagc

cctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgt
agtaattcttctcgctttagtagtgtcaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaa
tacccgctgaacttaa

EV76 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaat

acccgctgaacttaa

298

EV77 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgtcaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV79 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaat

acccgctgaacttaa

EV81 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgcgggcatctggccactaaacccttaatttttataggtttgacctcggatcaggtagcaat
acccgctgaacttaa

EV85 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgccaacgcaggcatctggccactaaacctttaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

299

EV89 El. vitis (Lawton, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgtcaacgctggcatctggccactaaacccctaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV216 El. vitis (Lawton, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcagagtcgcggagaccctaagcgta

gtaattcttctcgetttagtagtgttaacgctggcgtctagccactaaacccctaattttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV229 El. vitis (Baroda, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa
attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcattttaaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcggagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacctttaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV231 El. vitis (Schoolcrafi, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgtag
taattcttctcgctttagtagtgttaacgctggcatctggccactaaacctctaattttataggtttgacctcggatcaggtaggaatac
ccgctgaacttaa

300

EV232 El. vitis (Schoolcraft, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgtag
taattcttctcgctttagtagtgttaacgctggcatctggccactaaacctctaattttataggtttgacctcggatcaggtaggaatac
ccgctgaacttaa

EV238 El. vitis (Schoolcraft, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctacccggtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa
attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctggccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV257 El. vitis (Schoolcraft, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgtag
taattcttctcgctttagtagtgttaacgctggcatctggccactaaacctctaattttataggtttgacctcggatcaggtaggaatac
ccgctgaacttaa

EV258 El. vitis (Schoolcraft, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag

ctaccctgtagttaccctgtaaggactacccgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaatag

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttgacgccggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaa
tacccgctgaacttaa

301

EV268 El. vitis (Schoolcraft, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcagagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcgtctagccactaaacccctaattttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV270 El. vitis (Schoolcrafi, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcagagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcgtctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV279 El. vitis (Schoolcraft, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcgacggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcaattgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV290 El. vitis (Schoolcraft, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgta

gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaattttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

302

 

 

 

EV293 El. vitis (Schoolcraft, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacttttaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV295 El. vitis (Schoolcraft, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV300 El. vitis (Schoolcraft, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaattttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV325 El. vitis (Baroda, MI)
541 bp

agggateattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag

ctaccctgtagttaccctgtaaggactactcgtcgacggaccatcaaactctgtttttctatgaaacttctgagtgttttaacttaataa
attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc

agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc

ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcggagtcgcggagaccctgagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctggccactaaacccctaattttataggtttgacctcggatcaggtaggaat

acccgctgaacttaa

303

EV329 El. vitis (Baroda, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgttgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaat
taaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca

gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaattttataggtttgacctcggatcaggtaggaatac
ccgctgaacttaa

EV330 El. vitis (Baroda, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgrtgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgttgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaat
taaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaattttataggtttgacctcggatcaggtaggaatac
cc gctgaacttaa

EV331 El. vitis (Baroda, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgttgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaat
taaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca

gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV334 El. vitis (Baroda, MI)
542 bp

agggatcattaaagagtagtttttataacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaa
ttaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca
gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcggagtcgcggagaccctaagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

304

 

EV336 El. vitis (Baroda, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgttgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaat
taaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca

gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagegtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV337 El. vitis (Baroda, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagttaccctgtaaggactactcgttgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataaat
taaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattca

gtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagccc
tatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgtag
taattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

EV339 El. vitis (Baroda, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa
attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcattttaaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcggagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

EV344 El. vitis (Schoolcraft, MI)
542 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcgggataactcctcaaatatattggcggagtcgcggagaccctgagcgta
gtaattcttctcgctttagtagtgttaacgccggcatctagccactaaacccctaatttttataggtttgacctcggatcaggtaggaat
acccgctgaacttaa

