c06-09-0565(5) horvath

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.

1206 CROP SCIENCE, VOL. 47, MAY–JUNE 2007

RESEARCH

Dollar spot is caused by the pathogen Sclerotinia homoeocarpa F.T. Bennett (Bennett, 1937) and is a common pathogen

throughout the world. In North America, the disease is an impor-tant pathogen destructive to both cool- and warm-season grasses (Smith et al., 1989; Smiley et al., 2005; Vargas, 2005). The disease is a major problem for most golf courses in Michigan, with epidemics beginning in June and continuing through late September. Dollar spot generally occurs during periods of warm days, cool nights, and heavy dewfall with leaf wetness periods conducive to infection (Warren et al., 1974; Smith et al., 1989; Vargas, 2005). Symptoms of the disease begin as small, hourglass-shaped lesions on leaves that can expand to blight the entire plant. The disease can also blight large areas of turf as a result of coalescing disease foci, caus-ing extensive damage if left untreated. Diseased turf impairs the playing surface by creating depressions that aff ect golf ball roll on putting greens and leaves areas of bare soil where weed species can germinate and grow (Smith et al., 1989; Vargas, 2005).

The biology of S. homoeocarpa has not been studied exten-sively due to the relative ease with which this disease can be con-trolled with fungicides. Two reports from Great Britain suggest

Geostatistical Analysis of Dollar Spot Epidemics Occurring on a Mixed Sward of Creeping Bentgrass and Annual Bluegrass

B. J. Horvath,* A. N. Kravchenko, G. P. Robertson, and J. M. Vargas, Jr.

ABSTRACT

Dollar spot (Sclerotinia homoeocarpa F.T. Ben-

nett) causes one of the most important diseases

faced by golf course superintendents through-

out the USA. Fungicides are effective, but fun-

gicide resistance and regulation have restricted

their use. Site-specifi c application allows more

judicious use of fungicides, but the spatial het-

erogeneity of dollar spot incidence has been

unknown. An intensive study was designed to

determine the spatial distribution of dollar spot

incidence using geostatistical analysis. Dollar

spot foci were manually counted every 2 to 3

d in 200 quadrats in 2000, and in 888 quadrats

in 2001 and 2002. Variograms showed spatial

aggregation of dollar spot foci on a small scale,

and remained stable throughout the growing

season despite increasing disease incidence.

The range parameter of the variograms was

stable within a season, with values ranging

between 2.5 and 9.5, 1.3 and 1.9, and 1.2 and

2.2 m in 2000, 2001, and 2002, respectively.

Kriged maps of dollar spot incidence also show

a stable pattern of spatial aggregation remains

throughout the growing season. The locations

of dollar spot clusters changed between sea-

sons, but the variograms from each season

were similar, further suggesting stability of the

spatial structure. It appears that limited second-

ary dispersal of S. homoeocarpa occurs during

epidemics, as evidenced by the stable vario-

grams observed throughout the three growing

seasons. The underlying factors involved in the

observed stability are unknown.

B.J. Horvath, Dep. of Plant Pathology, Physiology, and Weed Science,

Virginia Polytechnic Inst. and State Univ., Blacksburg, VA 24061; J.M.

Vargas, Jr., Dep. of Plant Pathology, Michigan State Univ., East Lan-

sing, MI 48824; A.N. Kravchenko and G.P. Robertson, Dep. of Crop

and Soil Sciences, Michigan State Univ., East Lansing, MI 48824. This

work was supported by a Project GREEEN grant (GR00-037) from the

Mich. Agric. Exp. Stn., East Lansing. Received 5 Sept. 2006. *Corre-

sponding author ([email protected]).

Abbreviations: PSV, proportion of structural variance; VCG, vegeta-

tive compatibility groups.

Published in Crop Sci. 47:1206–1216 (2007).doi: 10.2135/cropsci2006.09.0565© Crop Science Society of America677 S. Segoe Rd., Madison, WI 53711 USA

All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Permission for printing and for reprinting the material contained herein has been obtained by the publisher.

Published online May 31, 2007 Published online May 31, 2007

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.

CROP SCIENCE, VOL. 47, MAY–JUNE 2007 WWW.CROPS.ORG 1207

that the pathogen is capable of producing sexual spores, asexual spores, or both ( Jackson, 1973; Baldwin and New-ell, 1992). Hsiang and Mahuku (1998) reported that a S. homoeocarpa population from Ontario exhibited random amplifi cation of polymorphic DNA (RAPD) and inter-genic spacer–restriction fragment length polymorphism (IGS-RFLP) fi ngerprints that were in linkage disequi-librium indicating genetic recombination, but no fruit-ing bodies or spores were observed in their study. Other genetic diversity studies show S. homoeocarpa as a predomi-nantly clonal pathogen (Raina et al., 1997; Powell and Vargas, 2001; Viji et al., 2004). Little research exists on inoculum sources, but a few reports contend that stroma-tized, infected tissue is the primary inoculum source for S. homoeocarpa (Fenstermacher, 1980; Smith et al., 1989; Vargas, 2005). Spread of this disease is widely believed (Fenstermacher, 1980; Smith et al., 1989; Smiley et al., 2005; Vargas, 2005) to be the result of mechanical move-ment of the pathogen on infected leaf blades during mow-ing operations and human transport of infected clippings on shoes, golf balls, etc., but Williams et al. (1996) showed no signifi cant diff erences in the amount of dollar spot in plots where clippings were collected vs. plots where clip-pings were left in place.

