AN ABSTRACT OF THE THESIS OF
Thomas G. Pfleeger for the degree of Doctor of Philosophy in Botany and Plant
Patho loay presented on May 1. 1998. Title: Effects of Single and Multiple Stressors on
Communities of Wheat and Wild Oats.
Abstract approved:
Redacted for Privacy
Christopher C. Mundt
Most plant toxicology tests developed in support of environmental laws use a
single stress applied to an individual plant. While tests using individual species or
stresses require fewer resources and are easier to interpret, they are under increasing
criticism for being unrealistic and missing important ecological interactions. The
objective of this research was to increase our understanding of how plants and plant
communities respond to a variety of stressors. Model plant communities of spring
wheat (Triticum aestivum) and wild oats (Avena fatua) were planted at three densities
and five proportions in the field. Puccinia recondita, the causal agent of wheat leaf rust,
was inoculated on half of the plots. Disease severity was estimated as percent of wheat
flag leaves covered by rust lesions. Plants were harvested at maturity and measured.
Seeding density rarely had a significant influence on rust severity, probably because
tiller density differed little as a result of compensation due to increased tillering at low
seeding densities. In contrast, increasing the proportion of wheat in mixtures with wild
oats consistently increased wheat leaf rust severity. There was no evidence to suggest
that wild oats acted as a barrier to inoculum movement. Wild oats' effect on wheat leaf
rust was probably through its competitive reduction of wheat tiller density. Both wheat
and wild oats seed weight decreased as the proportion of wild oats increased in
mixtures. This indicates that intraspecific competition was stronger in wild oats than
was intraspecific competition with wheat in these mixtures. Wild oats generally did not
respond to the presence of leaf rust on wheat, while wheat was negatively impacted.
Thus, there was little competitive advantage to wild oats when its competitor (wheat)
was diseased. A small subset of the field treatments was treated with ozone, because of
the limited space available in the open-top ozone exposure chambers. Wheat height and
aboveground biomass generally decreased with ozone exposure and with increasing
disease severity in both years, while total grain weight decreased significantly only with
disease and only in one year. There was no interaction between ozone and disease,
regardless of cultivar, density, or plant response variable measured. There was little
evidence that ozone exposure affected the severity of wheat leaf rust. In general, there
seemed to be a lack of interactions among the different stressors and the results varied
considerably depending on year and wheat cultivar.
Effects of Single and Multiple Stressors on Communities of
Wheat and Wild Oats
by
Thomas G. Pfleeger
A Thesis Submitted
to
Oregon State University
In Partial Fulfillment of
the requirements for the
degree of
Doctor of Philosophy
Presented May 1, 1998
Commencement June 1998
Doctor of Philosophy thesis of Thomas G. Pfleeger presented on May 1, 1998
Approved:
Redacted for Privacy
Major Professor, representing Botany and Plant Pathology
Redacted for Privacy
Chair of Department of otany and Plant Pa
gy
Redacted for Privacy
Dean of Gradu
School
I understand that my thesis will become part of the permanent collection of Oregon
State University libraries. My signature below authorizes release of my thesis to any
reader upon request.
Redacted for Privacy
Thoma
.
Pfleeger, Author
ACKNOWLEDGMENTS
I thank the many people who have helped me in attaining this goal. If I have
forgotten to mention your name, it is not because I did not value your contribution, but
rather over the years and many tasks to arrive here, I am sure I have inadvertently
forgotten someone. Thank you all.
This work was entirely supported by the U.S. Environmental Protection Agency.
Tom Murphy, Peter Beedlow, Bill Hogsett, Connie Burdick and Pam Taylor all
performed administrative duties to allow me this opportunity. Without their approvals,
guidance, and assistance, I could not have even started.
The crew at the Botany and Plant Pathology Farm (Lew Tate, Aaron Henderson,
and Nick Webster) helped with field preparation, irrigation, and many other tasks. Lew
Tate, especially kept me on my toes and the Farm will never be quite the same now that
he has retired. Russ Karow offered advice on wheat cultivar selection and methods to
grow wheat. Molly Hoffer and Laura Brophy helped with disease control at both the
Farm and in the open-top chambers.
The staff at the US EPA facility on 35th Street was very helpful. Peter Ziminski
and Craig Hendricks ran the open-top facility with only minor problems through three
growing seasons. Dick Field managed the greenhouse operation, but more importantly
drove the tractor carrying pots of wheat through an obstacle course to the chambers
without incident. Grady Neely helped with moving pots, inoculations and anything else
that came up. James Alexander helped by filling, planting and moving pots. Jack
O'Donnell did a variety of tasks. Steve Broich found the wild oats seed for me. Kent
Rodecap and Milt Plocher periodically added valuable advice and assistance. Florence
Medley planted hundreds of wheat seeds to help maintain the inoculum. Karen
Gunderson helped grow the wheat in the greenhouse used for raising the inoculum and
helped collect the resulting spores. Bill Hogsett, Dave Tingey, Jim Weber and Chris
Andersen all freely shared information they had gained from their professional
experience with plant responses to ozone. Henry Lee was especially helpful trying to
patiently teach me statistics. Craig Mc Farland, John Fletcher, and Joel McCrady were
always available for advice on a variety of subjects. Virginia Robinson polished off the
graphic and made true her claim that she could do anything. The 'competition' figures
were made possible by Stu Eide using SAS. Ray Shimabuku, who shared an office with
me for about ten years, lent me his bicycle, "The Rolls" without hesitation. It saved me
countless minutes getting to and from campus because it was in such ill repair, that I
never had to lock it. Unfortunately, no one else will get the thrill of riding it, as it as
been permanently retired due to safety concerns.
While I was taking classes, I shared an office with Norman Hommes, Cynthia
Page and Lisa Stein. They were all with Dan Arps' lab and besides being nice office
mates they, along with Dallice Mills, tried to teach me a little molecular biology.
I had help from several students. Bob Hayes worked with me for five summers
including one on this project where, in addition to field assistance, he set up many of the
spreadsheets that we used through out the project. High school students, Corrina Chase
and Emily Cook, assisted in the field sampling in 1996. A special thank you goes to
Michelle da Luz who started on the project after high school and stayed with it until it
was almost complete, three years later. Her positive attitude was extremely helpful,
especially when we were endlessly counting seeds.
John Laurence, Mary Powelson, Don Zobel, Ken Johnson, and Pat Muir
reviewed manuscripts which significantly improved them. Thank you for your time and
effort.
Thanks to my committee consisting of Peter McEvoy, Don Zobel, Pat Muir, Pat
Hayes, and Chris Mundt. Don was my major professor on my masters degree and
suggested I talk with Chris. Don's advice was sound as usual. Chris was great to work
with and always seemed available, even while traveling. His advice was helpful and
insightful. He was equally adept at driving a tractor as discussing the intricacies of
SAS, plant pathology and ecology.
I thank my family including my parents, my in-laws, the families of my brother
and sister, and especially, my wife, Carolyn and children, Amanda and Adam for their
encouragement, support and tolerance. This has been a long journey and they made
many sacrifices along the way. Thanks.
Contribution of Authors
Dr. Christopher C. Mundt was involved in the design, analysis, and writing of each
manuscript. Ms Michelle A. da Luz assisted in data collection and was involved in the
analysis and writing of two manuscripts.
TABLE OF CONTENTS
Page
I.
INTRODUCTION
1
Competition between wheat and other plants
2
Interactions between plant competition and ozone
5
Plant competition and disease
7
Interactions between ozone exposure, plant pathogens, and plants
. .
References
II.
10
14
WHEAT LEAF RUST SEVERITY AS AFFECTED BY PLANT DENSITY
AND SPECIES PROPORTION IN SIMPLE COMMUNITIES OF WHEAT
AND WILD OATS
III.
20
Abstract
21
Introduction
22
Materials and Methods
24
Results
26
Discussion
34
References
40
EFFECTS OF A PATHOGEN ON PLANT INTERACTIONS: WHEAT, WILD
OATS, AND WHEAT LEAF RUST
43
Abstract
44
Introduction
45
Materials and Methods
47
Results
49
Discussion
64
References
70
TABLE OF CONTENTS (continued)
IV.
V.
LACK OF INTERACTION BETWEEN OZONE AND WHEAT LEAF RUST
IN WHEAT SWARDS
74
Abstract
75
Introduction
76
Materials and Methods
77
Results
80
Discussion
88
References
100
CONCLUSIONS
105
BIBLIOGRAPHY
109
LIST OF FIGURES
II-1.
11-2.
11-3.
III-1.
111-2.
Plot of wheat culm number (flag leaf number) on seeding density of two
cultivars (Twin and Penawawa) in monocultures and wheat-wild oats
mixtures
27
Regression of combined wheat and wild oats culm number on combined
seeding density of wheat (cultivar Twin or Penawawa) in monocultures
and wheat / wild oats mixtures
28
Regression of wheat leaf rust severity on proportion of wheat culms of
two cultivars (Twin and Penawawa) in wheat monocultures and wheatwild oats mixtures at harvest.
31
The effects of seeding density and species proportion on A) wheat
aboveground biomass per planted seed or harvested culm, B) total weight
of wheat grain harvested per planted seed or harvested culm, C) mean
seed weight of wheat or wild oats, and D) wild oats aboveground
biomass per planted seed or harvested culm
54
The relative biomass of wheat (biomass per planted seed of wheat /
biomass per planted seed of wild oats) in relation to seeding density and
species proportion for A) 1995 and B) 1996
62
IV-1. Treatment means for stem height, aboveground biomass, and grain
weight of wheat plants exposed to two levels of ozone and/or three levels
of Puccinia recondita inoculation in open-top chambers
84
IV-2. Plots of the treatment means for stem height, aboveground biomass, and
grain weight of wheat plants exposed to two levels of ozone exposure
and/or three levels of Puccinia recondita inoculation in open-top
chambers.
87
IV-3. Disease progression curves for wheat leaf rust for the 1996 and 1997
disease and ozone experiments
89
LIST OF TABLES
Table
II-1.
11-2.
11-3.
III-1.
111-2.
111-3.
111-4.
Page
Analyses of variance for percent leaf rust severity of two spring wheat
cultivars grown at three planting densities in both pure stands and in
mixture with wild oats at four proportions
30
Analyses of variance by cultivar for percent leaf rust severity of two
spring wheat cultivars grown at three planting densities in both pure
stands and in mixture with wild oats at four proportions
32
Y-intercepts and slopes for regression of percent left rust severity on
density of wheat culms (culms/m2) in pure stands and in wheat-wild oats
mixtures grown at Corvallis, OR during 1995 and 1996
35
Means of two cultivars of wheat and of wild oats for aboveground
biomass per seed and per culm, total grain weight (wheat only) per seed
and culm and mean seed weight when grown in pure stands and mixtures
in the presence and absence of wheat leaf rust at different planting
densities
50
The means of wheat cv. Penawawa and wild oats for aboveground
biomass, total grain weight (wheat only), and mean seed weight for 1995
and 1996 when grown in pure stands and mixtures at different planting
densities
52
The means of wheat cv. Twin and wild oats for aboveground biomass,
total grain weight (wheat only), and mean seed weight for 1995 and 1996
when grown in pure stands and mixtures at different planting densities. . .
53
The P-values testing for the significance of components from linear and
nonlinear regression models using wheat and wild oat response variables.
58
IV-1. Analysis of variance for A) stem height, B) aboveground biomass, and
C) grain weight of two spring wheat cultivars grown in pure stands
81
IV-2. Area under the disease progression curve for leaf rust on spring wheat
cultivars Yecora Rojo and Twin in open-top chambers
91
I
DEDICATION
I dedicate this thesis to James H. Pfleeger, my older brother who died in 1994 from
cancer. Thanks for being my brother.
Effects of Single and Multiple Stressors on Communities of
Wheat and Wild Oats
I. INTRODUCTION
The plant toxicological tests routinely done for environmental protection are
with individual plants in a greenhouse or laboratory under optimal growing conditions
(US EPA 1991). This type of testing is replicable, cost effective, and the results are
easily interpreted in the context of chemical toxicity to tissues and individuals
(Kimball and Levine 1985). Unfortunately, they lack the realism to predict
community or ecosystem responses adequately (Pontasch et al. 1989; Taub 1997) and
are increasingly coming under criticism for not being able to evaluate the effects
(direct and indirect) of the toxicant on plant-to-plant, plant-pathogen or plant-
herbivore interactions (US EPA 1996). In addition, there is little information on how
the results from existing greenhouse plant tests vary under field conditions (Fletcher et
al. 1990).
A model system was developed comprised of spring wheat (Tritium aestivum),
wild oats (Avena fatua), and Puccinia recondida (the causal agent of wheat leaf rust).
This system was chosen because it consists of annual plants, allowing replication
within a relatively short time, and also because both plant species and the pathogen
are well understood. Wheat and wild oats were grown under varying densities and
proportions to simulate different levels of plant competition. Some of the artificial
plant communities were inoculated with Puccinia recondida to determine the effects of
a pathogen on plant competition. In addition, ozone was introduced into some wheat
communities to determine the effects of a toxicant on a host / pathogen interaction.
The following literature review gives a brief synopsis of the current literature
in each of four areas: 1) Competition between wheat and other plants, 2) Interactions
2
between plant competition and ozone, 3) Plant competition and disease, and 4)
Interactions between ozone exposure, plant pathogens, and plants.
Competition between wheat and other plants
Considerable research has been conducted on the effects of competition
between wheat (Triticum aestivum) and other plants. As one would expect, the focus
of this work has been on the effect that other plants, generally weeds, have had on the
performance of wheat. All of the studies reviewed used a replacement, additive, or
some modification of those two designs for assessing competition. No neighborhood
studies were encountered.
Wheat has been studied with a variety of species to determine its competitive
ability. Recently, the species chosen have been other grasses because most broadleaf
weeds of wheat can now be easily controlled with herbicides. Cousens et al. (1991)
found that wild oats (Avena fatua) was more competitive than wheat at two different
field sites in a replacement series experiment planted at a single density of 286 plants
ni2. In another replacement series experiment using pots that separated the root
and/or the shoot compartment by species, wheat was found to be at a disadvantage
because of root competition (Martin and Field 1988). Cudney et al. (1989), using a
replacement experiment in the field, concluded that wheat and wild oats where
equivalent in competitiveness (mutually exclusive). While wild oats and wheat were
mutually exclusive in replacement series experiments, wild oats was found to be more
aggressive than wheat in vital attributes such as relative yields and shoot dry weight
(Martin and Field 1987). When wheat was grown with Italian ryegrass (Lolium
multi, lorum) in a modified replacement series experiment, wheat was the better
competitor over a variety of densities and proportions (Rousch et al. 1989). Liebl and
Worsham (1987) found a similar relationship between Italian ryegrass and wheat using
an additive experimental design. Wheat was more competitive than either jointed
3
goatgrass (Aegilops cylindrica) or downey brome (Bromus tectorum) in a replacement
series (Fleming et al. 1988).