305

EV346 El. vitis (Baroda, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcggcggggaaactacccggtag
ctaccctgtagctaccctgtaaggaatactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcagttgcggggtaactcctcaaatatattggcagagtcgcggagaccctaagcgta
gtaattcttctcgctttagtagtgttaacgctggcgtctagccactaaacccctaattttataggtttgacctcggatcaggtaggaat

acccgctgaacttaa

EV348 El. vitis (Baroda, MI)
541 bp

agggatcattaaagagtagtttttacaacaactccaaacccatgtgaacttacctatgttgcctcgacggggaaactacccggtag

ctaccctgtagctaccctgtaaggactactcgtcgacggaccattaaactctgtttttctatgaaacttctgagtgttttaacttaataa

attaaaactttcaacaacggatctcttggttctggcatcgatgaagaacgcagcgaaatgcgataagtaatgtgaattgcagaattc
agtgaatcatcgaatctttgaacgcacattgcgcccattagtattctagtgggcatgcctgttcgagcgtcatttcgaccatcaagcc
ctatttgcttggcgttgggagcttaccctgcaattgcgggataactcctcaaatatattggcggagtcgcggagaccctaagcgta

gtaattcttctcgctttagtagtgttaacgctggcatctagccactaaacccctaattttataggtttgacctcggatcaggtaggaata
cccgctgaacttaa

306

Final Compiled Sequences B-tubulin

E30 E. lata (California)
GenBank DQ006991
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatttttaattacccttagataacctcgtgtgat
tgtgtgatttaagctaaccacccaatagctacaacggcacctccgagctccagcttgagcgcattaacgtctacttcaacgaggta
agcaattgttttattagctggcaatgctaactcgccgctgtttggctttgctaacaatgattttttcttttccaactgtaggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggatgccgtccgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtctttggacaatccggtgctggcaacaactgg

E31 E. lata (California)
GenBank DQ006990
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatttttaattacccttagataacctcgtgtgat
tgtgtgatttaagctaaccacccaatagctacaatggcacctccgagctccagcttgagcgcattaacgtctacttcaacgaggta
agcaattgttttattagctggcaatgctaactcgccgctgtttggctttgctaacaatgattttttcttttccaactgtaggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggatgccgtccgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtctttggacaatccggtgctggcaacaactgg

E38 E. lata (California)
GenBank DQ006992
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatttttaattacccttagataacctcgtgtgat
tgtgtgatttaagctaaccacccaatagctacaatggcacctccgagctccagcttgagcgcattaacgtctacttcaacgaggta
agcaattgttttattagctggcaatgctaactcgccgctgtttggctttgctaacaatgattttttcttttccaactgtaggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggatgccgtccgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtctttggacaatccggtgctggcaacaactgg

EL1 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttactgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

307

EL5 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgcgtga
ttgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggta
agcaattgttccattagctgataatactaactagccactgtttgaccttacaaacaatgatgttttttcttcttaattataggcgtccggc
aacaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttcc
gtcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL19 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca
acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL45 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctactatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca
acaagtatgttcctcgtgccgttcttgtcgatctcaagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL55 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca
acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL62 E. lata (Erie, PA)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctacgagctacggcttgagcgcattaaagtatacttcagagaggta
agcaattattgcattggctgatgatagtaactaaccactgtttgacctggctgacaatgatgttttttcttcttatttataaacatccggc

aacaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttcc
gtcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

308

EL69 El. vitis (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca
acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL130 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtt'tgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL184 E. lata (Lawton, MI)
400 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagccttttcc
gtcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL198 E. lata (Lawton, MI)
399 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tgtgaataaacgcttaccgcccaatagctacaatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgatgttttttcttcttaattataggcgtccggca
acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL302 E. lata (Baroda, MI)
399 bp

tggcaaaccatatctggcgagcatggtctcgacagcaactgtgtgtatgtaaccgatctttatataccctctgataacctcgtgtgat
tgtgaatcaacgcttaccgcccaatagctactatggcacctccgaactccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattgttccattagctgataatactaactagccactgtttgaccttgctaacaatgataatttttcttcttaattataggcgtccggca

acaagtatgttcatcatgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

309

 