Management of dollar spot is usually accomplished by N fertilization and fungicide applications. Increased N fertility leads to a reduction in dollar spot disease sever-ity (Markland et al., 1969; Smith et al., 1989; Williams et al., 1996; Smiley et al., 2005; Vargas, 2005), but work by Couch and Bloom (1960) reported that the suscepti-bility of Poa pratensis L. was actually increased in higher fertility treatments in the greenhouse. They suggested, however, that this eff ect would probably be masked in the fi eld because symptoms would not appear before the rapidly growing, infected leaf blades were mowed. Many fungicides are available to control dollar spot, but the presence of fungicide-resistant S. homoeocarpa populations to demethylation-inhibiting (Golembiewski et al., 1995), benzimidazole (Detweiler et al., 1983), and dicarboxi-mide (Detweiler et al., 1983) fungicides have forced many golf course superintendents to consider using the contact fungicide chlorothalonil (2,4,5,6-tetrachloro-1,3-ben-zenedicarbonitrile) to manage dollar spot. The USEPA has banned the use of chlorothalonil on home lawns and placed restrictions on its commercial use (USEPA, 1999). Because of these restrictions, more judicious use of chloro-thalonil by golf course superintendents is needed. Making site-specifi c applications of N fertilizers and fungicides to manage dollar spot is desirable, but the spatial variability of dollar spot disease incidence is unknown.

Traditional statistical techniques used in the analysis of epidemiological data require adherence to assumptions such as the independence of samples and normality of data. It makes intuitive sense, however, that plants located

closer in space to a diseased plant have a higher probabil-ity of becoming infected than plants located farther away. Important epidemiological questions arise from spatially explicit descriptions of disease incidence and severity.

Originally developed for the study of geological phenomena, geostatistics has found wide application in a number of fi elds including phytopathology (Lecoustre et al., 1989; Johnson et al., 1991; Munkvold et al., 1993; Stein et al., 1994; Dandurand et al., 1997; Larkin et al., 1995; Xiao et al., 1997; Rekah et al., 1999; Gavassoni et al., 2001; Luo et al., 2001; Savary et al., 2001; Paulitz et al., 2003). These tools have also been used to study the physicochemical properties of soils (Rossi et al., 1992; Robertson and Gross, 1995; Goovaerts, 1998), plant dis-tributions and ecology (Oliver and Webster, 1986; Rossi et al., 1992), and microbial (Wollum and Cassel, 1984; Dandurand et al., 1995; Robertson et al., 1997; Franklin and Mills, 2003) and nematode (Todd and Tisserat, 1990; Webster and Boag, 1992; Robertson and Freckman, 1995) distributions. Geostatistics is not bound by the require-ments of stationarity, which is the property of a data set in which the mean and variance do not change with loca-tion, but rather seeks to explain the variation between samples as a function of separation distance (Goovaerts, 1997; Isaaks and Srivastava, 1989). Spatial dependence, or semivariance, is detected by measuring the variation among samples separated by the same distance (lag) using the equation

( )2( , )

1( )

2 ( )ij

i ji j h h

h v vN h =

γ = −∑

where γ is the semivariance, h is the average separation distance between sample pairs, N is the number of sample pairs, and v

i and v

j are the ith and jth data values at sepa-

ration distance h (Goovaerts, 1997; Isaaks and Srivastava, 1989). Results are plotted as a variogram that is displayed as semivariance vs. distance. Generally, as the distance between sample pairs increases, semivariance will also increase. Three key parameters describe the variogram: the nugget, sill, and range. The nugget is the disconti-nuity associated with experimental error and variability at scales smaller than the smallest sample interval, and is theoretically nil when h = 0, but in practice is defi ned as the y intercept of the variogram (Isaaks and Srivastava, 1989). The range, which is the distance where semivari-ance does not increase with increasing separation distance, defi nes the boundary where samples are no longer spatially dependent. The sill is the semivariance value at the range, and is a measure of the overall sample variance. If the sill value is less than the total variance, then there may be spatial structure at scales greater than those sampled. The proportion of structural variance (PSV) can be expressed as a proportion of the total variance that is spatially struc-tured. When this value approaches 1, a large proportion of

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.

1208 WWW.CROPS.ORG CROP SCIENCE, VOL. 47, MAY–JUNE 2007

factors involved in an epidemic (Lecoustre et al., 1989; Gavassoni et al., 2001). Ideally, examining multiple loca-tions with large sample sizes would enable the detection of spatial structure and the frequency with which any spatial structure occurs. Limited resources require a compromise, however, between the number of locations sampled and the sample frequency within a location. The impact of such compromises on the resulting geostatistical analyses is not trivial; diff erent conclusions about spatial structure can arise depending on the scale studied.

Knowledge about the heterogeneity of dollar spot dis-ease incidence is essential for the development of eff ec-tive disease management strategies. The objectives of this study were to determine the spatial structure of dollar spot disease incidence, and evaluate changes in the structure on a putting green with time using geostatistical analysis.

MATERIALS AND METHODS

SamplingOur study site was established in 2000 at the Robert Hancock

Turfgrass Research Center in East Lansing, MI (42°43 4́8˝ N,

84°28´35˝ W) on a 10-yr-old, mixed sward of creeping bent-

grass (Agrostis palustris Huds.) and annual bluegrass (Poa annua L.)

on an Owosso–Marlette sandy loam (fi ne-loamy, mixed, semi-

active, mesic Typic Hapludalf–Oxyaquic Glossudalf ). An area

of 0.017 ha (9.1 × 18.3 m) was maintained as a putting green

and mowed in diff erent radial directions 5 d wk−1 at a mow-

ing height of 0.4 cm. Clippings were collected throughout the

study. Nitrogen was applied at a rate of 24.4 kg N ha−1 mo −1 dur-

ing the growing season from April to September of each year.

No fungicide applications were made to the site throughout the

duration of our study. We divided the site into 200 0.09-m2

quadrats on a regular grid at 0.9-m intervals in 2000 (Fig. 1). In

2001 22 additional 0.09-m2 quadrats were established arbitrarily

at random locations within the study site, and all 222 quadrats

were subdivided into four 0.023-m2 subquadrats (Fig. 2). These

additional locations were added and all quadrats subdivided to

increase the number of sample locations at small separation dis-

tances. A 0.09-m2 wooden frame divided into quadrants was

placed on the centroid of each quadrat to delineate the four sub-

quadrats at the time of sampling. Two corner positions at each

location were marked on the turf with paint to place the frame

in the same orientation at each sample time.