The outcome or magnitude of competitive interactions can change in relation to
the amount of resources (i.e., water, nutrients) available. With increased fertility,
wild oats became more competitive than wheat, causing wheat yield to decrease by 13
to 23 percent (Wimschneider et al. 1990). Wheat yield decreased further with
decreasing soil moisture. The addition of nitrogen fertilizer was related to a decline
in wheat yield with a corresponding increase in wild oat panicle density in a modified
replacement series experiment (Carson and Hill 1985b). Addition of fertilizer to an
additive experiment with Italian ryegrass and winter wheat resulted in a greater
response from Italian ryegrass (Liebl and Worsham 1987). Green foxtail (Setaria
viridis) was more competitive with wheat when fertility and moisture increased
(Petterson and Nalewaja 1992a). However, jointed wheatgrass became more
competitive than wheat under soil moisture stress (Fleming et al. 1988).
Experimental conditions such as time of emergence, plant density, spatial
arrangement, and even temperature can alter the outcome of competitive interactions.
When wild oats was sown five weeks prior to wheat, wheat yields were reduced by as
much as 45 percent (Winschneider et al. 1990). When green foxtail was seeded prior
to wheat, wheat responded with fewer tillers (Peterson and Nalewaja 1992b).
Densities of wild oats and bromegrass (Bromus diandrus) ranging from 200 to 600
plants m-2 all had the same effect on wheat sown at 150 plants nit (Milroy and
Goodchild 1987). However, when wild oat density increased from 3 to 300 plants m-2
wheat yield decreased (Carlson and Hill 1985a). Cudney et al. (1989) found that, as
wild oats density increased, wheat shoot weight decreased and wheat had fewer
inflorescences at harvest. As wheat density increased, wheat yield increased when
wild oat density remained constant (Carlson and Hill 1985a). Hume (1985) showed
that as wheat density increased from 180 to 430 plants n12 in weed infested fields,
yield and tillering per plant decreased, but total yield increased. Koscelny et al.
(1991) found that the effects of increasing densities of cheat (Bromus secalinus) on
4
wheat yield were reduced if wheat row spacing was decreased and wheat density
increased. However, spatial arrangement (cross seeding, i.e., drilling seed in two
passes with the second pass at 90 degrees from the first pass) was ineffective in
reducing wheat yield loss to Italian ryegrass (Appleby and Brewster 1992).
Varietal differences in both wheat and its weeds can cause differences in
competitive interactions. Challaiah et al. (1986) tested ten wheat cultivars against
downey brome and found that cultivar type was important in competitiveness. The
most competitive cultivar was 'Turkey' but it yields the least in monoculture. They
concluded that wheat height made the best correlation with reduction in downey
brome yield than canopy diameter or number of tillers. Balyan et al. (1991) came to a
similar conclusion with wheat and wild oats; the taller and heavier (biomass) cultivars
of wheat suppressed wild oats. This may be because, while wheat did not respond by
growing taller than the height of wild oats, wild oats responded to wheat height by
growing taller (Balyan et al. 1991). Wild oat plasticity was further demonstrated when
different wild oat phenotypes, which produced the same response (growth and seed
output) in wheat when they emerged two weeks after wheat emergence, had
significantly different effects when they emerged at the same time as wheat
(Darmancy and Aujas 1992). The wild oat phenotype that had the largest effect also
had the longest panicles (Darmancy and Aujas 1992). When two wild oat varieties
where tested against wheat, the prostrate variety had a greater competitive effect
against wheat than the upright variety even when the upright variety emerged seven
days earlier. This suggested that morphology can be more important than emergence
time (Rooney 1991).
In conclusion, wheat appears to be more competitive than most other
graminoids tested, except wild oats, which is about equally competitive. Wheat
production is generally not enhanced by the addition of resources (nutrients, water)
when grown with other species. Wheat yields decrease with increasing densities of
competing species until a particular density is reached; further increase in competitor
density has little impact on yield. In some cases wheat spacing can be manipulated to
5
reduce competitive effects on wheat. Finally, taller cultivars of wheat are more
competitive than shorter cultivars and other plant morphological properties can be
more important to competitive success than early emergence.
Interactions between plant competition and ozone
The work done on the interactions between ozone and plant competition has
been very limited. The research that has been done has concentrated on the
interaction between clovers and grasses, (mainly Festuca spp.). The only exception to
clover/grass interactions was a recent study completed by Ziminski (1994) between C3
(western wheatgrass, Agropyron smithit) and C4 (sideoats grama, Bouteloua
curtipendula) grasses.
The first plant competition study with ozone was done by Bennett and
Runeckles (1977) using annual ryegrass (Lolium multiflorum) and crimson clover
(Trifolium incarnatum). The experimental design was a replacement series using
monocultures and a 50:50 mixture.
The ozone exposure was for 8 hrs/day for six
weeks at 0, 0.03 and 0.09 ppm 03. They found no significant differences between the
0 and 0.03 treatments. Ozone impaired the ability of clover to compete with ryegrass
at the 0.09 treatment level and ozone increased the tillering of ryegrass in mixtures
compared to controls and monocultures. However, differences between treatments as
large as 36 percent were not found to be significant (P > 0.05) even though there
were four replicates per treatment and each replicate consisted of twelve plants
(Bennett and Runeckles 1977).
Since Bennett and Runeckles (1977), few studies investigating the effects of
competition and ozone have been done using standard competition methods (Roush et
al. 1989). More typical has been the planting of a plowed field with a mixture of two
species (grass and clover), placing open top chambers over portions of the field, and
comparing treatment differences. Using this approach and including simulated
grazing (clipping), Blum et al. (1983) found that ozone reduced regrowth for both tall
6
fescue (Festuca arundinacea) and ladino clover (Trifolium repens), but more so for
clover. In a further extension of the Blum et al. (1983) study, Rebbeck et al. (1988)
investigated the belowground and aboveground effects of competition using the same
species and experimental design. They found a shift in species dominance from
clover to fescue, based on total biomass, with increasing ozone concentration. The
effects on clover were first noticeable in the aboveground biomass. Fescue increased
aboveground and belowground biomass production in response to increasing ozone
treatment, in part due to the suppression of clover (Rebbeck et al. 1988). Exposure of
field-grown red clover (Trifolium pratense) and timothy (Phleum pratense) to varying
levels of ozone decreased biomass in clover, but with no significant increase in
timothy, as ozone levels increased (Kohut et al. 1988).
Ziminski's (1994) replacement series experiment with a C3 and C4 grass was
done at temperatures not intended to give either species an advantage. He used
monocultures and ratios of 25:75, 50:50 and 75:25 in the replacement series.
Western wheatgrass (C3) was more sensitive to ozone, resulting in a decrease in interand infra- specific competition when measured by total biomass. Sideoats grama was
only able to take advantage (increased biomass) of the decrease in western wheatgrass
when wheatgrass was at its lowest density (25:75) (Ziminski 1994).
Several conclusions can be drawn from the limited work on the effects of
ozone on plant competition. First and foremost is the surprising lack of experimental
work done in this area. The need for experimentation is obvious, as is the need for
experiments designed to understand the effects of plant interference at different
proportions at known densities under different environmental conditions. This lack of
data makes it unwise to extrapolate from the impact of ozone on competitive
interactions under controlled conditions to natural and managed ecosystems. Finally,
the high degree of variability found in the data from almost all of the studies suggests
the need for more experimental control and increased number of replicates in future
studies.
7
Plant competition and disease
There have been relatively few studies investigating the effects of disease on
plant interactions. These studies have generally used some form of a mixed cropping
experiment (either cultivar or species mixtures) and have measured either yield or rate
of disease spread for understanding the effects of disease on plant fitness. In most
experiments, at least one species or cultivar is resistant to a plant pathogen while the
other is susceptible. In addition to traditional experimentation, models have been
developed to determine the response of artificial plant communities after a pathogen
has been introduced under a variety of scenarios.
Since the eighteenth century, agriculture has in part advanced by becoming
increasingly more dependant on a few plant genotypes (Wolfe 1985). The "Green
Revolution" is probably the most well known step in this advancement. However,
these crops are vulnerable to biotic and physical stresses (Wolfe 1985). Specifically,
with the extensively planted, less variable crop lines there is an increased chance for
severe epidemics. One approach to minimize this has been to introduce genetic
variability into agricultural fields by planting two or more cultivars of a crop or
species of crops on the same land. Resistant or immune plants trap inoculum and also
provide less host material for inoculum production and, therefore, increase the chance
of a successful crop. In a survey of the literature, Wolfe (1985) concluded that
mixtures usually yield at least as well as the mean of their components on a per area
basis and sometimes exceed the highest component. In two separate experiments,
Finckh and Mundt (1992 a,b) concurred with Wolfe (1985) and found that mixtures of
wheat cultivars (up to four) out-yielded the same cultivars as monocultures the
majority of the time when inoculated with wheat stripe rust (Puccinia striiformis).
Similar experiments have been performed on non-crop plants. For example,
Burdon et al. (1984) investigated the effects of disease on competition between
genotypes of skeleton weed (Chondrilla juncea) that were either resistant or
susceptible to rust (Puccinia chondrillina). When grown in the absence of rust, yields
8
were very similar, but in mixed stands susceptible plants consistently out-yielded
resistant plants. In the presence of disease, both pure and mixed stand yields were
higher from the resistant than from the susceptible genotypes (Burdon et al. 1984).
This suggests that, at least in these mixtures, disease can led to a reversal of
competitive ability.
If the impact of a pathogen on yield is diminished in mixed stands versus
stands of a single cultivar, then what is the mechanism of this reduction? Burdon and
Chilvers (1982) suggested that the direct effects of susceptible host density and
spacing may be major factors. Chin and Wolfe (1984) showed that the three most
important factors were the: 1) reduction in the density of susceptible plants, 2)
reduction in the movement of inoculum (resistant plants act as barriers) and, 3)
induction of resistance due to non-virulent pathogen biotypes. Other reasons for
reduced pathogen effects in mixed stands may be indirect, such as changes in the
availability of resources from competing species, changes in micro-environment, and
changes in the movement of animal vectors to and from the site (Burdon and Chilvers
1982). Several authors have suggested that the density of the susceptible cultivar is
the primary determinant of disease severity in a wheat stand composed of resistant and
susceptible cultivars (Finckh and Mundt 1992a; Miller et al. 1986; Burdon and
Chilvers 1976 a,b; Burdon and Chilvers 1977). Burdon and Chilvers (1977)
suggested that, at low densities the addition of non-host plants starts to make a
significant contribution to the control of an epidemic. The complexity of the situation
may, in part, be explained by the fmdings of Finckh and Mundt (1992a), who showed
that frequency-dependent effects can be obscured by plant-plant interactions in
heterogeneous host populations.
In an experiment designed to separate pathogen dispersal and competition
effects, Boudreau and Mundt (1992) inter-cropped corn (Zea mays) with beans
(Phaseolus vulgaris) in the presence of bean rust (Uromyces appendiculatus). In some
experimental plots, above-ground corn was removed while in others only the corn
tops were placed in bean plots. The majority of the time, disease was reduced by
9
inter-cropping. However, the observed alteration in pathogen dispersal due to
intercropping may have been the result of a reduction in bean leaf area as the result of
competition from corn rather, than the physical interference of corn plants on spore
dispersal (Boudreau and Mundt 1992). It appears that the reduced impact of disease
in mixed stands is in part related to the density of host individuals, but can be
moderated to a large degree by a number of other factors such as competition and
micro-habitat alterations.
Fitness has been defined as the ability of an organism to establish, survive, and
reproduce successfully (Silvertown 1982). In general, it has been considered that
plants resistant to new hazards incur a metabolic cost, resulting in a loss of fitness
when the selective agent is absent. For example, introduction of the triazine
herbicides resulted in the selection of resistant individuals but at the cost of fitness
(Conard and Radosevich 1979). This principle has recently been challenged for
sulfonylurea herbicides, where the resistant trait has not reduced growth, seed
production, or competitiveness compared to susceptible biotypes of kochia (Kochia
scoparia) (Thompson et al. 1994). With respect to plants and disease resistance, Clay
(1990) reviewed the literature and found such a paucity of empirical studies that it was
inconclusive to suggest that there is a cost in fitness associated with resistance. The
concept that gains in resistance lead to a loss of fitness appears to be an over-
simplification, which may hold only in some instances. The concept is in need of
further study, especially in native plant populations (Clay 1990; Bergelson and
Purrington 1996).
Mathematical models developed for investigating the effects of competition and
disease on plant communities have shown that there are a variety of outcomes
possible, even with their numerous simplifying assumptions. These ecological
interactions do not necessarily lead to stability, but rather the outcome is dependent on
the growth rates, relative competitive abilities of plants, and the rate and selectivity of
pathogen infection and its effect on individual plants (Gates et al. 1986). From a
practical standpoint, models have been used to show growers that using mixtures of
10
plants and spatially separating crops with alternating rows, swaths, or fields of
different host genotypes can be a viable method of disease control (Mundt and Brophy
1988).
In conclusion, while there have been few studies on the effects of disease on
plant interactions, several important findings have been made. The use of cultivar
mixtures can offset yield losses due to disease in agriculture and may help to explain
the relatively high genetic diversity in natural populations. The effects of disease on
plant interactions can be altered by a number of factors including resistant/susceptible
plant density, resource availability, presence of vectors, and environmental
conditions. However, more research is needed to understand more fully the relative
importance of each of these factors.
Interactions between ozone exposure, plant pathogens, and plants
While some research has been performed on bacterial and viral pathogens, this
review is limited to fungal pathogens. Reviews of bacterial and viral pathogens and
ozone can be found in Manning and Keane (1988) and Heagle (1982).
The literature on the interactions between plants, ozone, and fungal plant
pathogens, while not extensive, has slowly developed since at least 1970 (Heagle
1970).