EL 316 E. lata (Baroda, MI)
400 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tttgtgaattaagcttaccgcccaatagctacaatggcacctccgagctccagcttgagcgcattaacgtctacttcaacgaggtaa
gcaattattccattagctgatgatactaactagccactgtttgaccttgctgacaatgatgttttttcttcttaattataggcgtccggca

acaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttccg
tcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EL318 E. lata (Baroda, MI)
400 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtaaccgatctttatttaccctctgataacctcgtgtgat
tttgtgaattaagcttaccgcccaatagctacaaaggcacctccgagctccagcttgagcgcattaacgtctacttcaacgaggta
agcaattattccattagctgatgatactaactagccactgtttgaccttgctgacaatgatgttttttcttcttaattataggcgtccggc
aacaagtatgttcctcgtgccgttcttgtcgatctcgagcccggtaccatggacgccgttcgtgctggtcccttcggtcagcttttcc
gtcccgacaacttcgtcttcggacaatccggtgctggcaacaactgg

EVA1 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcaccccatgctccgtgatgatagcccgttgctgatggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EVA2 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcaccccatgctccgtgatgatagcccgttgctgatggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtctgtgccggccccttcggtcagcttttccgccccaacaacttcg
tcttctgacagtccggtgccggcaacaactgg

EVA5 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctaatctaatatgtcgggg
aatccagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccaatcaccccatactccatgatgatagcccgttgctgacagcagtatataggcgtctggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggctccttcggtcagcttttccgccccgacaacttc
gtcttcggacagtccggtgccggcaacaactgg

310

EVA6 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcggga
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcacacccatactccatgatgatagcccgttgctgatggcagtatacaggcgtctggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccctttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EVA9 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatttgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtatgtggt
tttttgcaacaaaaaccccatgctcgatgatggcagaaggttgctgacagcagtatacaggcgtccggcaacaagtatgtccccc
gtgttgtcaacatcgaactcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt
cgtcttcggacagtacggtgccggcaacaactgg

EVA12 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatccgatatgtcggg

gaattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtatgtg

gtttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt

cgtcttcggacagtccggtgccggcaacaactgg

EV66 El. vitis (Lawton, MI)
374 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatgtcgggga
attcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtggtt
tctcgccccaatcaccccatacttcatgatgatagctcgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccgt
gccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttcg
tcttcggacagtccggtgccggcaacaactgg

EV70 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatctactagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtatgtggt
ttttcgccccaatcaccccattctccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttccccgt
gccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttcg
tcttcggacagtccggtgccggcaacaactgg

311

EV71 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctactatggtacctccgagctccagcttgagcgcatcaacgtctacctcaacgaggtacgtggc
tttttcgccccaatcatcccatactccatgatgatagcccgttgctgacggctgtatacaggcgtctggcaacaagaatgttctccgt
gccgtcctcgtcgatctcgagcccggtaccatgcatgccgtccgtgccggccccttcggtcagcttttctgccccgacaacttcgt

cttcggacagtccggtgccggtaacaactgg

EV72 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
ttttacgccccaatcacccccatactctatgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt
cgtcttcggacagtccggcgccggcaacaactgg

EV73 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggtacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt
tttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV74 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggcctcgacagcaacggtgtgtatgtgatagaccttctccgcgtctgatctgatgtcgggg
aattcagctcacgcatctaatagctacaatgacacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg

gtttctcgccccaatcaccccatacttcatgatgatagctcgttgctgacggcagtatacaggcgtctggcaacaagtatgttctcc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgctgtccgtgcagaccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV76 El. vitis (Lawton, MI)
374 bp

tggcaaaccatctctggcgagcacggcctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatgtcgggga
attcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtggtt
tctcgccccaatcaccccatacttcatgatgatagctcgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccgt
gccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgcagaccccttcggtcagcttttccgccccgacaacttcg
tcttcggacagtccggtgccggcaacaactgg