Data CollectionDollar spot foci were counted three times per week in 2000

and twice per week in 2001 and 2002 at each location in the

study area. Foci were counted when they became large enough

to observe (~1.2 cm diam.). Counting within the study area

was stopped when disease became so severe that there were too

many disease foci to count accurately due to coalescing foci.

Geostatistical AnalysisSpatial continuity was measured using the variogram, calcu-

lated for each date using the geostatistical software package

GS+ version 5.3b (Gamma Design Software, Plainwell, MI).

the total sample variance is spatially dependent. When the value approaches 0, spatial dependence is low. These tech-niques can also be used with nominal data such as geno-types or size classes (Goovaerts, 1997; Goovaerts, 1998). Geostatistics provides a powerful set of tools that can yield insight into the dynamic nature of plant disease.

Research in plant disease epidemiology using geo-statistics has matured during the last decade. Geostatis-tics has been used to study the spatial pattern of disease incidence and severity (Chellemi et al., 1988; Todd and Tisserat, 1990; Johnson et al., 1991; Stein et al., 1994; Lar-kin et al., 1995; Rekah et al., 1999; Savary et al., 2001; Paulitz et al., 2003) and inoculum levels (Munkvold et al., 1993; Dandurand et al., 1997; Xiao et al., 1997; Luo et al., 2001, Charest et al., 2002), and to identify possible

Figure 1. Schematic layout of 200 regular grid sampling areas for

2000 with black squares ( ) denoting the center of each 0.09-m2

sampling area at the Robert Hancock Turfgrass Research Center,

East Lansing, MI.

Figure 2. Schematic layout of 800 regular grid and 88 randomly

located sampling areas for 2001 and 2002 with black squares ( )

denoting the center of each 0.023-m2 sampling area at the Robert

Hancock Turfgrass Research Center, East Lansing, MI.

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


General geostatistical methods that were used in

this study are described by Isaaks and Srivastava

(1989). Lag distance and lag interval (separation

distance class) were defi ned iteratively based on

the distance and interval that yielded a smooth,

well-behaved variogram. In 2000, a 12.2-m lag

distance with a 1.5-m lag interval was used, and

in 2001 and 2002, a 6.1-m lag distance with a

0.61-m lag interval was used due to the greater

sample density. The resulting variograms were

automatically fi t using the GS+ software, which

evaluated the goodness of fi t based on least-

squares weighting to the linear, exponential,

spherical, and Gaussian models. The fi ts were

then adjusted visually to avoid overextrapola-

tion of the variogram model at small separation

distances. A spherical model for variograms from

two dates in each year, representing early and late

epidemic development, were used to estimate

dollar spot incidence using kriging, an interpola-

tion technique. Kriged estimates of dollar spot

incidence were generated for a grid of 0.019-m2

with a search radius that was 70% of the range

for a particular date. Because dollar spots were

counted in an area and not at a discrete point,

estimates at four grid nodes were averaged to

produce a block estimate, and maps of dollar spot

incidence were generated using the block estimates.

RESULTS

Sampling and Data CollectionDisease was observed and the number of dollar spot foci counted at each location beginning in early June of each year and continuing until 25 Aug. 2000, 9 Sept. 2001, and 13 Sept. 2002. Overall disease progress curves of the number of dollar spot foci per square meter and the area under the disease progress curve values show that the epi-demic in 2002 was the most severe, followed by the epi-demics in 2000 and 2001 (Fig. 3). Each progress curve shows an early-season disease period that is not as severe as the late-season period beginning in early August and continuing into September.

Geostatistical AnalysisVariograms calculated for each date in 2000 to 2002 show dollar spot counts were correlated across small separation distances, indicating spatial structure (Table 1, Fig. 4, 5, and 6). A spherical model best described the spatial struc-ture present throughout most of the study (Table 1). Three dates, 6 June 2000, 9 June 2000, and 12 July 2002, showed no spatial structure (nugget eff ect). The yearly average PSV was 43% in 2000 and 2001, and 41% in 2002. The PSV generally ranged between 30 and 60% of the total variation for each date a variogram was calculated (Fig. 7). Several dates at the end of 2000 had PSVs that increased beyond the 30 to 60% range. Range values for each var-

iogram in 2000 increased from 2.5 to 9.5 m through the early part of the epidemic, but several dates at the end of 2000 had ranges that increased from 13 to 31 m (Table 1). Ranges for 2001 and 2002 remained stable for the entire year, with values between 1.3 and 1.9 m in 2001 and 1.2 and 2.2 m in 2002 (Table 1). For illustration purposes, six representative variograms were selected from each year to account for disease progress during diff erent phases of the epidemic (Fig. 4–6). Kriged maps of dollar spot incidence from the beginning and end of the epidemic in each year showed that dollar spot begins as clusters of individual infection foci that remain visible even as disease incidence increases late in the epidemic (Fig. 8). Anisotropy, or a change in spatial variability that changes with direction, was examined, and no anisotropic trends were apparent (data not shown).

DISCUSSIONThis study quantifi ed the spatial pattern dynamics of dollar spot incidence on a golf putting green during a 3-yr period using geostatistical analysis. To our knowledge, this is the fi rst report using geostatistics to study the spatial pattern of dollar spot incidence. While our study was performed at a single location for 3 yr, raising questions of replication and repeatability, Campbell and Madden (1990) suggested that this is always a concern with spatial pattern studies and that true replication is usually unfeasible, if not impossible, given the constraints of natural environments and limited resources. We designed an intensive sampling scheme that enabled us to capture spatial information at a variety of

Figure 3. Overall disease progress of dollar spot epidemics on a mixed sward of

creeping bentgrass and annual bluegrass from 2000 to 2002 as measured by

the average number of dollar spot foci per square meter. Area under the disease

progress curve (AUDPC) values calculated from dollar spot foci counts are shown in

upper right of graph.