From over twenty years of research, it has been concluded that it is
impossible to generalize and predict effects for any given situation (Heagle 1982, US
EPA 1986, Manning and Keane 1988). The reasons for this lack of clarity may be
from any number of factors, including the inherent variability and unpredictability of
ecological systems. Some experiments were designed to investigate the effects of an
acute ozone exposure on plants inoculated with a disease organism before or after the
ozone exposure (Heagle and Kay 1973). Other studies looked at the effects of chronic
ozone exposures on plants inoculated with disease organisms (Tiedemann et al. 1991;
Fenn et al. 1990; Tonneijck 1994). A variety of plant species have been used as
diseased hosts, including wheat (Tiedemann et al. 1991), soybeans (Damicone et al.
11
1987), ponderosa pine (Fenn et al. 1990), beans (Tonneijck 1994), and corn (Heagle
1977). An even greater number of plant diseases have been used on these hosts. For
example Tiedemann et al. (1990) used six different diseases on just two hosts, wheat
and barley. The causal agents represent both obligate and facultative pathogens as
well as necrotrophic (pathogens of dead tissue) and biotrophic (pathogens of live
tissue) pathogens. However, most obligate pathogens are biotrophic, and most
facultative ones are necrotrophic. Also, the phenological stage of both the plant host
and the disease at exposure varies by experiment. In addition, some experiments used
ozone levels that did not cause visible plant damage (Tiedemann et al. 1991) while in
other experiments ozone levels resulted in visible lesions (Fenn et al. 1990)
With so much variability among a limited number of experiments, it is
understandable that no patterns are apparent. However, Dowding (1988) has listed
several potential underlying themes that may lead to a general understanding of the
experimental work done to date. One such tentative conclusion is that exposure to
ozone encourages the incidence of disease caused by necrotrophic pathogens as
compared with diseased caused by biotrophic pathogens. Dowding (1988) described
several reasons for this, including that: 1) the hyphae of biotrophic pathogens are
more exposed to ozone in the gas phase than are the hyphae/vegetative cells of
necrotrophic pathogens, 2) biotrophic pathogens do not possess the detoxification
mechanism for responding to phytoalexin or polyphenol oxidase, which can be
induced in plants in response to ozone exposure, while necrotrophic pathogens usually
do and, 3) necrotrophic pathogens may be at an advantage because of the increased
amounts of soluble and easily degraded carbohydrates in ozone-insulted leaves, which
presents a more concentrated and more readily available carbon resource and, thus,
enhances growth rates, lesion size, and reproductive rates of necrotrophic pathogens
versus biotrophic ones.
The view that facultative (usually necrotrophic) pathogens are of more
importance than obligate (usually biotrophic) ones in response to ozone exposure was
12
I
so pervasive than no published reports on obligate pathogens and ozone exposure were
reported between 1973 and 1982 (Heagle 1982). However, since 1987 there have
been several studies using obligate pathogens (Dohman 1987, Tiedemann et al 1990,
1992a and b). Tiedemann et al.(1990) concluded that it was too early to suggest that
obligate pathogen responses were well understood, and has made several requests that
studies continue and expand on obligate pathogens and ozone exposure (Tiedemann et
al. 1990, 1991). One thing that does appear known about obligate pathogens is that
the rust diseases are more sensitive to ozone than the powdery mildews (Heagle
1975).
From the limited literature, several rather broad conclusions can be made.
First, whether a disease is influenced by ozone, and the magnitude of that effect,
depends on the timing of pollutant exposure and the infective periods of a particular
host/pathogen combination (Dowding 1988). Second, obligate parasitism is generally
inhibited by air pollutants (Heagle 1982), while infection by facultative pathogens can
result in increases, decreases, or no change in host responses (Heagle 1982; Manning
and Keane 1988). Thirdly, it is difficult to generalize and impossible to predict
effects based on our present knowledge (Heagle 1982, US EPA 1986, Manning and
Keane 1988).
Many basic questions regarding interactions between plant diseases, plants,
and ozone exposures remain to be answered, because so little research has addressed
many variables. For example, the need for field studies to validate laboratory
fmdings was suggested over ten years ago by Heagle (1982), yet today there are only
a few studies using field approaches. There has been little work done on the effects of
root diseases in response to ozone exposure (Fenn et al. 1990). Little work has been
done on the effects of ozone, plants, and pathogen interactions on plant yields
(Manning and Keane 1988). As mentioned above, there is a lack of knowledge on
obligate pathogens (Tiedemann et al. 1990, 1991), which have been dismissed as
predictable. And finally, most studies have been conducted on only the single
interaction of the plant and an air pollutant without regard to other stressors. There is
13
a need for a more holistic approach to evolve that includes factors such as disease
epidemiology, so that the total response of the plant to the pollutant can be determined
(Manning and Keane 1988).
14
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20
II. WHEAT LEAF RUST SEVERITY AS AFFECTED BY PLANT DENSITY
AND SPECIES PROPORTION IN SIMPLE COMMUNITIES OF WHEAT AND
WILD OATS
Thomas G. Pfleeger and Christopher C. Mundt
T.G. Pfleeger, U.S. Environmental Protection Agency, Western Ecology Division, 200
SW 35th St., Corvallis, OR 97333.
C.C. Mundt, Department of Botany and Plant Pathology, 2082 Cordley Hall, Oregon
State University, Corvallis, OR 97331-2902.
The information in this article has been funded by the U.S. Environmental Protection
Agency. It has been subjected to the Agency's peer and administrative review, and it
has been approved for publication as an EPA document. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
Accepted for publication by Phytopathology March 31, 1998.
21
Abstract
Pfleeger, T.G. and C.C. Mundt. 1998. Wheat leaf rust severity as affected by plant
density and species proportion in simple communities of wheat and wild oats.
Phytopathology 88:in press.
While it is generally accepted that dense stands of plants exacerbate epidemics
caused by foliar pathogens, there is little experimental evidence to support this view.
We grew model plant communities consisting of wheat and wild oats at different
densities and proportions and exposed these communities to Puccinia recondita to
induce wheat leaf rust. Wild oats was included because it is a common competitor of
wheat and may act as a barrier to dispersal of P. recondita spores among wheat plants.
Disease severity was estimated as percent of wheat flag leaves covered by rust lesions.
Seeding density rarely had a significant influence on rust severity, probably because of
compensation due to increased tillering at low seeding densities. In contrast, increasing
the proportion of wheat in mixtures with wild oats consistently increased wheat leaf rust
severity. Regression parameters describing wheat leaf rust severity as a function of
wheat seeding density did not differ significantly between pure wheat stands and wheatwild oat mixtures and, thus, failed to support an effect of wild oats on wheat leaf rust
other than through its competitive impact on wheat tiller density.
22
Introduction
According to Burdon and Chilvers (1982), the concept that growing plants in
dense stands contributes to severe epidemics is an accepted axiom with few
experimental data to support it.
They further questioned the contribution of density of
resistant plants in mixed stands on disease control, as opposed to changes in density of
susceptible plants. Since 1982, some progress has been made in determining effects of
plant density on disease severity (reviewed in Augspurger 1989, Boudreau and Mundt
1997). The difficulty in determining effects of plant density on disease severity is due,
in part, to the diversity of life strategies that plants and their pathogens exhibit, making
it difficult to make generalizations concerning plant disease and hostlnonhost densities.
For example, the severity of rosette disease in groundnut (Arachis hypogea L.) is
negatively correlated with plant density (A'Brook 1967, Davies 1976). The causal
agent of groundnut rosette disease is an aphid (Aphis craccivora Kock.)-transmitted
virus. The flying aphids preferentially land on plants associated with surrounding bare
ground (Davies 1976). In contrast, the spread of white rot in onions (Allium cepa L.) by
the fungal pathogen Sclerotium cepivorum Berk. is positively correlated with plant
density and can be controlled by spacing the host to eliminate root contact between
adjacent plants (Scott 1956).
In an attempt to clarify the effect of plant density on disease, Burdon and
Chilvers (1982) classified plant/pathogen interactions into categories based on temporal
scale. They further subdivided each of these three defined time scales (short, medium,
and long) by type of pathogen (virus or fungus) and type of dispersal (soilborne,
airborne, or splash-dispersed). Even with this detailed analysis, they were able to
conclude nothing more specific than the suggestion that host density plays an important
role in the host-pathogen-environment triangle (Burdon and Chilvers, 1982). Others
(Augspurger 1989, Boudreau and Mundt 1997) have suggested, based on these and
more recent data, that there is a tendency for viral-mediated diseases to decrease with
increasing host density and for fungal-mediated diseases to increase with increasing
23
host density. However, it is also becoming clearer that host/nonhost density effects may
be disease-specific, and even temporally and spatially specific. For example, steep
spore deposition gradients will intensify disease locally, but slow the spread of disease,
whereas pathogens with shallow gradients will intensify more slowly, but spread more
rapidly (Fitt and McCartney 1986). In addition, the effects of plant density have not yet
been evaluated for many diseases (Boudreau and Mundt 1997).
Planting mixtures of plant genotypes and /or species for control of herbivores
and pathogens has been and continues to be practiced by aboriginal peoples (Thurston
1991, Plotkin 1993). Mixtures are also becoming an increasing part of modern
agriculture, because in some cases they reduce both the incidence and rate of increase of
disease (Wolfe 1985). Several workers have suggested that mixtures will be most
effective against pathogens with shallow spore dispersal gradients and for hosts with
small plants (Fitt and McCartney 1986; Mundt and Leonard 1986). However,
intercropping can increase, decrease, or have no effect on disease severity (Boudreau
and Mundt 1997). For example, Van Rheenen et al. (1981) working with a bean-maize
intercrop at seven sites over five years in Kenya found that, while eight of ten bean
diseases were likely to be more severe in a monocrop than in the intercrop, there was
always the exception where a given disease was worse or not affected in the intercrop.
The success of intercropping systems for disease control are dependent to a large degree
on the interactions between the amount and location of each crop's tissues, pathogen
dispersal properties, micro climatic conditions, and interspecific competition (Boudreau
and Mundt 1997).
In an attempt to clarify the importance of plant density and species proportion
on cereal rust severity, we grew artificial communities consisting of spring wheat
(Triticum aestivum L.) and wild oats (Avena fatua L.) at different densities and
proportions and exposed these communities to the wheat leaf rust pathogen, Puccinia
recondita Rob. ex. Desm. This system was chosen because wheat is a valuable
agricultural crop, the interaction between wheat and P. recondita is fairly well
understood, and wild oats is a commonly occurring competitor of wheat. We
24
hypothesized that increased host (wheat) density in monocultures and increased host
proportion in mixtures would result in increased wheat leaf rust.
Materials and Methods
Experimental plots were located at the Oregon State University (OSU) Botany
and Plant Pathology Field Laboratory immediately east of Corvallis, OR. Different
fields (approximately 0.2 ha) were used for each year (1995, 1996). No grain crops had
been grown in the fields for several years prior to this experiment. The Chehalis silty
clay loam soil was prepared using standard agricultural practices in the fall prior to
spring planting.
Several days prior to planting (28 March 1995; 16 March 1996), the
field was sprayed with the herbicide glyphosate. A standard soil nutrient test (OSU
Central Analytical Laboratory) indicated that nitrogen levels were below recommended
concentrations for growing wheat. Nitrogen (46-0-0) as urea was added twice over the
1995 growing season at a rate of 56 kg/ha for a total of 112 kg/ha and three times in
1996, at 56 kg/ha once and twice at 45 kg/ha, for a total of 146 kg/ha. Bromoxynil
(Buctril) was sprayed post emergence to control broad leaf plants. The fields were
irrigated with overhead sprinklers three times each year in June and early July.
The experiment was arranged in a randomized complete block design with four
replicates consisting of thirty, 1.2 x 3-m plots. Two commercial, economicallyimportant cultivars of spring wheat (Twin and Penawawa) and wild oats (Avena fatua
L.) were planted with a small plot grain drill at three densities of 81, 162, and 324
germinable seeds per m2 and at five proportions (100:0, 75:25, 50:50, 25:75, and 0:100)
of wheat:wild oats. A total of 24 cultivar x density x proportion combinations were
used for disease analysis; the 100 percent wild oats plots were excluded. No plot
contained both wheat varieties. Between rows.of plots, a 1.2-m wide buffer strip of
barley (Hordeum vulgare L. cv. Steptoe) (1995) or triticale ( x Triticosecale Wittmack
cv. Celia) (1996) was planted to reduce the spread of spores between plots.
The wheat cultivar Twin is susceptible to wheat leaf rust, whereas Penawawa is
moderately resistant, and wild oats is immune. Wild oats was chosen because it is a
25
competitor of cultivated wheat in western Oregon and many other wheat growing areas
and may act as a barrier to dispersal of P. recondita spores among wheat plants. Several
sources of wild oats seed were tested for germination. Seed collected as screenings
from a grain elevator in Alberta, Canada was used as it had the highest germination rate.
Wheat also was tested for germination prior to planting, and seeding rates for both
species were adjusted to produce densities of 81, 162, and 324 germinable seeds per m2.
Uredospores of P. recondita were collected from greenhouse-inoculated wheat
and mixed with talc powder prior to field application. Plots were inoculated with
uredospores using a hand-held duster in the early morning or late evening during periods
of low wind to minimize drifting. Generalized inoculation was used to diminish
sampling difficulties associated with focal point inoculation, and because generalized
inoculation more closely simulates the natural inoculation of wheat with wheat leaf rust
in western Oregon. In 1995, a total of 11.2 grams of uredospores were applied on 3 and
4 June. In 1996, a total of 15 grams were applied on seven days from 5 May to 13 June.
More spores and days of application were required in 1996 because cold temperatures
dUring May prevented disease establishment. Inoculation was started earlier in 1996 to
assure the disease was established during flag leaf emergence.
Disease severity readings were taken on 18-19 July 1995, and on 27-28 June and
10-11 July 1996 by estimating the percent of leaf area covered by pustules and
associated chlorosis for 10 randomly-chosen wheat flag leaves in each plot. One
hundred percent disease was defined as a leaf blade covered completely with pustules
and associated chlorosis. All disease readings were performed by the senior author
following training using the DISTRAIN computer program (Tomerlin and Howell 1988)
prior to each assessment. The final proportions of wheat and wild oats in the mixtures
were determined by counting the number of culms of each species from a 0.25 m2
subplot in the center of each plot at harvest.