312

EV77 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggtacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt
tttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV79 El. vitis (Lawton, MI)
372 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcaattc
agctcaccatctactagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtatgtggtttttcg
ccccaatcaccccattctccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttccccgtgccg
tcctcgtcgatctcgagcccagtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttcgtcttc

ggacagtccggtgccggcaacaactgg

EV81 El. vitis (Lawton, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatctaatagctacaatggcacctccgagctccagctctagagcatcaacgtctacttcaacgaggtacgtgga
ttctcgccccaatcacctcatacttcatgatgatagctcgttgctgacagcagtatacaggcgtctggaaacaagtatgttccccgt
gccgtcctcgacgatctcgagcctggcaccatgaatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttcg
tcttcggacagtccggtgccggcaacaactgg

EV85 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggtacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt
tttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV89 El. vitis (Lawton, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
ttttacgccccaatcacccccatactctatgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt
cgtcttcggacagtccggcgccggcaacaactgg

313

EV229 El. vitis (Baroda, MI)
375 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatctactagctacaatggcacctccgagctccagctcgagcgcattaacgtctacttcaacgaggtacgtggt
ttctcgccccaatcaccccatactccatgatgatggcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV231 E1. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcaccccatactccatgatgatagcttgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccgt
gccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggccagcttttccgccccgacaacttcg
tcttcagacagtccggtgccggggacaactgg

EV232 El. vitis (Schoolcraft, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatatcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcacccccatactccatgatgatagtccgttgctgacggcagtatacaggcgtccggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt
cgtcttcggacagtccggtgccggcaacaactgg

EV238 El. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcacggcctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcggg

gaattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccaatcaccccatactccatgatgatcgcccgttgctgatggcagtatacaggcgtctggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgttcgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

314

EV239 El. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcacggcctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcggg
gaattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccaatcaccccatactccatgatgatcgcccgttgctgatggcagtatacaggcgtctggcaacaagtatgttcccc

gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgttcgtgccggccccttcggtcagcttttccgccccgacaacttc
gtcttcggacagtccggtgccggcaacaactgg

EV257 El. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctactatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtggt

ttttcgccccaatcaccccatactccatgatgatagcttgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccgt
gccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggccagcttttccgccccgacaacttcg
tcttcggacagtccggtgccggcaacaactgg

EV258 El. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatcctatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtggt

ttttcgccccaatcaccccatactccaagatgatagctcgttgctgacggcagtatacaggcgtctggcaacaagtatgtgccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc
gtcttcggacagtccggtgccggcaacaactgg

EV266 El. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgcactgatctgatatgtcggg
gaattcagctcaccatccaatagctacaatggcacctccgaactccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccgatcaccccatgctccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttccc
cgtgccgttctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagctcttccgccccgacaact
tcgtcttcggacagtccggtgccggcaacaactgg

EV268 El. vitis (Schoolcraft, MI)
377 bp

tggcaaaccatctctggcgagcacggcctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcggg
gaattcagctcaccatccaatagctaccatggtcacttccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccaatcaccccatactccatgatgatcgcccgttgctgatggcagtatacaggcgtctggcaacaaggatgttccc
cgtgccgtcctcgtcgatctcgagcccggtaccatggatgccgttcgtgccggccccttcgaacagcttttccgccccgacaact
tcgtcttcggacagtccggtgccggcaacaactgg