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


separation distances from 0.15 to >19 m because there was no previous research to indicate an appropriate sample scale. Samples could have been allocated to more locations with fewer samples per location, but the lack of knowledge about the spatial structure of dollar spot incidence meant that there was a reasonable risk of not detecting spatial struc-ture due to inappropriate sampling. Therefore, we chose to focus our eff orts on detecting spatial structure, if it was present, and designed our study accordingly. The results of this study may or may not apply to other locations, as the spatial structure of disease incidence is probably infl uenced

by many, currently unknown factors. The development of site-specifi c management practices in the future will rely on the knowledge and background of the underlying spatial structure of a disease. Our study does provide an estimation of the spatial structure of S. homoeocarpa, and suggests test-able hypotheses suitable for future research that may yield results that can be used in the development of site-specifi c management of dollar spot.

Disease progress curves in all 3 yr were similar and showed that dollar spot epidemics in Michigan begin in early to mid-June with a minor outbreak and then

Table 1. Summary of model type, parameters (nugget, sill, and range), proportion of structural variance (PSV), and coeffi cient

of determination (R2) of variograms based on geostatistical analysis of dollar spot epidemics on a mixed sward of creeping

bentgrass and annual bluegrass in 2000, 2001, and 2002.

Year DateModel type

Nugget (C

o)

Sill (Co

+ C)Range

(a)PSV [C/(C

o

+ C)]R2

m

2000 6 June Linear 0.82 0.82 n/a† n/a‡ 0.507

9 June Linear 0.13 0.13 n/a n/a 0.209

12 June Spherical 0.07 0.11 2.50 0.386 0.438

15 June Spherical 0.57 0.83 3.80 0.313 0.288

19 June Spherical 0.49 0.80 4.00 0.387 0.583

22 June Spherical 2.00 3.77 4.10 0.469 0.789

26 June Spherical 2.70 4.27 4.10 0.368 0.832

28 June Spherical 4.10 7.00 5.00 0.414 0.934

3 July Spherical 2.50 3.90 6.00 0.359 0.331

5 July Spherical 2.50 3.81 3.90 0.343 0.857

7 July Spherical 8.60 13.50 5.00 0.363 0.562

10 July Spherical 10.00 14.00 6.00 0.286 0.356

12 July Spherical 12.50 25.50 6.00 0.510 0.648

14 July Spherical 8.90 16.10 6.20 0.447 0.539

17 July Spherical 13.40 26.00 6.20 0.485 0.895

19 July Spherical 17.80 35.50 7.20 0.499 0.825

21 July Spherical 5.20 8.40 7.50 0.381 0.798

24 July Spherical 1.60 2.70 8.40 0.407 0.943

26 July Spherical 2.00 3.80 7.50 0.474 0.840

28 July Spherical 2.40 3.45 7.20 0.304 0.673

31 July Spherical 13.00 21.00 6.50 0.381 0.981

2 Aug. Spherical 34.30 60.80 6.50 0.436 0.963

4 Aug. Spherical 51.70 109.10 7.01 0.526 0.972

7 Aug. Spherical 18.80 47.29 9.50 0.602 0.970

9 Aug. Spherical 34.10 72.87 9.04 0.532 0.994

14 Aug. Spherical 38.20 99.26 13.28 0.615 0.994

17 Aug. Spherical 47.70 135.60 13.97 0.648 0.997

21 Aug. Spherical 55.40 181.40 15.27 0.695 0.998

25 Aug. Spherical 44.40 265.40 30.99 0.833 0.985

2001 15 June Spherical 0.12 0.24 1.30 0.500 0.962

19 June Spherical 0.28 0.54 1.50 0.479 0.970

22 June Spherical 0.26 0.50 1.50 0.480 0.969

25 June Spherical 0.26 0.45 1.40 0.418 0.976

28 June Spherical 0.13 0.29 1.40 0.545 0.990

2 July Spherical 0.12 0.20 1.50 0.406 0.939

5 July Spherical 0.11 0.19 1.50 0.407 0.949

9 July Spherical 0.38 0.64 1.50 0.408 0.960

12 July Spherical 0.28 0.49 1.50 0.427 0.936

16 July Spherical 0.29 0.49 1.50 0.409 0.936

Year DateModel type

Nugget (C

o)