Data were initially compiled in a computer spreadsheet for preliminary statistical
measures. Final statistical analyses were performed using the Statistical Analysis
System (SAS Institute Inc., Cary, NC). Four plots were lost to planting and seed
mixing errors in 1995.
26
Analysis of variance (ANOVA) for disease severity was performed using the
SAS general linear models procedure (PROC GLM) to test for cultivar, seeding density,
and species proportion main effects, and all possible interactions among these main
effects. The percent disease readings were transformed using the arcsin of the square
root transformation to better meet the homogeneity of variance and normality
assumptions. One outlier was removed from the analysis for the June 1996 data,
although the ANOVA with and without the outlier indicated little difference in results.
Post-ANOVA polynomial contrasts were used to describe the effects of density,
proportion, and their interaction on rust severity.
Regression of wheat leaf rust severity on measured wheat density was performed
on two subsets of the data that consisted of: 1) plots containing 100 percent wheat, but
planted at different densities and 2) mixed wheat-wild oats plots planted at one seeding
density (324 seeds per m2 ). These regressions were used to differentiate between the
effects of density of susceptible plants versus barrier effects of resistant plants
(interception of spores by nonhost species). Tests for equality of regression lines and yintercepts were performed on the two subsets by using F-tests.
Results
Generally, there was an increase in the number of wheat cuims or flag leaves
with increasing wheat seeding density in plots containing 100 percent wheat, indicating
that density of resources for pathogen multiplication increased with seeding density
(Fig. II-1). Increasing the proportion of wild oats in mixed stands with wheat decreased
the number of cuims per wheat plant, indicating a negative competitive effect of wild
oats on wheat (Fig. II-1). Regressions of wheat and wild oat culm number on total
seeding density showed that, while overall culm density was greater at higher seeding
densities, there was not a one to one ratio or even a linear relation between planting
density and the resulting number of cuims (Fig. 11-2).
Wheat leaf rust severity was not significantly affected by seeding density.
However, disease severity was significantly impacted by the proportion of wheat in
27
1995
700
700
Tit
600
500
400
300
200
100
II
600
500
II
400
300
li
200
100
0
0
.411*1111.!1*./
324
vo,e
A,/,,,_
,.....,
81
/7 50%
rseects
..
-,
47,J)
.b
ioo To
75%
a)
25%
i'
e
ie
1,41
cr
V
1996
700
600
600
500
500
400
400
300
300
200
200
100
100
0
It iti
II
II
II
0
324
00% 05.z
SN. 81
e
75%
50%
25%
e
Figure II-1. Plot of wheat culm number (flag leaf number) on seeding density of two
cultivars (Twin and Penawawa) in monocultures and wheat-wild oats mixtures. Each bar
is the mean of four replicate plots planted at one of four proportions (wheat:wild oats,
100:0, 75:25, 50:50, 25:75) for each of three seeding densities (81, 162 and 324 seeds
m-2). A = Twin wheat 1995, B = Twin wheat 1996, C = Penawawa wheat 1995, and
D = Penawawa wheat 1996.
28
650
600ri
g
o
1995
550500
450-
Twiny=9.7+ 11.5 x+ 12.2 x2
400
cr)
-o
P. 0.0001, 0.0028 R2 = 0.64
Penawawa y = 8.8 + 10.3x 11.4 x 2
35°-
P= 0.0001, 0.0765 R2 = 0.75
1'4
300
650
174
4.)
600
tu
550
E
500
=
o
450
3C Twiny=11.3 + 11.9 x+ 12.4 x
P. 0.0140, 0.6459 R2 = 0.64
400
Penawawa y = 9.8 + 11.1 x 11.7 x
350300
2
P= 0.0001, 0.0101 R2 = 0.60
1-
50
2
100
I
150
I
200
I
250
1
300
350
Seeding density (seeds m-2)
Figure 11-2. Regression of combined wheat and wild oats culm number on combined
seeding density of wheat (cultivar Twin or Penawawa) in monocultures and wheat / wild
oats mixtures. Regressions were performed on transformed number of culms per m2
(square root) and the data were back-transformed for plotting. Each point is the mean of
sixteen data points: four replicate plots planted at four proportions (wheat:wild oats,
100:0, 75:25, 50:50, 25:75) for each of three seeding densities (81, 162 and 324 seeds
m-2). R2 - values were calculated from the entire data set for each cultivar by year. P values are from F-tests to determine if the model had a significant linear component
and/or a significant nonlinear component.
29
wheat-wild oats mixtures for both July data sets (Table II-1). The wheat cultivar Twin
was significantly more susceptible than Penawawa for both years and at all samplings
(Table II-1; Fig. 11-3). Generally, there was a lack of interactions between experimental
treatments. Only two of the twelve interactions including density and/or proportion
were significant (Table II-1).
The significant interaction between proportion and cultivar in June 1996, along
with the known differential sensitivity of each cultivar to wheat leaf rust, indicated the
need for separate ANOVAs by cultivar (Table 11-2). At the cultivar level, density of
wheat was again not a significant factor in determining rust severity, whereas proportion
of wheat in mixture with wild oats was always a significant factor in the July data sets
(Table 11-2). The exception was the cultivar Twin in July 1996, when density had a
significant effect (Table 11-2). The June 1996 data indicated a difference based on
cultivar (Table II-1), where seeding density and proportion were significant factors
influencing disease severity for the cultivar Twin, but not for Penawawa (Table 11-2, Fig.
11-3).
There was often a positive and linear relationship between percent rust severity
and wheat proportion (Table 11-2). In some cases, there was also a significant nonlinear
component, but in all of these cases the nonlinear component was less significant than
the linear component (Table 11-2). The proportion x density interaction reported for
combined cultivars (Table II-1) in 1995 was not present when cultivars where analyzed
separately, except for the cultivar Twin in July 1996 (Table 11-2).
Leaf rust severity increased with the proportion of wheat in all cases, except the
June 1996 data for Penawawa (Fig. 11-3). The proportion of wheat in mixtures with wild
oats was pooled across different seeding densities for regression analysis (Fig. 11-3)
because density was rarely significant, there was an absence of significant density by
proportion interactions in the 1996 data, and because the proportion mean square was
much larger than the density by proportion interaction mean square in the 1995 data set
(Table II-1). As expected, the slopes and y-intercepts for the susceptible cultivar Twin
Table II-1. Analyses of variance for percent leaf rust severity of two spring wheat cultivars (Twin and Penawawa) grown at three
planting densities (81, 162, and 324 seeds m2) in both pure stands and in mixture with wild oats at four proportions (wheat:wild oats,
100:0, 75:25, 50:50, 25:75)'. One outlier was removed from the June 1996 data.
July 1995
Source of variation
DF
MS
Block
3
Cultivar
June 1996
P>F
DF
0.0288
0.0172
1
4.194
Density
2
Cultivar x Density
July 1996
MS
P>F
3
0.0629
0.0009
0.0001
1
0.0681
0.0145
0.1689
2
2
0.0128
0.2074
Proportion
3
0.1018
Cultivar x Proportion
3
Density x Proportion
Cultivar x Density x
Proportion
MS
P>F
3
0.1259
0.0001
0.012
1
0.8789
0.0001
0.0177
0.184
2
0.0147
0.3901
2
0.015
0.2372
2
0.0263
0.188
0.0001
3
0.0172
0.1791
3
0.2149
0.0001
0.0055
0.5577
3
0.0318
0.0316
3
0.0059
0.7643
6
0.0218
0.019
6
0.0085
0.5458
6
0.0102
0.6768
6
0.0089
0.3592
6
0.0085
0.551
6
0.0112
0.6285
Error
65
0.0079
68
0.0102
69
0.0514
Total
91
94
95
0.90
0.45
0.68
R2
a Analyses were conducted on the arcsin of square root-transformed values.
DF
31
Leaf Rust Severity
100
X Twin y = 0.0022 x + 0.7155
P = 0.0003 R2 = 0.53
July 1995
80
Penawawa y = 0.0011 x + 0.2858
60
P = 0.0005 R2
40-
= 0.63
207
0
100
80-
June 1996
X Twin y = 0.0019 x + 0.5871
P = 0.0003 R2 = 0.66
6040
20-
Penawawa y = 0.0001 x
P = 0.8571 R
= 0.28
ar-11.00"""1"1.11111"."-
0
100
80-
60-
X Twin y = 0.0033 x + 1.1741
P = 0.0001 R2 2 = 0.81
40-
Penawawa y = 0.0028 x + 0.9827
200
0
P = 0.0019 R2 = 0.45
I
I
I
25
50
75
100
Percent of wheat in mixture
Figure 11-3. Regression of wheat leaf rust severity on proportion of wheat culms of two
cultivars (Twin and Penawawa) in wheat monocultures and wheat-wild oats mixtures at
harvest. Regressions were performed on transformed percent disease severity
measurements (arcsin of the square root) and the data were back-transformed for plotting.
Each point is the mean of twelve data: four replicate plots planted at three seeding densities
(81, 162 and 324 seeds m-2) for each of four proportions (wheat:wild oats, 100:0, 75:25,
50:50, 25:75). R2 values were calculated from the entire data set for each cultivar by
date. P - values are from F-tests to determine if slopes were significantly different from
zero.
Table 11-2. Analyses of variance by cultivar (Penawawa or Twin) for percent leaf rust severity of two spring wheat cultivars grown at
three planting densities (81, 162, and 324 seeds m-2) in both pure stands and in mixture with wild oats at four proportions (wheat:wild
oats, 100:0, 75:25, 50:50, 25:75)a. Penawawa is moderately resistant and Twin is susceptible to wheat leaf rust. One outlier was
removed from the June 1996 Penawawa data.
July 1995
Cultivar
Source of variation
DF
June 1996
MS
P>F
DF
July 1996
MS
P>F
DF
MS
P>F
Penawawa
Block
3
0.0164
0.0144
3
0.0345
0.0756
3
0.0949
0.0231
Density
2
0.0045
0.3393
2
0.0002
0.9869
2
0.0215
0.4467
Proportion
3
0.0373
0.0001
3
0.0087
0.5988
3
0.1088
0.0133
0.0613
0.0005
1
0.0004
0.8566
1
0.2986
0.0019
linear
1
nonlinear
2
0.0231
0.0073
2
0.0128
0.4027
2
0.0138
0.5949
Density x Proportion
6
0.0055
0.2577
6
0.0064
0.8266
6
0.0101
0.8831
32
0.0137
33
0.0262
Error
31
Total
45
R2
0.63
0.004
46
0.28
47
0.45
Table 11-2
cont.
Twin
Block
3
0.0156
0.303
3
0.0472
0.0004
3
0.051
0.0001
Density
2
0.023
0.1708
2
0.033
0.0089
2
0.0193
0.0159
Proportion
3
0.0714
0.0029
3
0.0407
0.0011
3
0.112
0.0001
linear
1
0.1984
0.0003
1
0.097
0.0003
1
0.3028
0.0001
nonlinear
2
0.0062
0.6065
2
0.0125
0.1425
2
0.0165
0.0274
Density x Proportion
6
0.0349
0.0922
6
0.0101
0.1576
6
0.0113
0.0277
Error
33
0.0041
33
0.006
Total
47
le
0.81
47
0.66
a Analyses were conducted on the arcsin of square root-transformed values.
33
47
0.81
0.004
34
were greater than for the more resistant cultivar Penawawa (Fig. 11-3). Slopes increased
from June to July 1996 for both cultivars.
There was no evidence to suggest that the barrier effect of wild oats in mixtures
was significant in decreasing disease severity. This was tested using regression analysis
to determine if the effects of differing culm density in pure stands were significantly
different from the effects of differing culm density (changes in proportion) of wheat in
wheat-wild oats mixtures (Table 11-3). In no case was there statistical evidence to
suggest that the slopes for rust severity on culm number per unit area were different for
density versus proportion treatments (Table 11-3), and in only one case (Penawawa, July
1996) was there evidence that the regression lines were not equivalent (Table 11-3).
Discussion
Host density: Planting density did not affect rust severity significantly, even though
the highest seeding density was four times the lowest. This lack of significance
probably can be explained by the plastic response of plants in response to space (Harper
1977) and, in particular, the ability of wheat to make compensatory growth in response
to variable growing conditions (Frederick and Marshall 1985). The plastic growth of
plants, particularly annuals, is a confounding factor with density treatments, as density
remains fixed while individual plants change by growing in response to their
environment. For example, the range in density of harvested culms among treatments
was much smaller (1.1 to 1.9 times) than the four-fold range in initial seeding rates (Fig.
II-1 and 11-2). This response held regardless of the proportion of wheat in a mixture
(Fig. II-1). However, wheat culm density changed the least with increasing seeding
density in the mixed plots having the lowest percentages of wheat, probably due to the
competitive effect of wild oats on wheat.
Table 11-3. Y-intercepts and slopes for regression of percent left rust severity on density of wheat culms (culms/m2) in pure stands'
and in wheat-wild oats mixturesb grown at Corvallis, OR during 1995 and 19960
Date
July
1995
June
1996
July
1996
Cultivar
Plot-type
y-intercept
SEd
slope
SEd
Penawawa
Mixed stand
Pure stand
0.249
0.266
0.038
0.044
0.0008
0.0009
0.0009
0.0005
0.89
0.73
Twin
Mixed stand
Pure stand
0.740
0.692
0.088
0.135
-0.0012
0.0009
0.0015
0.0010
0.24
0.17
Penawawa
Mixed stand
Pure stand
0.508
0.608
0.083
0.190
0.0002
-0.0003
0.0012
0.0013
0.77
0.84
Twin
Mixed stand
Pure stand
0.529
0.396
0.049
0.127
0.0008
0.0015
0.0006
0.0008
0.43
0.58
Penawawa
Mixed stand
Pure stand
0.849
1.363
0.093
0.211
0.0006
-0.0019
0.0013
0.0015
0.23
0.03
Twin
Mixed stand
Pure stand
1.025
1.108
0.048
0.125
0.0017
0.0009
0.0005
0.0008
0.43
0.70
P>Fe
P>Ff
a Wheat seeded at densities of 81, 162, and 324 seeds m-2.
Wheat-wild oats mixtures seeded at a combined density of 324 seeds M-2 at four proportions (wheat:wild oats, 100:0, 75:25, 50:50,
25:75).
Analyses were conducted on the arcsin of square root-transformed values.
d Standard errors of the estimates.