315

EV270 El. vitis (Schoolcraft, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagtaccttctccgcactgatctgatatgtcggg
gaattcagctcaccatccaatagctacaatggcacctccgaactccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccgatcaccccatgctccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttccc
cgtgccgttctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagctcttccgccccgacaact

tcgtcttcggacagtccggtgccggcaacaactgg

EV279 El. vitis (Schoolcrafi, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagtaccttctccgcactgatctgatatgtcggg
gaattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg

gtttttcgccccgatcaccccatactccctgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgtgccc
cgtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaact

tcgtcttcggacagtccggtgccggcaacaactgg

EV290 El. vitis (Schoolcraft, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatatcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcacccccatactccatgatgatagtccgttgctgacggcagtatacaggcgtccggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt

cgtcttcggacagtccggtgccggcaacaactgg

EV293 El. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcg'ggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttcccc

gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagctcttccgccccgacaactt

cgtcttcggacagtccggtgccggcaacaactgg

EV295 E1. vitis (Schoolcraft, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttcccc

gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagctcttccgccccgacaactt
cgtcttcggacagtccggtgccggcaacaactgg

316

EV300 El. vitis (Schoolcraft, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
caattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccaatcaccccatactccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttccc

cgtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagctcttccgccccgacaact

tcgtcttcggacagtccggtgccggcaacaactgg

EV325 El. vitis (Baroda, MI)
377 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctcttatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
ttcttccgccccggtcaccccatactccatgatgaaagcccgttgctgactgcagtatacaggcgtctggcaacaagtatgttccc
cgtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaact
tcgtcttcggacagtccggtgccggcaacaactgg

EV329 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt
ttttcgccccaatcaccccatactccatgatggtagtcggttgctgacagcagtatacaggcgtccggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc
gtcttcggacagtccggtgccggcaacaactgg

EV330 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt

ttttcgccccaatcaccccatactccatgatggtagtcggttgctgacagcagtatacaggcgtccggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV331 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt

ttttcgccccaatcaccccatactccatgatggtagtcggttgctgacagcagtatacaggcgtccggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc
gtcttcggacagtccggtgccggcaacaactgg

317

EV334 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt

ttttcgccccaatcaccccatactccatgatggtagtcggttgctgacagcagtatacaggcgtccggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV336 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt
ttttcgccccaatcaccccatactccatgatggtagtcggttgctgacagcagtatacaggcgtccggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV337 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatggaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagcttgagcgcatcaacgtctacttcaacgaggtacgtggt

ttttcgccccaatcaccccatactccatgatggtagtcggttgctgacagcagtatacaggcgtccggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV339 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatctactagctacaatggcacctccgagctccagctcgagcgcattaacgtctacttcaacgaggtacgtggt

ttctcgccccaatcaccccatactccatgatgatggcccgttgctgacggcagtatacaggcgtctggcaacaagtatgttccccg
tgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaacttc

gtcttcggacagtccggtgccggcaacaactgg

EV344 El. vitis (Schoolcraft, MI)
377 bp

tggcaaaccatctctggcgagcatggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatatcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccaatcacccccatactccatgatgatagtccgttgctgacggcagtatacaggcgtccggcaacaagtatgttcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt

cgtcttcggacagtccggtgccggcaacaactgg

318

EV346 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgctctgatctgatatgtcgggg
aattcagctcaccatccaatagctacaatggcacctccgagctccagctcgagcgcatcaacgtctacttcaacgaggtacgtgg
tttttcgccccgatcaccccatactccctgatgatagcccgttgctgacggcagtatacaggcgtctggcaacaagtatgtgcccc
gtgccgtcctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagcttttccgccccgacaactt

cgtcttcggacagtccggtgccggcaacaactgg

EV348 El. vitis (Baroda, MI)
376 bp

tggcaaaccatctctggcgagcacggtctcgacagcaacggtgtgtatgtgatagaccttctccgcactgatctgatatgtcggg
gaattcagctcaccatccaatagctacaatggcacctccgaactccagctcgagcgcatcaacgtctacttcaacgaggtacgtg
gtttttcgccccgatcaccccatgctccatgatgatagcccgttgctgacggcagtatacaggcgtccggcaacaagtatgttccc
cgtgccgttctcgtcgatctcgagcccggtaccatggatgccgtccgtgccggccccttcggtcagctcttccgccccgacaact

tcgtcttcggacagtccggtgccggcaacaactgg

319

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