Sill (Co

+ C)Range

(a)PSV [C/(C

o

+ C)]R2

m

19 July Spherical 0.29 0.57 1.50 0.489 0.931

23 July Spherical 0.45 0.85 1.60 0.472 0.935

26 July Spherical 0.47 0.86 1.60 0.453 0.935

31 July Spherical 0.44 0.84 1.90 0.476 0.965

2 Aug. Spherical 0.67 1.05 1.70 0.364 0.874

6 Aug. Spherical 0.54 0.97 1.90 0.443 0.957

9 Aug. Spherical 0.46 0.70 1.90 0.343 0.956

13 Aug. Spherical 0.25 0.38 1.50 0.340 0.945

17 Aug. Spherical 0.22 0.38 1.50 0.421 0.942

20 Aug. Spherical 1.20 1.88 1.60 0.361 0.955

24 Aug. Spherical 2.70 4.68 1.70 0.423 0.892

27 Aug. Spherical 2.40 3.90 1.50 0.385 0.880

30 Aug. Spherical 3.00 5.42 1.60 0.447 0.928

4 Sept. Spherical 2.10 3.31 1.60 0.366 0.906

9 Sept. Spherical 1.75 2.88 1.80 0.392 0.876

2002 24 June Spherical 0.24 0.38 1.20 0.363 0.818

2 July Spherical 0.24 0.38 1.20 0.363 0.818

8 July Spherical 0.17 0.20 1.50 0.150 0.467

12 July Linear 0.41 0.41 n/a n/a 0.006

16 July Spherical 0.28 0.41 1.20 0.315 0.857

19 July Spherical 0.34 0.50 1.20 0.324 0.855

23 July Spherical 1.10 1.68 1.20 0.344 0.889

25 July Spherical 1.55 2.38 1.60 0.349 0.918

30 July Spherical 2.45 4.40 1.50 0.443 0.953

2 Aug. Spherical 2.60 4.40 1.50 0.409 0.963

6 Aug. Spherical 2.35 4.40 1.50 0.466 0.970

9 Aug. Spherical 2.38 4.30 1.50 0.447 0.974

13 Aug. Spherical 2.60 4.40 1.50 0.409 0.978

16 Aug. Spherical 2.50 4.35 1.50 0.425 0.976

20 Aug. Spherical 2.80 5.98 1.50 0.532 0.985

23 Aug. Spherical 4.20 8.10 1.50 0.482 0.933

27 Aug. Spherical 4.80 8.70 1.50 0.448 0.952

30 Aug. Spherical 4.90 9.50 1.50 0.484 0.964

3 Sept. Spherical 4.40 8.20 1.50 0.463 0.976

10 Sept. Spherical 4.20 6.67 1.60 0.370 0.915

13 Sept. Spherical 3.80 7.70 2.20 0.506 0.925

†Range not applicable to nugget effect variograms.

‡PSV not applicable to nugget effect variograms.

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


become much more severe during the months of August and September (Fig. 3). Our data agree with others who have shown the late-season phase of the epidemic is most damaging to a turf area (Fenstermacher, 1980; Smith et al., 1989; Powell and Vargas, 2001; Vargas, 2005). Dollar spot incidence declined during July, presumably because of hotter and more humid conditions that were less con-ducive for disease development.

Spatial structure was detected at small lag distances throughout epidemics in all 3 yr studied. The range parameters for each date do not measure the physical loca-tion of disease incidence, but they do show the average cluster size of dollar spot foci, or the distance across which counts of dollar spot foci are correlated. Therefore, if the range parameters change with time, then changes in the overall spatial structure would also be occurring. The range values estimated by the spherical variogram model

for each date were less stable in 2000, possibly due to the rapid increase in disease incidence later in the season (Fig. 3) and the lack of small-scale sampling. After the sampling design was changed to a high-density design, the range values for variograms from 2001 and 2002 were stable for the entire year (Table 1, Fig. 7), with values varying by 0.6 m in 2001 and 1.0 m in 2002. Spatial structure was not detected (nugget eff ect) on three dates (Table 1) that had very low disease incidences, indicating that the sam-pling design was not capable of detecting spatial struc-ture below a certain disease incidence threshold. Stable PSV values (Fig. 7) throughout the study and disease inci-dence maps (Fig. 8) generated by interpolation (kriging) of disease incidence data support the variogram data and illustrate the stability of the spatial structure with time. The maps show that dollar spot epidemics develop as small clusters of infection foci, and while the physical location

Figure 4. Variograms of dollar spot epidemic caused by S. homoeocarpa on a mixed sward of creeping bentgrass and annual bluegrass

on six dates in 2000 selected to be representative of changes in disease progress (Fig. 3) throughout the growing season.

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


of the clusters is diff erent each year, spatial structure that is established at the onset of the epidemic remains pres-ent as disease incidence increases throughout the season. The overall stability of these values with time suggests that the spatial structure of disease incidence is governed by underlying factors that are similarly stable.

Several working hypotheses can be generated that support our fi nding of a stable spatial structure for dollar spot disease incidence. Our study was not able to deter-mine which of these factors infl uence the observed spatial structure, but we can suggest possible infl uences that are consistent with our data. One possibility is that the ability of the pathogen to spread is limited. Others include varia-tion in the virulence of the pathogen, or variation in the susceptibility of the host plants.

Spread of a pathogen can be accomplished either by the dissemination of inoculum through spore produc-

tion or other means, or through the direct movement of infected materials from one place to another. The method of dissemination used by S. homoeocarpa for dispersal is unclear. The pathogen has been shown to produce asco-spores and conidia in laboratory experiments and in the fi eld in the United Kingdom (Bennett, 1937; Jackson, 1973; Baldwin and Newell, 1992), but spore production in the fi eld, if it occurs, has not been observed outside of the U.K. Four of eight populations in a fi eld experiment (Hsiang and Mahuku, 1998) in Ontario showed signifi cant linkage disequilibrium, indicating that these populations were not random mating. Asexual reproduction through mycelial fragments seems to be the major inoculum source for this pathogen, and fi ts the observed linkage disequilib-rium data; however, linkage disequilibrium values in the other four populations indicated that it was possible that sexual recombination was taking place. If S. homoeocarpa



Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


inoculum were dispersed (via spores or other means) in the present study, the resultant polycyclic disease devel-opment would alter the spatial structure. More specifi -cally, the range values of calculated variograms would be expected to change during the polycyclic phase, even if dispersal were limited. Previous studies in the Venturia inaequalis pathosystem have shown that dispersal of V. inae-qualis ascospores is sharply reduced beyond 5 to 6 m from a source (Kaplan, 1986), and that the eff ective dispersal radius is about 20 m (Hsiang et al., 2000). Our data, from 888 locations, shows range values were stable in 2001 and 2002, with values generally between 1 and 2 m and vary-ing by just 0.6 m in 2001 and 1.0 m in 2002 after sampling was increased. The stability and small variance associated with the range parameters support the possibility that lim-ited spore dispersal and movement take place.