`Testing the hypothesis H.: regression slopes for Pure and Mixed stands are equal, using an F-test.
f Testing the hypothesis H.: regression lines (i.e., joint test for equal intercepts and equal slopes) for Pure and Mixed stands are equal,
using an F-test.
b
36
A wider range in seeding densities than we used might help clarify the effects of
density on rust severity, but at high densities self-thinning may occur (Yoda et al. 1963,
Darwinkel 1978). Darwinkel (1978, 1980) planted winter wheat at densities varying up
to 160-fold, but only a 4.3 to 5.7-fold increase in culm number at harvest was found due
to an increased number of culms/plant with decreased planting density. Shah et al.
(1994) planted winter wheat at two moderate densities (84 and 168 kg/ha) and found no
significant difference in grain yield when seeded early. Yields differed with later
planted wheat, and they attributed this difference to the greater tillering ability of early
planted wheat.
Gassner (cited in Chester 1946) sowed wheat from 30 to 160 kg/ha and found no
difference in the amount of wheat leaf rust because the wheat had a tendency to regulate
its stand to a uniform density. When artificial density gradients were maintained, he
still found no correlation between plant density and disease severity. In contrast,
Russian scientists (Smitikova-Rusakova; Rusakov cited in Chester 1946) did similar
controlled density studies with wheat and found that rust intensity increased with
density. Gassner (cited in Chester 1946) conducted his studies in Uruguay, where he
suggested that micro-climatic conditions were conducive to rust infection at all wheat
densities, whereas optimal conditions for rust development were met only in the higher
densities of wheat in Russia (Chester 1946).
Since the early work described above, others (Berger 1975, Strandberg and White
1978, Burdon and Chilvers 1976a, Burdon et al. 1992) have suggested that micro-
climatic changes resulting from higher plant densities has lead to improved conditions
for disease development. This, in part, may explain the year to year differences seen in
epidemic length and severity (Fig. 11-3), as the 1995 crop was less productive and
probably had microclimatic differences less conducive to disease development than the
1996 field environment. Thus, proximity of plants may not be the only cause of
positive correlations between plant density and disease severity. However, dense stands
also produce higher levels of competition and the subsequent loss of leaf tissue lower in
the canopy, which could otherwise become infected (Chester 1946, Shaner 1973,
37
Burdon and Chilvers 1976a). In addition, plant nutritional status probably differed with
density and host nutritional levels can have a significant impact on disease susceptibility
and progression (Chester 1946).
Species proportion: The relation between disease severity and wheat proportion was
linear and positively correlated for both transformed and back-transformed scales. A
similar result was found for stripe rust, caused by P. striiformis, in wheat cultivar
mixtures (Akanda and Mundt 1996). Leonard (1969) developed an analytical model,
based on the biology of rust pathogens, to predict the impact on disease of the
proportion of susceptible plants in mixtures of resistant and susceptible plants. This
model predicts a logarithmic relationship between the proportion of susceptible plants in
mixture and the rate of epidemic development, a relationship that is supported by
experimental studies (Burdon and Chilvers 1977; Leonard 1969; Luthra and Rao 1979).
As the relationship between epidemic rate and disease severity is not linear for a
polycyclic epidemic, it is not surprising that proportion of susceptible to nonsusceptible
plants in a population has a different impact on these two parameters. Regardless,
treatments with the 'owes/ proportion of susceptible plants had the least disease in our
study, as is usually the case for rusts and mildews of small grains (Browning and Frey
1969; Mundt and Browning 1985; Wolfe 1985).
Mechanisms of non-host influence: The question remains as to the role wild oats
played in reducing wheat leaf rust severity. Did the oats act as a physical barrier to the
movement of inoculum, or did it merely occupy space and effectively change the
density of wheat? As the proportion of wheat increases in a series of mixed species
plots planted at the same combined total density, the density of wheat increases while
the barrier effect provided by the nonhost species decreases. If the regression lines from
the two subsets of data (Table 11-3) are the same, then the density component of
proportion would be the most important factor. In contrast, if the slope from the
changing proportion data is steeper, then there could be a significant barrier effect from
38
the wild oats. We found no significant difference between the regression lines,
suggesting that non-host tissue was not an effective barrier to inoculum movement.
However, the variability associated with our measurements was quite high and larger
sample sizes are needed to clarify the issue. In addition, it is possible that competitive
interactions between wheat and wild oats altered the susceptibility of wheat to rust, as
was suggested for wheat cultivar mixtures infected by stripe rust (Finckh and Mundt
1992).
While at the end of the growing season wild oats is much taller than wheat, wild
oats height extension does not occur until wheat has already set seed. Therefore, the
effect of height differences between wild oats and wheat are probably limited to later
epidemic stages that would impact wheat seed size (as other yield components will
already have been established), and to potential dispersal to other, later maturing fields.
Burdon and Chilvers (1976b, 1977) have suggested that a decrease in disease
caused by fungal pathogens in mixed species assemblages is not a response to the
nonhost interception of airborne inoculum, but rather to a reduction in density of the
host. chin and Wolfe (1984) in an elegant experiment to separate effects of density,
proportion, and induced resistance in cultivar mixtures, were never able to quantify the
difference between density and proportion. They concluded that the effect of cultivar
mixtures could be partitioned into three categories: 1) when plants were small, changes
in plant density controlled disease levels; 2) the presence of resistant cultivars increased
in importance as plants grew, by acting as barriers to inoculum movement; and 3)
differentially susceptible cultivars helped control disease later in the season by allowing
induced resistance to occur. In addition, changes in plant competition and
microclimate are also likely to change with host density. For example, a species that is
competitively superior might have more resources available for obligate pathogen
infection when grown in mixture than in monoculture at the same density.
As suggested by Power (1991), the general difference attributed to insect-vectored
pathogens versus wind-dispersed fungal pathogens in relation to host density may be
due to active selection of host plants by a limited number of insects. In contrast, fungal
39
pathogens passively disperse spores and spores may be transported to plant genotypes
on which they cannot reproduce. When nonhost species acquire significant space, they
reduce host species densities and/or act as traps for inoculum. In doing so they may
have an important role in natural communities by decreasing the severity of some
diseases. Thus, plant diversity may help to sustain rare but susceptible species in plant
communities, in contrast to some herbivore/plant interactions, where a plant's
susceptibility to herbivory can increase if surrounded by diverse, acceptable host plants
when attacked by polyphagous insects (Stanton 1983). Therefore, rarity could be a
more successful strategy for plants to escape fungal pathogens than to escape
herbivores.
The conceptual problem of trying to attribute part of wheat leaf rust progression to
host density and/or proportion is that neither term is adequately inclusive to explain the
dynamic nature of the process. Wheat leaf rust progression might be better considered
as space acquisition by the host. Initially, plant hosts functionally exist in twodimensional space, where the number of seedlings is a meaningful way to interpret the
potential for infection. Later, as plants grow and occupy three-dimensional space,
inoculum is not limited to landing on bare ground or seedlings, but can also be
intercepted by nonhost tissue. However, locally, inoculum may be so plentiful that
nonhost interception is insignificant. At a larger spatial scale, wheat leaf rust
progression across the landscape is additionally influenced by such factors as the
distribution of infected tissue, its relation to prevailing winds, and changes in wind
speed at the source and potential infection sites.
40
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insect search. In: Host Seeking Behavior and Mechanisms. S. Ahmad, ed.
Academic Press. New York, NY. p. 125-157.
Strandberg, J.0., and White, J.M. 1978. Cercospora apii damage of celery--effects of
plant spacing and growth on raised beds. Phytopathology 68:223-226.
Thurston, H.D. 1991. Sustainable Practices for Plant Disease Management in
Traditional Farming Systems. Westview Press. Boulder. CO.
Tomerlin, J.R. and Howell, T.A. 1988. DISTRAIN: a computer program for training
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University 14:107-129.
43
III. EFFECTS OF WHEAT LEAF RUST ON INTERACTIONS BETWEEN
WHEAT AND WILD OATS
Thomas G. Pfleeger, Michelle A. da Luz, and Christopher C. Mundt
T. G. Pfleeger, U.S. Environmental Protection Agency, Western Ecology Division, 200
SW 35th St., Corvallis, OR 97333.
Michelle A. da Luz
Dynamac Corp., 200 SW 35th St., Corvallis,.OR 97333.
Christopher C. Mundt
Department of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University,
Corvallis, OR 97331-2902.
This document is a preliminary draft. It has not been formally released by the U.S.
Environmental Protection Agency and should not be construed to represent Agency
policy. It is being circulated for comments on its technical merit and policy implications.
Do not release. Do not quote or cite.
Accepted for publication 0000 00 0000
44
Abstract.
Pfleeger, T.G., M.A. da Luz, and C.C. Mundt. 1998. Effects of wheat leaf rust on
interactions between wheat and wild oats. Ecological Applications.
Plant competition has long been considered a major process in the determination
of the composition and structure of plant communities. The importance of competition as
a major influence on plant communities has recently been questioned because other types
of interactions can cause significant compositional changes. For example, plant
pathogens are considered important in determining plant distribution and are used to
control alien plant species. The goal of this research was to broaden our understanding of
disease as a process structuring plant communities under a variety of competitive
scenarios. The experiment was arranged in a randomized, complete block design with
four replicates. Two commercial, economically important cultivars of spring wheat (Twin
and Penawawa) and wild oats (Avena fatua L.) were planted at three densities of 81, 162,
and 324 germinable seeds per m2 and at five proportions (100:0, 75:25, 50:50, 25:75, and
0:100) of wheat:wild oats for a total of 30 cultivar x density x proportion combinations.
One-half of each block was inoculated with uredospores of Puccinia recondita. Disease
severity readings were taken a month prior to the harvest of the aboveground plant
material. Plant measures included total aboveground biomass, number of stems, length of
stems, and grain yield. The two wheat cultivars differed in their growth and in their
interactions with wild oats. As expected, increasing the proportion of wheat or oats in
mixtures lead to consistent and significant increases in the amount of aboveground
biomass and total seed weight for that species. Wheat and wild oats seed weight and
aboveground biomass per culm or per planted seed decreased as the proportion of wild
oats increased in mixtures, indicating an advantage for wild oats in mixtures with wheat.
Wild oats generally did not respond significantly to the effects of leaf rust on wheat,
while wheat was negatively impacted. Lowered wheat performance in inoculated stands
was the main reason for lower relative biomass ratios of wheat and wild oats.
45
Introduction
Plant competition has long been considered a major process in the determination
of the composition and structure of plant communities (Clements 1929). The importance
of competition as a major influence on plant communities has recently been questioned
because other types of interactions can cause significant compositional changes. For
example, the presence of seed predators controlled recruitment and density of a native
shrub over an elevation gradient in the coastal vegetation of California (Louda 1982). It
is now a routine, although elaborate, procedure to introduce herbivores to control alien
plant species (DeBach and Rosen 1991). Plant pathogens have also been considered
important in determining plant distribution (Burdon 1987) and used to control alien plant
species, although at a more limited scale (Te Beest et al. 1992). In addition to these
negative interactions, mutualistic interactions, via mycorrhizae, may support less
dominant species and individuals by providing a means to transport materials between
plants. For example, Simard et al. (1997) demonstrated carbon transfer between different
species of trees via mycorrhizal fungi, based on source-sink relationships.
Investigations on the effects of plant pathogens on plant communities have
generally been limited to agricultural systems, due to economic incentives and the
biological complexity of pathogen, host, and environmental interactions (Burdon 1987).
In agriculture, fields mixed with two or more species or genotypes provide traps for
inoculum and provide less host material for inoculum production, increasing the chances
for a successful crop (Wolfe 1985). In a rare example using non-crop plants, Burdon et
al., (1984) found that competitive abilities of skeleton weed (Chrondrilla juncea)
genotypes resistant and susceptible to rust (Puccinia chrondrillina) could be reversed in
the presence of disease. While it may be a common axiom to suggest that gains in
disease resistance lead to a loss of fitness, this appears to be an over simplification based
on limited data and may hold only in some instances (Bergelson and Purrington 1996;
Simms and Triplett 1994; Clay 1990). The outcome of interactions between disease and
competition are dependent on the growth rate, relative competitive abilities of plants, the
46
rate and selectivity of pathogen infection, its effect on individual plants, and microhabitat
alterations (Chilvers and Brittain 1972; Gates et al. 1986; Boudreau and Mundt 1992).
While there have been few studies on the separate and joint effects of disease and
plant interactions, several significant findings have been made. The use of varietal
mixtures can offset yield losses due to disease in agriculture and may help explain the
presence of genetic diversity in natural populations (Finckh and Mundt 1992; Burdon
1982). The effects of disease on plant interactions can be altered by a number of factors,
including resistant/susceptible plant density, resource availability, presence of vectors,
and environmental conditions (Boudreau and Mundt 1992; Burdon 1993). However, the
relative importance of each of these factors is unknown.
The overall goal of our research was to broaden our understanding of disease as a
process structuring plant communities. Our specific objectives were to determine: 1) the
effects of density and proportion of a competing species pair on a species' productivity;
2) if those effects were modified by a pathogen, and 3) if seeding density, mixture
proportions, or disease interact. We hypothesized that the diseased host species would
be less productive, thus increasing the resources available to the non-host species and
resulting in enhanced growth of the non-host. Further, we hypothesized that varying the
initial conditions by increasing seeding density and higher proportions of the host species
in mixtures would intensify pathogen effects.
Analysis of disease severity in the experiment described in this paper has been
published (Pfleeger and Mundt 1998). These analysis indicated that: 1) seeding density
rarely had an influence on disease severity and 2) as the proportion of non-host tissue
increased disease severity decreased. The effects of increased non-host proportion were
attributed to decreased host tiller density and not from decreasing the movement of
inoculum (`barrier effect').
47
Materials and Methods
Experimental plots were located at the Oregon State University (OSU) Botany and
Plant Pathology Field Laboratory immediately east of Corvallis, OR. A different field
(approximately 0.2 ha) was used each year (1995, 1996). No grain crops had been grown
in the fields for several years prior to this experiment. The Chehalis silty clay loam soil
was prepared using standard agricultural practices in the fall prior to spring planting.
Several days prior to planting (28 March 1995; 16 March 1996), the field was sprayed
with the herbicide glyphosate (Roundup). A standard soil nutrient test (OSU Central
Analytical Laboratory) indicated that nitrogen levels were below recommended
concentrations for growing wheat. To overcome this, nitrogen (46-0-0) as urea was
added twice during the 1995 growing season at a rate of 56 kg/ha for a total of 112 kg/ha
and three times in 1996, once at 56 kg/ha and twice at 45 kg/ha, for a total of 146 kg/ha.