Another possible method of spread would be through direct movement of the pathogen. Several reports have ascribed dispersal of S. homoeocarpa to the movement of myce-lial fragments on diseased tissue via human and mechanical transport (Fenstermacher, 1980; Smith et al., 1989; Vargas, 2005). We are confi dent that dispersal of infected tissue is possible; it has been used as an inoculation technique for establishing dollar spot in experimental plots at our research center. We would expect an increase in the variogram range with time as clusters expanded radially if mowing practices were a factor in the dispersal of S. homoeocarpa. While the study area was mowed in diff erent radial directions fi ve times per week and clippings were collected, however, range values for variograms in 2001 and 2002 fl uctuated by <1 m. Other geostatistical studies of plant pathosystems have generally observed changes in spatial structure as epidemics progress



Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


(Lecoustre et al., 1989; Stein et al., 1994; Larkin et al., 1995; Rekah et al., 1999; Gavassoni et al., 2001). For example, spa-tial patterns of cyst and egg populations of Heterodera glycines showed that no-till and ridge tillage treatments increased aggregation of nematodes, and conventional and reduced tillage decreased aggregation as spatial structure changed with time depending on the tillage practice used (Gavassoni et al., 2001). Stein et al. (1994) detected spatial structure in epidemics of Peronospora parasitica on cabbage (Brassica oleracea L.) early in the season, especially during optimal periods for epidemic development, but the spatial structure diminished as disease severity increased. The structure returned again as a result of lessening disease severity and plant recovery (Stein et al., 1994). Spatial patterns of pathogens on tomato (Sola-num lycopersicum L.; Rekah et al., 1999), and bell pepper (Cap-sicum annuum L. var. annuum; Larkin et al., 1995) also show increasing spatial structure with time. Our results diff er from these studies because we clearly show a nonrandom spatial structure organized on a small scale that changes little as dis-ease incidence increases. While we cannot rule out the pos-sibility that some short-distance movement takes place, our results suggest that, at a minimum, the mowing equipment used in this study was eff ective at collecting and removing infected tissue from the study area and was not a major factor in the movement of S. homoeocarpa. Williams et al. (1996) also showed that removing or leaving clippings was not a signifi -cant factor in the development of dollar spot. Future research could test this possibility by measuring the ability of mow-ing equipment to move isolates of S. homoeocarpa downrange from an inoculated point source.

Direct movement of the pathogen is another source of spread that could be involved in the observed spatial pattern. Spatial analysis of Fusarium oxysporum f. sp. radicis-lycopersici epi-demics in tomato and work on soybean [Glycine max (L.) Merr.] sudden death syndrome showed that movement of the pathogens along roots was eff ective as a short-distance dispersal mechanism (Rekah et al., 1999; Luo et al., 2001). Epidemics of rice (Oryza sativa L.) sheath blight, caused by Rhizoctonia solani, showed a large nugget eff ect for spatial structure occurring at separation distances greater than four hills (~1 m), and suggested that processes occurring at the single-hill level (~0.2 m), where individual tillers could come into con-tact with neighboring diseased or healthy tillers, is crucial for development of spatial structure in epidemics (Savary et al., 2001). Direct contact of healthy plants by infected plants in our study area would take place across a very small scale because of the very small size of the turfgrass plants on a putting surface. It is not known if these methods of spread occur in the dollar spot pathosystem, but changes occurring at these scales could easily

accumulate during the growing season and would result in a small variation in the range values similar to the variation we observed in 2001 and 2002 (0.6 and 1.0 m, respectively). These possibilities could be tested in the future by evaluating S. homoeocarpa’s ability to move from plant to plant, through thatch, and through soil.

The susceptibility of the host and virulence of the pathogen may also play a role in spatial structuring of dol-lar spot epidemics. The predominant grasses found on golf course putting surfaces are creeping bentgrass and annual bluegrass. Both of these grasses are nonuniform in their susceptibilities to S. homoeocarpa (Cole et al., 1969; Vincelli and Doney, 1997; Viji et al., 2004). Creeping bentgrass is a synthetic cultivar, meaning that each seed is genetically distinct, and results in a range of variation in susceptibility and resistance to dollar spot even within a particular culti-var. Annual bluegrass is a noncultivated grass that invades putting surfaces as a weed, and also is known to be geneti-cally variable (Vargas, 2005). The area we studied was at least 10 yr old and was a mixed sward of creeping bentgrass and annual bluegrass. With time, the competitiveness of each seedling could govern the particular genotypes that established a site. These successful genotypes would then be more or less susceptible to dollar spot, and this eff ect would be observed as a mosaic of disease incidence with a spatial structure corresponding to the spatial structure of the grasses. Variability within the pathogen could also play a role in the spatial structure we observed. Powell and Vargas (2001) showed seasonal variation in the vegetative compatibility groups (VCGs) of S. homoeocarpa present in

Figure 7. Proportion of structural variance (PSV) (C/(Co + C), where C = sill value-nugget

variance, and Co = nugget variance) displayed over time for spherical variogram

models based on geostatistical analysis of dollar spot epidemics on a mixed sward

of creeping bentgrass and annual bluegrass from 2000 to 2002. Dotted lines in the

plot area represent the area where structural variance accounted for between 30 and

60% of the total variation present.

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


a turf area. Viji et al. (2004) also showed diff erences in pathogenicity of diff erent VCGs. Thus, it is possible that variation in the pathogen could lead to changes in the spatial structure of epidemics, depending on the makeup of the S. homoeocarpa population present. Since previously infested tissue is believed to be the primary inoculum source for dollar spot (Fenstermacher, 1980; Smith et al., 1989; Vargas, 2005), diff erences in the incidence of disease could also result in changes to the spatial structure.