Bromoxynil (Buctril) was sprayed post emergence to control broadleaf plants. The fields
were irrigated with overhead sprinklers three times each year, in June and early July.
The experiment was arranged in a randomized complete block design with four
replicates consisting of thirty, 1.2 x 3-m plots. Two commercial, economically important
cultivars of spring wheat (Triticum aestivum L. cv. Twin and Penawawa) and wild oats
(Avena fatua L.) were planted with a small plot grain drill at three densities of 81, 162,
and 324 germinable seeds per m2 and at five proportions (100:0, 75:25, 50:50, 25:75, and
0:100) of wheat:wild oats for a total of 30 cultivar x density x proportion combinations.
No plot contained both wheat cultivars. Between rows of plots, a 1.2-m wide buffer strip
of barley (Hordeum vulgare L. cv. Steptoe) (1995) or triticale ( x Triticosecale Wittmack
cv. Celia) (1996) was planted to reduce the spread of spores between plots.
The wheat cultivar Twin is susceptible to wheat leaf rust, whereas Penawawa is
moderately resistant and wild oats is immune. Wild oats was chosen because it
commonly occurs with cultivated wheat in western Oregon and many other wheat
growing areas. Several sources of wild oats seed were tested for germination. Seed
collected as screenings from a grain elevator in Alberta, Canada was used as it had the
highest germination rate. Wheat also was tested for germination prior to planting, and
48
seeding rates for both species were adjusted to produce densities of 81, 162, and 324
germinable seeds per m2.
Uredospores of Puccinia recondita f. sp. tritici were collected from greenhouseinoculated wheat and mixed with talc powder prior to field application. One-half of each
block was inoculated with uredospores of P. recondita. The other half of each block was
sprayed with the fungicide (Bayleton) at 0.28 kg ai/ha to control wheat leaf rust.
Bayleton was applied at three-week intervals (two times in 1995, four times in 1996) to
the non-inoculated plots using a backpack sprayer fitted with a hand-held, four nozzle
spray boom. Bayleton does not affect yield of wheat (Mundt et al.1995). A detailed
analysis of disease severity in the plots has been published (Pfleeger and Mundt 1998).
Aboveground plant material (0.25m2) was harvested by hand from the center of
each plot when the wheat grain reached maturity. Wheat and wild oats were bagged
separately in the field and transported to the laboratory where they were air-dried to
constant weight. Aboveground biomass was measured for each species. Grain yield of
wheat was determined after a portable thresher was used to separate the seed from the rest
of the plant. Grain yield of wild oats was not determined because the seeds are dehiscent
and much of the seed was dispersed prior to wheat maturity. Mean seed weight was
determined by weighing 200 seeds for each species from each plot
Data were initially compiled in a computer spreadsheet for preliminary statistical
measures. Final statistical analyses were performed using the Statistical Analysis System
(SAS Institute Inc., Cary, NC). Four plots were lost to planting and seed mixing errors in
1995. Analysis of variance (ANOVA) was performed using the SAS general linear
models procedure (PROC GLM) to test for disease main effects and cultivar, planting
density, and species proportion split-plot effects, and all possible interactions among
these treatments. Interactions between the whole plot treatment factor and the split-plot
treatments were tested using the split-plot mean squared error term. Simple means
comparisons for differences between split- plot factors were tested using t-tests with the
split-plot mean squared error term in the denominator. Post-ANOVA regression analysis
was done with either seeding density or species proportion as the predictor variable
49
against wheat or wild oats response variables. Tests for equality of regression lines and
y-intercepts were performed by using F-tests.
Relative biomass was calculated by dividing the aboveground biomass of wheat
per planted seed by that of wild oats. A relative biomass greater than one implies that
wheat was more productive than wild oats. A relative biomass of one indicates the two
species were equally productive. Expected values of relative biomass, under the
assumption of no interspecific competition, were calculated based on the aboveground
biomass of wheat and wild oats per seed planted in monoculture at each of the three
planting densities. Differences between expected and observed relative biomass where
assumed to measure interspecific competition.
Results
Disease effects: Wheat leaf rust generally decreased wheat performance. This trend was
consistent, but not always significant. The trend was apparent for both years and for all
measures taken, including both aboveground biomass and reproductive output (Table III1). In contrast, the wild oats data lacked a consistent pattern and generally, wild oats was
not significantly impacted by inoculation (Table III-1).
The pattern of smaller wheat from inoculated plots was also consistent at the
cultivar level for all response variables, although the pattern was stronger for Twin than
for Penawawa, especially in 1995 (Table III-1). This was expected, as Penawawa is
considered moderately resistant to wheat leaf rust. However, Penawawa responded
similarly to Twin, a susceptible cultivar, in 1996 (Table III-1). In contrast, wild oats was
not significantly impacted by inoculation regardless of the disease resistance of its wheat
neighbor (Table III-1).
Cultivar effects: As expected, the two wheat cultivars differed in their growth and in
their interactions with wild oats. Penawawa was smaller (less aboveground biomass and
total grain weight) than Twin, but mean grain weight from Penawawa was larger (Table
Table III-1. Means of two cultivars of wheat and of wild oats for aboveground biomass per planted seed and per culm, total grain
weight (wheat only) per planted seed and culm and mean seed weight when grown in pure stands and mixtures at different densities in
the presence and absence of wheat leaf rust .
Wheat
Treatment
Year
Biomass
Biomass/
Seed
(g)
(g/m2)
Inoculation
No
1995
Yes
No
1996
Yes
Inoculation
Cultivar
No
Penawawa 1995
Yes Penawawa
No Twin
Yes Twin
Cultivar
No
Penawawa 1996
Yes Penawawa
No Twin
Yes Twin
449.6*
384.2
1034.4
779.5
+
Wild Oats
Biomass/ Total
Total Grain Total Grain Mean Grain
Culm
Grain Wt. Wt./ Seed Wt./ Culm Weight
(g/m2)
(g)
(g)
(g)
(g)
8.47
8.35
164.4*
12.67
25.50
33.06
11.63*
9.73
373.2*
236.0
+
+
+
+
8.82
9.00
8.12
7.73
129.6*
118.3
203.6
153.5
4.16
3.96
6.76*
4.97
3.46
344.2*
236.5
403.6*
235.5
11.02*
7.62
14.75
+
324.0
314.0
10.56
596.3*
464.0
19.54*
+
+
10.56
15.05
135.4
+
889.2
28.41
12.11*
550.8
22.66
1188.8
38.32
10.30
11.22*
870.4
28.30
9.18
Biomass/ Biomass/ Mean Grain
Biomass
Culm
(g)
Seed
(giro
(g)
Weight
(g)
5.43*
4.45
12.11*
3.13
2.96
4.12*
0.0353
0.0349
0.0360*
570.4
574.8
890.8
7.73
2.79
0.0294
869.2
+
+
+
614.4
592.8
526.4
556.8
19.45
7.24
0.0220
17.39
6.86
6.73
6.39
0.0220
0.0225
0.0386*
0.0330
935.6
879.2
27.56
27.67
11.97
0.0204
11.92
0.0196
0.0335*
0.0258
846.0
859.6
25.81
12.65*
0.0210
26.01
11.48
0.0204
+
13.18*
7.78
3.42
2.86
2.56
+
4.54*
3.13
3.76*
2.43
0.0368
0.0370
0.0338
0.0327
17.31
16.48
26.63
26.83
15.21
15.60
6.97
6.62
12.31
11.7
+
+
0.0223
0.0225
0.0207
0.0200
+
0.0230
+
* ANOVA results indicating significant difference (P < 0.05) between inoculated and not inoculated plants within a year and/or within
a cultivar.
+ ANOVA results indicating significant difference (P < 0.05) between cultivars within a year.
51
III-1). Wild oats also responded differently, both vegetatively and reproductively, from
growing with each wheat cultivar. In both years, wild oats plants produced more
biomass in mixtures with the cultivar Penawawa (Table III-1). In contrast, wild oats
seed size was heavier when grown with Twin (Table III-1). There were no significant
inoculation by cultivar interactions for the wild oats response variables in either year (P
< 0.05).
Further analyses, detailed below, were performed at the wheat cultivar level
because of the differences in leaf rust resistance, highly significant cultivar differences,
and the large number of significant inoculation by cultivar interactions. In contrast,
there were very few inoculation by seeding density and inoculation by species
proportion interactions (Table III-1). Additionally, there were few significant density
by proportion interactions. The few significant interactions were for variables described
on a per planted seed basis and in almost all cases the mean square error of the
interaction was ten percent or less than of the main effects mean square error terms.
Therefore, we averaged density over different proportions and proportion over different
densities in the analyses discussed below.
Density effects: Planting density was a consistent and significant factor influencing the
performance of wheat and wild oats (Table 111-2 and 111-3). The responses were either
linear or nonlinear, depending on the parameter measured (Fig. III-1). Shapes of
density response curves were not affected by inoculation for either wheat or wild oats.
However, the response curves for the inoculated wheat were less than or equal to the
noninoculated wheat curves.
A nonlinear response was exhibited when effects of density changes were
measured as aboveground biomass or total seed weight per planted seed (Fig. III-1A,
III-1B, and III-1D), which approximates the response on a per plant basis. This pattern
suggests that the maximum biomass per plant was reached around the middle planting
density of 162 plants per square meter. This nonlinear response was significant and
similar regardless of species or wheat cultivar (Table 111-4).
52
Table 111-2. The means of wheat cv. Penawawa and wild oats for aboveground biomass,
total grain weight (wheat only), and mean seed weight for 1995 and 1996 when grown
in pure stands and mixtures at different planting densities.
Wheat
Treatment
Year
Density
Wild Oats
Biomass Total Seed Mean Seed
(g)
Weight (g) Weight (g)
Biomass
(g)
Mean Seed
Weight (g)
(seeds/m2)
81
1995
162
324
81
324
1995
158.8
0.0368
0.0374
0.0367
603.2
655.6
552.8
0.0207*
0.0218
0.0234
739.8
780.6
852.6
260.9*
268.2
336.4
0.0359
0.0353
0.0362
846.4
975.2
902.4
0.0196*
0.0207
0.0203
94.5*
232.3
34.5*
0.0345*
0.0370
0.0381
0.0381
410.8*
525.6
651.2
827.2
0.0225
0.0214
0.0217
0.0222
0.0311*
0.0342
0.0375
0.0400
464.0*
805.6
1107.2
1254.8
0.0213*
0.0207
0.0192
0.0188
120.6
670.8
90.9
162.7
260.6
287.6*
536.4
998.6
75.3*
181.3
392.8
1645.1
668.9
.415.3
1.00
0.25
0.50
0.75
1.00
93.0*
1996
162
Proportion
0.25
0.50
0.75
229.2*
315.4
421.1
1996
* ANOVA results indicating a significant main effect difference (P < 0.05) within
groups by density or proportion and year.
53
Table 111-3. The means of wheat cv. Twin and wild oats for aboveground biomass, total
grain weight (wheat only), and mean seed weight for 1995 and 1996 when grown in
pure stands and mixtures at different planting densities.
Wheat
Treatment
Year
Biomass
(g)
Wild Oats
Total Seed Mean Seed
Weight (g) Weight (g)
Density
Biomass
(g)
Mean Seed
Weight (g)
(seeds/m2)
468.3*
530.8
591.5
145.1*
1015.1
284.3
162
1064.1
324
991.0
333.6
324.9
194.9*
67.2*
381.0
694.8
1034.3
137.2
81
1995
162
324
81
Proportion
0.25
0.50
0.75
1996
1995
1.00
0.25
0.50
0.75
1.00
1996
470.0*
813.4
1301.8
1737.2
180.6
211.4
222.2
343.8
123.9*
237.3
415.4
573.7
0.0330*
0.0326
0.0342
549.2
517.6
558.0
0.0212*
0.0228
0.0243
0.0294
0.0294
0.0302
786.4
880.8
890.8
0.0202*
0.0205
0.0214
0.0325*
0.0327
0.0333
0.0345
238.4*
483.2
614.8
829.6
0.0235
0.0228
0.0221
0.0227
0.0272*
0.0285
0.0312
0.0317
392.8*
712.0
1032.0
1274.0
0.0220*
0.0214
0.0208
0.0187
* ANOVA results indicating a significant main effect difference (P < 0.05) within
groups by density or proportion and year.
54
/
80
Density effects on wheat
Proportion effects on wheat
80
Penawawa
60
40
C1120
"CI
CP
20
0
50
co
80
100
150
200
250
300
350
80
5 605
r:1:1 40
20
0
50
0
1
100
150
200
250
14
12
10
300
14
12
10
Penawawa
6
50
cd
cf1C4
I
I
100
150
h
200
I
I
250
300
I
80
100
40
60
80
100
40
60
80
100
Penawawa
6
350
14
20
Twin
14
CC
12
10
12
10
8
6
1
50
60
8
tip 8
E
40
20
350
100
150
T
200
1
250
6
I
300
Seeding density (seeds/m2)
350
20
Percent wheat
Figure III-1A. The effects of seeding density and species proportion on wheat
aboveground biomass per planted seed or harvested culm. Two cultivars of wheat (Twin
and Penawawa) were grown separately with wild oats at three seeding densities (81, 162,
and 324 germinable seeds per m2) and at five proportions (100:0, 75:25, 50:50, 25:75,
and 0:100) of wheat:wild oats. Half of the plots were inoculated with Puccinia recondita
(causal agent of wheat leaf rust) while the other half were protected with the fungicide
triadimefon. = 1995, not inoculated. 0 = 1996, not inoculated. = 1995, inoculated.
0 = 1996, not inoculated.
55
Density effects on wheat
Proportion effects on wheat
"C$
CU
1
0
50
444
100
150
200
250
300
20
40
60
80
100
0 20
40
60
80
100
60
80
100
350
Twin
3
C 1:1
"2
3
0 50
100
150
200
6
4
2
250
6
4
2
0
50
350
Penawawa
Penawawa
6
2
0
50
300
100
150
200
0
I
250
300
350
Twin
20
6
40
4
2
I
I
I
100
150
200
0
1
250
300
Seeding density (seeds/m 2)
350
20
I
40
I
I
60
80
Percent wheat
1
100
Figure III-1B. The effects of seeding density and species proportion on total weight of
wheat grain harvested per planted seed or harvested culm. Two cultivars of wheat (Twin
and Penawawa) were grown separately with wild oats at three seeding densities (81, 162,
and 324 germinable seeds per m2) and at five proportions (100:0, 75:25, 50:50, 25:75,
and 0:100) of wheat:wild oats. Half of the plots were inoculated with Puccinia recondita
(causal agent of wheat leaf rust) while the other half were protected with the fungicide
triadimefon. = 1995, not inoculated: = 1996, not inoculated. = 1995, inoculated.
0 = 1996, not inoculated.
56
Density effects on wheat
0.046
Proportion effects on wheat
0.046
Penawawa
0.038
0.038
0.03
0
0
0
0.022
50
100
150
200
1
I
250
I
300
350
0.046
Twin
I
0.038
0.03
T71)
50
cL)
40
150
200
250
300
Seeding density (seeds/m2)
0.03
350
I
20
Density effects on wild oats
40
I
I
I
60
80
100
Percent wheat
Proportion effects on wild oats
Wild oats with Penawawa
0.026
cv
80
0.046
0.022
100
60
0.038
0
0.022
OA
0.03
0.024
0.024
0.022
0.022
0.02
0.02
0.018
Wild oats with Penawawa
0.026
0.018
50
100
150
200
250
300
Wild oats with Twin
0.026
20
350
60
80
100
80
100
0.026- Wild oats with. Twin
0.024
0.024-
0.022
0.022-
0.02
0.02
0.018
40
0.018
50
100
150
200
250
300
Seeding density (seeds/m2)
350
20
40
60
Percent wild oats
Figure III-1C. The effects of seeding density and species proportion on mean seed
weight of wheat or wild oats. Two cultivars of wheat (Twin and Penawawa) were grown
separately with wild oats at three seeding densities (81, 162, and 324 germinable seeds
per m2) and at five proportions (100:0, 75:25, 50:50, 25:75, and 0:100) of wheat:wild
oats. Half of the plots were inoculated with Puccinia recondita (causal agent of wheat leaf
rust) while the other half were protected with the fungicide triadimefon.
= 1995, not
inoculated. = 1996, not inoculated. = 1995, inoculated. 0 = 1996, not inoculated.
57
Density effects on wild oats
60
Proportion effects on wild oats
Wild oats with Penawaw.
60
40
40
20
20
0
Wild oats with Penawawa
0
50
100
150
200
250
300
20
350
60
60
40
40
20
20
0
I
50
100
I
150
I
200
I
I
250
300
I
60
80
100
80
100
Wild oats with Twin
0
20
350
Wild oats with Penawawa
15
40
40
60
Wild oats .with.Penawawa.
10
Si
5
0
50
caD
v)
100
150
200
250
300
Wild oats with. Twin
15
20
350
40
60
80
100
Wild oats with Twin
15
0:1
10
0 10
1:4
5-
5
0
50
I
100
I
150
I
200
I
250
I
300
Seeding density (seeds/m2)
i
350
0
20
i
40
I
60
I
80
Percent wild oats
i
100
Figure III-1D. The effects of seeding density and species proportion on wild oats
aboveground biomass per planted seed or harvested culm. Two cultivars of wheat (Twin
and Penawawa) were grown separately with wild oats at three seeding densities (81, 162,
and 324 germinable seeds per m2) and at five proportions (100:0, 75:25, 50:50, 25:75,
and 0:100) of wheat:wild oats. Half of the plots were inoculated with Puccinia recondite
(causal agent of wheat leaf rust) while the other half were protected with the fungicide
triadimefon.
= 1995, not inoculated.
= 1996, not inoculated. = 1995, inoculated.
0 = 1996, not inoculated.
Table 111-4. Significance of components from linear and nonlinear regression models using wheat and wild oat response variables.
The two wheat cultivars and wild oats were grown in pure stands and mixtures at different proportions and planting densities. The
regressions used the data plotted in Figure III-IA-D. P-values less than 0.05 are shown in bold.
Wheat
Cultivar
Year
Penawawa 1995
Penawawa 1996
Twin
Twin
1995
1996
Penawawa 1995
Penawawa 1996
Twin
Twin
1995
1996
Penawawa 1995
Penawawa 1996
Twin
1995
Twin
1996
Response'
Variable
Biomass/seed
Intercept' Density3
Proportion'
Intercept' Density3
Proportion'
Disease Linear' Nonlinear' Linear' Nonlinear' Disease Linear' Nonlinear' Linear' Nonlinear'
Effect
Slope
Slope
Effect
Slope
Slope
0.9995
0.1334
0.0160
0.1316
0.0001
Biomass/culm
Biomass/culm
Biomass/culm
Biomass/culm
0.5236
0.0414
0.0639
0.0121
0.0001
Total wt/seed
Total wt/seed
Total wt/seed
Total wt/seed
0.6540
0.0243
0.0021
0.0001
0.0172
Biomass/seed
Biomass/seed
Biomass/seed
Wild Oats
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.5654
0.0929
0.0067
0.4260
0.0025
0.0050
0.6442
0.2182
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0001
0.0207
0.0001
0.9438
0.4645
0.7536
0.4230
0.2866
0.8943
0.7677
0.9160
0.0903
0.0001
0.0003
0.0001
0.4410
0.3014
0.9359
0.2043
0.0405
0.0001
0.0001
0.0001
0.8171
0.9067
0.6042
0.9169
0.9149
0.8380
0.2386
0.0001
0.0001
0.0001
0.8655
0.0001
0.0001
0.0001
0.0001
0.0001
0.0036
0.5105
0.1498
0.0002
0.4780
0.4944
0.9073
0.6393
0.3867
0.6529
0.3462
0.0001
0.5218
0.6340
0.0191
0.5660
Table III-4
cont.
Penawawa 1995
Penawawa 1996
Twin
1995
Twin
1996
Penawawa 1995
Penawawa 1996
Twin
Twin
1995
1996
Total wt/culm
Total wt/culm
Total wt/culm
Total wt/culm
0.9092
0.0164
Seed weight
Seed weight
Seed weight
Seed weight
0.0001
0.0059
0.4632
0.2089
0.3351
0.8435
0.1073
0.1896
0.4613
0.6815
0.2831
0.8827
0.0001
0.0002
0.1470
0.0040
0.4552
0.0287
0.0940
0.1911
0.0001
0.2208
0.5983
0.0050
0.0001
0.0763
0.0001
0.0001
0.5412
0.0001
0.1010
0.1004
0.5037
0.5975
0.0806
0.8953
0.6763
0.1543
0.9454
0.0001
0.3312
0.2076
0.1592
0.1633
0.0001
0.0021
0.7037
0.5094
0.7928
0.2762
0.0001
0.3093
0.8046
0.1226
0.0001
0.5648
0.3284
0.0760
'Response variables - Biomass/seed = aboveground biomass per seed planted, Biomass/culm = aboveground biomass per harvested
culm, Total wt./seed = total harvested grain weight per seed planted, Total wt/culm = total harvested grain weight per harvested
culm, and Seed weight = mean weight per seed.
2 Intercept disease effect - P values determining the significance in the difference between y-intercepts of regressions of inoculated
and not inoculated wheat plants.
3 Density - Seeds were planted at three densities of 81, 162, and 324 seeds per m2.
4 Proportion - Wheat and wild oats were planted at five different proportions of 0:100, 25:75, 50:50, 75:25, and 100:0 (wheat:wild
oats).
5 Linear slope - P-values to determine if the slope was significantly different than zero for linear regression model combining both
inoculated and not inoculated treatments.
6 Nonlinear slope - P-values to determine if there is a significant nonlinear component to the regression model of combined inoculated
and not inoculated treatments.
60
In contrast, a linear response to planting density occurred for aboveground
biomass and total seed weight per harvested culm, and by mean seed weight. With both
wheat and wild oats, there was a decrease in aboveground biomass per culm with
increasing density, suggesting that the plants responded to increasing density with
smaller culms (Fig. III-1A and -1D). Wheat mean grain weight and total grain weight
per culm did not respond to seeding density (Fig. III-1C and -1B, Table 111-4). In
contrast, the mean seed weight of wild oats responded positively to increased density
(Fig. III-1C, Table 111-4). Unfortunately, data on the number of seeds from each wild
oats plant were not obtained due to seed dehiscence making total seed harvest
impossible.
Proportion effects: Species proportion was consistently the most significant treatment,
regardless of species, cultivar, or year. As expected, increasing the proportion of wheat
or oats in mixtures lead to an increase in the amount of aboveground biomass and total
seed weight (measured in wheat only) for that species (Tables 111-2 and -3). It also led to
an increase in wheat mean seed weight regardless of cultivar, but a decrease in wild oats
mean seed weight (Tables.III-2 and -3).
A linear pattern existed for all proportion response variables measured regardless
of species, cultivar, or year (Table 111-4, Fig. III-1 A-D). There was very little evidence
to support any nonlinear component to these patterns (Table 111-4). However, while
wheat increased in response to proportion on a per seed planted or per harvested culm
basis (Fig. III-1A and -1B), wild oats decreased or had no change (Fig. III-1D). A
similar pattern holds for mean grain weight for each species (Fig. III-1C).
Interactions of inoculation with density or proportion: Generally, there were no
significant interactions of inoculation with density or proportion for the response
variables measured in either year. The shapes of the density and proportion effects
curves for wheat did not change in response to the inoculation treatment, but the
positions of the curves were generally lower for inoculated plants (Fig. III-1A, -1B, and -
61
1C). The intercepts were significantly different in many cases, especially in 1996 (Table
111-4).
In contrast, wild oats did not appear to respond to the inoculation treatment,
except for the wild oats grown with Twin in 1996 (biomass per culm) (Table 111-4, Fig.
III-1D). The parallel nature of the lines is also an indication of the lack of interaction
between inoculation and density or proportion (Fig. III-1A, -1B, -1C, and -1D).
Relative biomass: Deviations of observed relative biomass from expected relative
biomass calculated from wheat and wild oats monoculture data indicate that wheat was
very often less competitive than wild oats when grown in mixtures (Fig. 111-2). In
addition, there was a consistent and statistically significant difference in cultivar
performance in the presence of wild oats, with Twin being relatively more competitive.
Sometimes Twin out-performed wild oats, but rarely did Penawawa, except when
inoculated in 1995 (Figure III-2A). Penawawa produced less biomass while wild oats
produced more biomass in these interactions when compared to monoculture data. In
contrast, there were no consistent patterns in the changes in biomass with interactions
between Twin and wild oats, indicating that Twin was closer competitively to wild oats
than Penawawa. However, the expected relative biomass values indicate that wheat
would have been more productive than wild oats (values > 1) in the absence of
interspecific competition especially in 1996.
In only one case (Twin 1996) did inoculation significantly reduce the relative
biomass of wheat (Fig. III-2B). Inoculation seemed to have little consistent effect on
interspecific competition (difference between the expected and observed relative
biomass), as the expected relative biomass was usually lower in response to inoculation,
compensating for lower relative biomass values. However, when disease had an effect
(Fig. 111-2), it was caused by greatly lowered wheat performance while only a slight
impact on wild oats performance; this was most notable in 1996 with Twin (Table III-1).
In 1995, the expected relative biomass increased with density for the non-
inoculated plots, while the lowest planting density had a lower expected value than the
higher densities for the non-inoculated plots (Fig. 2A). In contrast, there was no change
62
1995
2
I 162 I
1-- 81
Twin
I
81
-H
I 324 I
81
Inoculated
1 162
-1
I
I 162
Penawawa
324 -H
1 81
I-162
H 324 I
Inoculated
324 --d
Density (seeds m-2)
Figure III-2A. The relative biomass of wheat (biomass per planted seed of wheat /
biomass per planted seed of wild oats) in relation to seeding density and species
proportion for 1995. Two cultivars of wheat (Twin and Penawawa) were grown
separately with wild oats at three seeding densities (81, 162, and 324 germinable seeds
per m2) and at five proportions (100:0, 75:25, 50:50, 25:75, and 0:100) of wheat:wild
oats. Half of the plots were inoculated with Puccinia recondita (causal agent of wheat leaf
rust) while the other half were protected with the fungicide triadimefon. = 25 percent
wheat. = 50 percent wheat. El = 75 percent wheat. The horizontal lines are the relative
biomass ratios expected in the absence of interspecific competition, based on the
performance of wheat and wild oats in monocultures. Error bars are the pooled standard
errors of the mean for each density within year, cultivar, and inoculation.
63
1996
2
Not Inoculated
Penawawa
/ L5
O
0.5
Cd
a1
I-- 162 1
a
I- 32A
f 162 I
1 324 1
C/)
Twin
Inoculated
Penawawa
O
Inoculated
L5
Cd
71)
0.5
0.5
I- 162 -I
I- 324
H
81 I
162 H
13241
Density (seeds m-2)
Figure III-2B. The relative biomass of wheat (biomass per planted seed of wheat /
biomass per planted seed of wild oats) in relation to seeding density and species
proportion 1996. Two cultivars of wheat (Twin and Penawawa) were grown separately
with wild oats at three seeding densities (81, 162, and 324 germinable seeds per m2) and
at five proportions (100:0, 75:25, 50:50, 25:75, and 0:100) of wheat:wild oats. Half of
the plots were inoculated with Puccinia recondita (causal agent of wheat leaf rust) while
the other half were protected with the fungicide triadimefon./ = 25 percent wheat.
=
50 percent wheat. Ea = 75 percent wheat. The horizontal lines are the relative biomass
ratios expected in the absence of interspecific competition, based on the performance of
wheat and wild oats in monocultures. Error bars are the pooled standard errors of the
mean for each density within year, cultivar, and inoculation.
64
in expected relative biomass in response to density in 1996 for inoculated and for
noninoculated plants (Fig. III-2B). The values of observed relative biomass showed
inconsistent responses to seeding density. Also, changing the proportion of a species in
mixture did not seem to have any consistent effect on relative biomass (Fig. 111-2).
Discussion
The most striking finding of this study was the almost total lack of interaction of
disease with density or proportion. One might expect that increases in density would
result in decreased resource availability for plant growth, producing plants more
vulnerable to disease. However, this is often not the case with biotrophic pathogens,
which require living hosts for infection, and those hosts with the greatest resources are
quite often the most vulnerable (Agrios 1988). Also, grass species are very plastic, and
when grown at low densities, they respond with increased tillering so that final biomass
per unit area does not have a one to one response to changes in planting density.