Overall, we identifi ed that dollar spot epidemics display a relatively stable spatial structure organized on a small scale that is relatively unaff ected as disease incidence increases. While the factors underlying the observed spatial structure are unknown, it is possible that the factors responsible for the structure would be similarly stable. We suggest testable hypotheses that future studies can determine: what infl uences, such as mowing, spore dispersal, local spread of mycelium through contacts with diseased tissue, variation in suscep-tibility of hosts, and diff erences in the pathogenicity of S. homoeocarpa isolates, aff ect spatial structuring that is present. It is likely that many factors contribute to the spatial struc-ture of S. homoeocarpa, and future research should include an evaluation of other geographic locations to determine the generality of the spatial structure that we observed, and test-ing of the proposed hypotheses to determine those factors responsible for any spatial structure that is present.

ReferencesBaldwin, N.A., and A.J. Newell. 1992. Field production of fer-

tile apothecia by Sclerotinia homeocarpa in Festuca turf. J. Sports

Turf Res. Inst. 68:73–76.

Bennett, F.T. 1937. Dollar spot of turf and its causal organism Scler-

otinia homoeocarpa n. sp. Ann. Appl. Biol. 24:236–257.

Campbell, C.L., and L.V. Madden. 1990. Introduction to plant

disease epidemiology. John Wiley & Sons, Toronto.

Charest, J., M. Dewdney, T. Paulitz, V. Philion, and O. Carisse.

2002. Spatial distribution of Venturia inaequalis airborne asco-

spores in orchards. Phytopathology 92:769–779.

Chellemi, D.O., K.G. Rohrbach, R.S. Yost, and R.M. Sonoda.

1988. Analysis of the spatial pattern of plant pathogens and dis-

eased plants using geostatistics. Phytopathology 78:221–226.

Cole, H., J.M. Duich, L.B. Massie, and W.D. Barber. 1969. Infl u-

ence of fungus isolate and grass variety on Sclerotinia dollar

spot development. Crop Sci. 9:567–570.

Couch, H.B., and J.R. Bloom. 1960. Infl uence of turfgrasses: II.

Eff ect of nutrition, pH, and soil moisture on Sclerotinia dollar

spot. Phytopathology 50:761–763.

Dandurand, L.M., G.R. Knudsen, and D.J. Schotzko. 1995. Quan-

tifi cation of Pythium ultimum var. sporangiiferum zoospore encyst-

ment patterns using geostatistics. Phytopathology 85:186–190.

Dandurand, L.M., D.J. Schotzko, and G.R. Knudsen. 1997. Spatial

patterns of rhizoplane populations of Pseudomonas fl uorescens.

Appl. Environ. Microbiol. 63:3211–3217.

Detweiler, A.R., J.M. Vargas, Jr., and T.K. Danneberger. 1983.

Resistance of Sclerotinia homoeocarpa to iprodione and beno-

myl. Plant Dis. 67:627–630.

Fenstermacher, J.M. 1980. Certain features of dollar spot dis-

Figure 8. Spatial pattern of dollar spot disease incidence on (A) 28 June 2000, (B) 2 Aug. 2000, (C) 22 June 2001, (D) 30 Aug. 2001, (E) 2 July

2002, and (F) 30 Aug. 2002, representing dates early (A, C, and E) and late (B, D, and F) in disease epidemics of each year. Decile scale to

the right of each map represents block estimates of dollar spot foci 0.08−1 m−2. Maps generated by kriging of disease incidence data from

sampling locations shown in Fig. 1 and 2. Maps are oriented with north at top, and east and north coordinates on x and y axes.

Repro

duced

from

Cro

pS

cie

nce.

Publis

hed

by

Cro

pS

cie

nce

Socie

tyof

Am

erica.

All

copyrights

reserv

ed.


ease and its causal organism Sclerotinia homoeocarpa. p. 49–53.

In P.O. Larsen and B.G. Joyner (ed.) Advances in turfgrass

pathology. Harcourt, Brace, Jovanovich, Duluth, MN.

Franklin, R.B., and A.L. Mills. 2003. Multi-scale variation in spatial

heterogeneity for microbial community structure in an eastern

Virginia agricultural fi eld. FEMS Microbiol. Ecol. 44:335–346.

Gavassoni, W.L., G.L. Tylka, and G.P. Munkvold. 2001. Relation-

ships between tillage and spatial patterns of Heterodera glycines.

Phytopathology 91:534–545.

Golembiewski, R.C., J.M. Vargas, Jr., A.L. Jones, and A.R. Detwei-

ler. 1995. Detection of demethylation inhibitor (DMI) resistance

in Sclerotinia homoeocarpa populations. Plant Dis. 79:491–493.

Goovaerts, P. 1997. Geostatistics for natural resources evaluation.

Oxford Univ. Press, New York.

Goovaerts, P. 1998. Geostatistical tools for characterizing the spa-

tial variability of microbiological and physico-chemical soil

properties. Biol. Fertil. Soils 27:315–334.

Hsiang, T., X.L. Ma, and T. Zhou. 2000. Temporal and spatial

analyses of genetic diversity in Venturia inaequalis assessed by

RAPD markers. Can. J. Plant Pathol. 22:186.

Hsiang, T., and G.S. Mahuku. 1998. Genetic variation within and

between southern Ontario populations of Sclerotinia homeo-

carpa. Plant Pathol. 48:83–94.

Isaaks, E.H., and R.M. Srivastava. 1989. An introduction to applied

geostatistics. Oxford Univ. Press, New York.

Jackson, N. 1973. Apothecial production in Sclerotinia homeocarpa

F.T. Bennett. J. Sports Turf Res. Inst. 49:58–63.

Johnson, D.A., J.R. Alldredge, J.R. Allen, and R. Allwine.

1991. Spatial pattern of downy mildew in hop yards dur-

ing severe and mild disease epidemics. Phytopathology

81:1369–1374.

Kaplan, J.D. 1986. Dispersal gradients and deposition effi ciency of

Venturia inaequalis ascospores and their relationship to lesion

densities. Ph.D. diss. Univ. of New Hampshire, Durham.

Larkin, R.P., M.L. Gumpertz, and J.B. Ristaino. 1995. Geostatis-

tical analysis of Phytophthora epidemic development in com-

mercial bell pepper fi elds. Phytopathology 85:191–203.

Lecoustre, R., D. Fargette, C. Fauquet, and P. de Reff ye. 1989.

Analysis and mapping of the spatial spread of African cassava

mosaic virus using geostatistics and the kriging technique.


Luo, Y., S.K. Chong, and O. Myers. 2001. Spatio-temporal analy-

sis of soybean root colonization by Fusarium solani f.sp. glycines

in fi elds. Plant Dis. 85:303–310.

Markland, R.E., E.C. Roberts, and L.R. Frederick. 1969. Infl u-

ence of nitrogen fertilizers on Washington creeping bentgrass,

Agrostis palustris Huds.: II. Incidence of dollar spot, Sclerotinia

homoeocarpa infection. Agron. J. 61:701–705.

Munkvold, G.P., J.A. Duthie, and J.J. Marois. 1993. Spatial pat-

terns of grapevines with eutypa dieback in vineyards with or

without perithecia. Phytopathology 83:1440–1448.

Oliver, M.A., and R. Webster. 1986. Combining nested and linear

sampling for determining the scale and form of spatial varia-

tion of regionalized variables. Geogr. Anal. 18:227–242.

Paulitz, T.C., H. Zhang, and R.J. Cook. 2003. Spatial distribution of

Rhizoctonia oryzae and rhizoctonia root rot in direct-seeded cere-

als. Can. J. Plant Pathol. 25:295–303.

Powell, J.F., and J.M. Vargas, Jr. 2001. Vegetative compatibility

and seasonal variation among isolates of Sclerotinia homoeocarpa.

Plant Dis. 85:377–381.

Raina, K., N. Jackson, and J.M. Chandlee. 1997. Detection of

genetic variation in Sclerotinia homoeocarpa isolates using

RAPD analysis. Mycol. Res. 101:585–590.

Rekah, Y., D. Shtienberg, and J. Katan. 1999. Spatial distribution

and temporal development of fusarium crown and root rot of

tomato and pathogen dissemination in fi eld soil. Phytopathol-

ogy 89–831–839.

Robertson, G.P., and D.W. Freckman. 1995. The spatial distribu-

tion of nematode trophic groups across a cultivated ecosys-

tem. Ecology 76:1425–1432.

Robertson, G.P., and K.L. Gross. 1995. Assessing the heterogene-

ity of below-ground resources: Quantifying pattern and scale.

p. 237–253. In Plant exploitation of environmental heteroge-

neity. Academic Press, New York.

Robertson, G.P., K.M. Klingensmith, M.J. Klug, E.A. Paul, J.C.

Crum, and B.G. Ellis. 1997. Soil resources, microbial activ-

ity, and primary production across an agricultural ecosystem.

Ecol. Appl. 7:158–170.

Rossi, R.E., D.J. Mulla, A.G. Journel, and E.H. Franz. 1992. Geo-

statistical tools for modeling and interpreting ecological spa-

tial dependence. Ecol. Monogr. 62:279–314.

Savary, S., N.P. Castilla, and L. Willocquet. 2001. Analysis of the

spatiotemporal structure of rice sheath blight epidemics in a

farmer’s fi eld. Plant Pathol. 50:53–68.

Smith, J.D., N. Jackson, and A.R. Woolhouse. 1989. Fungal dis-

eases of amenity turf grasses. EF Spon, London.

Smiley, R.W., P.H. Dernoeden, and B.B. Clarke. 2005. Com-

pendium of turfgrass diseases. 3rd ed. Am. Phytopathol.

Soc., St. Paul, MN.

Stein, A., C.G. Kocks, J.C. Zadoks, H.D. Frinking, M.A. Ruissen,

and D.E. Myers. 1994. A geostatistical analysis of the spatio-

temporal development of downy mildew epidemics in cab-

bage. Phytopathology 84:1227–1239.

Todd, T.C., and N.A. Tisserat. 1990. Occurrence, spatial distribution,

and pathogenicity of some phytoparasitic nematodes on creeping

bentgrass putting greens in Kansas. Plant Dis. 74:660–663.

USEPA. 1999. Reregistration eligibility decision (RED): Chloro-

thalonil. EPA 738-R-99-004. USEPA, Washington, DC.

Vargas, J.M., Jr. 2005. Management of turfgrass diseases. 3rd ed.

John Wiley & Sons, Hoboken, NJ.

Viji, G., W. Uddin, N.R. O’Neill, S. Mischke, and J.A. Saun-

ders. 2004. Genetic diversity of Sclerotinia homoeocarpa

isolates from turfgrasses from various regions in North

America. Plant Dis. 88:1269–1276.

Vincelli, P., and J.C. Doney, Jr. 1997. Variation among creeping

bentgrass cultivars in recovery from epidemics of dollar spot.

Plant Dis. 81:99–102.

Warren, C.G., P. Sanders, and H. Cole. 1974. Sclerotinia homoeo-

carpa tolerance to benzimidazole confi guration fungicides.


Webster, R., and B. Boag. 1992. Geostatistical analysis of cyst

nematodes in soil. J. Soil Sci. 43:583–595.

Williams, D.W., A.J. Powell, Jr., P. Vincelli, and C.T. Dougherty.

1996. Dollar spot on bentgrass infl uenced by displacement of

leaf surface moisture, nitrogen, and clipping removal. Crop

Sci. 36:1304–1309.

Wollum, A.G., II, and D.K. Cassel. 1984. Spatial variability of Rhi-

zobium japonicum in two North Carolina soils. Soil Sci. Soc.

Am. J. 48:1082–1086.

Xiao, C.L., J.J. Hao, and K.V. Subbarao. 1997. Spatial patterns of

microsclerotinia of Verticillium dahliae in soil and verticillium

wilt of caulifl ower. Phytopathology 87:325–331.

c06-09-0565(5) horvath

Documents