Increasing the proportion of the non-host changed host density more effectively than
changes of planting density in monocultures. This results in species proportion having a
greater effect on disease progression than seeding density (Pfleeger and Mundt 1998).
The temporal segregation of disease progression from density and proportion effects
could explain the lack of significant interactions. Plants had likely already responded to
density and proportion influences prior to inoculation. A similar temporal separation
was found between wheat leaf rust and ozone exposure (Pfleeger et al. 1998). However,
Finckh and Mundt (1996) found that competition between genotypes can occur both
early and late in the growth cycle of wheat.
Seeding density rarely had a significant influence on rust severity in these plots
(Pfleeger and Mundt 1998). This was probably because compensation due to increased
tillering at low seeding densities had essentially diluted any density effect. In contrast,
increasing the proportion of wheat in mixtures consistently increased wheat leaf rust
severity, in a linear relationship. In addition, there was no evidence to suggest that wild
65
oats in mixtures acted as an inoculum barrier that significantly decreased disease
severity though such evidence could have been masked by the large amount of
variability in the data (Pfleeger and Mundt 1998). This suggests that wild oats had little
effect on disease severity other than to occupy space and effectively change the density
of wheat.
Most of the plant responses to density and proportion were approximately linear
when expressed on either a per seed planted or on a per culm basis. Some of the
responses showed non-linear curves suggesting that the functions had 'plateaued' with
increasing density Claw of constant final yield', Kira et al. 1953). However, these
responses consistently decreased from the middle to the highest density (Figure III-1).
This might indicate that, at the highest seeding rate, there were insufficient resources to
support plants at the same level of productivity as the middle seeding rate. In addition,
`the law of constant yield' is based on a unit area and not on a per plant or stem basis
(Kira et al. 1953). The highest seeding level we used is the recommended rate for
commercial wheat production in the Willamette Valley of Oregon. Growers may be able
to attain high productivity at lower rates but prefer not to for other reasons, such as weed
control through space preemption or uniformity of ripening, which is facilitated by
having even-aged tillers resulting from fewer tillers per plant in crowded stands.
Our data indicate that wild oats was usually more competitive than wheat (Figure
111-2). Wheat responded positively to increasing wheat proportion for all per plant and
per culm measures of productivity and reproduction. In contrast, wild oats either
decreased or showed no response to increasing wild oats proportion. These results are
consistent with wheat experiencing decreased interspecific competition from wild oats as
wheat proportion increased, and wild oats experiencing increased intraspecific
competition as its proportion increased. Further, relative biomass was usually greater for
wild oats than for wheat, and most of these differences were accounted for by
interspecific competitive effects, rather than innate productivity differences found in
monocultures. Wild oats has been determined to be the stronger competitor in several
66
previous studies (Carlson and Hill 1985a; Martin and Field 1988; Cousens et al. 1991),
but not in all (Cudney et al. 1989).
Since wheat and wild oats are closely related and morphologically similar, the
outcome of their interactions could depend on site-specific environmental conditions. In
fact, the cultivar Twin was sometimes more competitive than wild oats. Competitive
interactions changed in favor of wheat when wild oats emergence was delayed (Martin
and Field 1988), or when exposed to increased ultraviolet-B radiation (Barnes et al.
1988). However, the opposite occurred with the addition of nitrogen fertilizer or water
(Carlson and Hill 1985b). Competitive relations have also been reported to change
within a single growing season, with wheat being the more competitive species until flag
leaf emergence (Cousens et al. 1991).
We expected the relative biomass of wheat / wild oats to decrease in the presence
of rust, but this occurred only for the cultivar Twin and only in 1996. One explanation
might be the biology of the pathogen, which has higher rates of infection when nighttime
temperatures are above 15° C. The epidemic is, therefore, generally limited to the later
stages of the plant's life cycle, where it leads to reduced yields (Samborski 1985) (Table
III-1) but not necessarily culm number (Pfleeger, unpublished data). While overall size
(height and culm number) of a wheat plant may not be diminished by this disease,
increased respiration rates and maintenance costs would decrease the amount of carbon
available to be fixed in plant tissues. This scenario is also beneficial to the pathogen, as
its own fitness is increased when more plant surface area is available for infection.
We, also expected wheat would escape interspecific competition from wild oats
as wheat proportion increased. However, only non-inoculated Penawawa in 1996
demonstrated this pattern (Fig. III-2B). Otherwise, the expected pattern did not
materialize because; 1) wild oats and wheat biomass were both increasing with
increasing wheat proportion so the differences between them was relatively constant
(Fig. III-1A, and -1D), and 2) relative biomass is a ratio of two numbers and their
variances when combined increases.
67
Numerous indices have been developed to analyze data from competition studies
(Mead and Riley 1981). Generally, these indices are various manipulations of plant
performance with and without interspecific competition. For example, the relative
crowding coefficient is defined as the (performance of species A when grown with
species B / performance of species A in monoculture) divided by (the performance
species B with species A / performance of species B in monoculture). Aggressivity can
be expressed similarly, except that the two ratios are subtracted rather than divided
(Snaydon 1991). Another approach has been to use regression analysis, both linear
(Spitters 1983) and non-linear (Watkinson and Freckleton 1997; Cousens 1985). These
approaches all have advantages and disadvantages which have been debated extensively
in the literature (Freckleton and Watkinson 1997; Markham 1997). We tried different
approaches and found some of them, such as the indices, biologically unclear and others,
such as the regressions, to require assumptions that we could not evaluate. For example,
the value for b in Watkinson and Freckleton's (1997) non-linear regression model 'in
many cases typically has a value of approximately unity.' However, we are unaware of
a biological basis for such a value. We thus chose instead to use relative biomass
because it is a straightforward measure and requires few assumptions.
Deviations of relative biomass from 1.0 could be caused by differences in innate
productivity and/or competitive ability. Calculation of expected values based on
biomass production in monocultures allowed us to separate these two effects. For
example, the relative biomass of non-inoculated Penawawa in 1995 was substantially
greater at the highest than at the other two densities, but this was due almost entirely to
increased productivity of wheat at high density rather than differences in competition
among the three densities (Fig. III-2A). In contrast, there were many other situations
where deviations of relative biomass from 1.0 were due mostly to interspecific
competition.
We calculated relative biomass in a manner analogous to relative fitness, which
is the relative number of descendants left to future generations by one form compared
with others (Harper 1977). It is difficult to measure fitness directly, especially because
68
of the short length of most experiments. Some of the most common measures of fitness
are seed parameters. Unfortunately, wild oats seed is dehiscent, preventing its
collection, and, therefore, aboveground biomass was measured. However, in the future,
it may be possible to determine the number of wild oats seeds by developing a
correlation between the number of glumes remaining on the panicle and number of seeds
present prior to dehiscence. If larger plants of the same species produce more and better
quality seed than smaller plants, seed from larger plants would have a higher relative
fitness. This assumption is supported by the proportion data presented in Figure III-1,
where mean seed weight tracks aboveground biomass whether biomass either increases
or decreases in response to proportion. In addition, heavier wild oats seeds produced
more competitive plants when grown with barley (Peters 1988). Nonetheless, fitness
effects need to be studied directly to make definitive conclusions.
Statistically significant differences were found between inoculated and protected
plants for wheat total grain weight and seed weight (Table III-1). Jarosz et al. (1989)
demonstrated that wheat seed quality also decreased following disease epidemics and
reduced fitness of wheat for at least for two disease-free years. If such processes are
occurring in natural systeths, such as reported by Jarosz and Burdon (1992), then the
ecological importance of pathogens in natural systems may have been underestimated.
The growth responses that wild oats demonstrated when grown with different
wheat cultivars (Table III-1) suggests that genetic diversity within a species can affect its
neighbors. It is widely known that test species used in standard toxicology tests, such as
done under FIFRA (Federal Insecticide, Rodenticide, and Fungicide Act), lack the
natural genetic diversity of the field (Fletcher et al. 1990). However, the secondary
responses to this diversity on interspecific interactions is generally unknown or rarely
considered (Pfleeger and Zobel 1995). These findings further strengthen the need to
evaluate FIFRA laboratory tests for relevance to the field.
Attention has increased concerning the impact of plant pathogens on the structure
of natural plant communities (Gilbert and Hubbell 1996; Burdon 1993). Unfortunately,
most experiments, including this one, have used agricultural models to try to explain
69
what may be occurring in natural systems. This is because natural systems are complex,
difficult to manipulate, and most pathogens in natural systems are not well known or
understood. Many pathogens have complex life cycles and mainly those with economic
importance have been described in enough detail to make manipulative experiments
possible. In addition, there is a need for multi-generational investigations on the effects
that pathogens have on natural plant communities.
70
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combination. Plant Pathology 44:173-182.
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of different weights. Weed Research 25:67-77.
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ozone and wheat leaf rust in wheat swards. Environmental and Experimental
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interactions in an annual plant communities. Ecotoxicology 4:15-37.
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74
IV. LACK OF INTERACTION BETWEEN OZONE AND WHEAT LEAF RUST
IN WHEAT SWARDS
T.G. Pfleeger, M. A. da Luz and C. C. Mundt
T.G. Pfleeger, U.S. Environmental Protection Agency, Western Ecology Division, 200
SW 35th St., Corvallis, OR 97333.
M. A. da Luz, Dynamac Corp., 200 SW 35th St., Corvallis, OR 97333.
C. C. Mundt, Department of Botany and Plaiit Pathology, 2082 Cordley Hall, Oregon
State University, Corvallis, OR 97331-2902.
The information in this article has been funded by the U.S. Environmental Protection
Agency. It has been subjected to the Agency's peer and administrative review, and it
has been approved for publication as an EPA document. Mention of trade names or
commercial products does not constitute endorsement or recommendation for use.
Accepted for publication 00 00000 0000.
75
Abstract
Pfleeger, T.G., M.A. da Luz, and C.C. Mundt. 1998. Lack of interaction between ozone
and wheat leaf rust in wheat swards. Environmental and Experimental Botany.
submitted.
Ozone is an air pollutant regulated in the United States under the Clean Air Act.
Increasingly, concerns have been regarding the interactions between ozone and pests,
pathogens, and plant competition. This study was conducted to increase our
understanding of plant responses to ozone in the presence of pathogens, and specifically
to determine the effect that wheat leaf rust and ozone exposure had on wheat productivity.
The study was conducted in open-top ozone exposure chambers in Corvallis, Oregon,
using two cultivars of spring wheat (Twin and Yecora Rojo). Twin was grown at two
densities. Two levels of ozone and three levels of disease were applied in all
combinations, for a total of six treatments. Treatments were replicated twice and
repeated over two years. Disease severity readings were taken three or four times during
each growing season. At the completion of grain-fill, the plants were removed from the
chambers, and were harvested. Wheat height and aboveground biomass generally
decreased with ozone exposure and with increasing disease severity in both years, while
total grain weight decreased significantly only with disease and only in 1997. There was
no interaction between ozone and disease, regardless of cultivar, density, or plant
response variable measured. There was little evidence that ozone exposure affected the
severity of wheat leaf rust.
76
Introduction
Ozone is an air pollutant regulated in the United States under the Clean Air Act.
National Air Quality Standards have been established for ozone levels and are
periodically updated to protect public health and welfare (US EPA 1996). The protection
afforded plants under the Clean Air Act has been derived mainly from single species tests
(US EPA 1996). Increasingly, there have been concerns that isolated, single species
testing may not be an adequate representation of the effects of ozone on plant
communities and ecosystems (Woodward 1992; Evans and Ashmore 1992). Similar
concerns have been raised about the interactions between ozone effects and pests,
pathogens, and plant competition (US EPA 1996).
The 1986 US EPA Air Quality Criteria for Ozone and Other Photochemical
Oxidants Report suggested the need for more complex experiments designed to
investigate the effects of ozone and biotic stressors. Since then, experiments with ozone
and competition, herbivory, and pathogenicity are beginning to show a complex series of
interactions (EPA 1996). For example, with plant pathogens, three generalizations have
been made. First, whether a disease is influenced by ozone depends on the timing of
pollutant exposure and the infective periods of a particular host/pathogen combination
(Dowding 1988). Second, obligate parasitism is generally inhibited by air pollutants
(Heagle 1982; Dowding 1988), while infection by facultative pathogens can result in
increases, decreases, or no change in host responses to pollutants (Heagle 1982; Manning
and Keane 1988). Third, it is impossible to predict effects based on our present
knowledge (Heagle 1982; US EPA 1996; Manning and Keane 1988).
The present study was conducted to increase our understanding of plant responses
to ozone in the presence of pathogens. Specifically, the objective was to determine the
effect of wheat leaf rust and ozone exposure on wheat productivity. The wheat / wheat
leaf rust model was chosen because it is well understood, wheat is one of the most
important food crops globally, and leaf rust occurs worldwide (Samborski 1985). In
addition, this model system has a relatively short life cycle, making replication easier.
77
We hypothesized that the effects of ozone and disease exposure in combination would be
greater than each stress individually, and there would be an interaction between the two
stressors.
Materials and Methods
The study was conducted at the U.S. Environmental Protection Agency
Laboratory using the Field Ecological Research Facility (FERF) in Corvallis, Oregon
(Hogsett et al. 1985). Two commercial, economically important cultivars of spring wheat
(Triticum aestivum L. cv. Twin and Yecora Rojo) were grown separately in 43-cm
diameter pots containing 0.04 m3 of artificial soil (50:50, peat:perlite) supplemented with
100 g of Osmocote fertilizer (14-14-14). Both Yecora Rojo and Twin are susceptible to
wheat leaf rust (caused by Puccinia recondita f. sp. tritici). Yecora Rojo was selected
because it was commercially grown in California under relatively high ambient ozone
conditions and, therefore, may be more tolerant of ozone. Twin was selected because it is
commercially grown in the Willamette Valley of Oregon and therefore adapted to the
environmental conditions at FERF. Twin was seeded at two densities of 81 and 323
seeds ni2 (12 and 44 plants per pot) so the effects of density on disease progression could
be investigated under ozone exposure scenarios. The recommended seeding rate for
wheat in Oregon is 323 seeds m-2. Yecora Rojo was seeded only at the higher density
because of limited chamber space. Seeding emergence rate ranged from 84-100 percent.
Pots with less than 100 percent emergence had same-age plants transplanted into them at
the two-leaf stage so that all pots of the same planting density had the same number of
plants.
Wheat plants were germinated and grown for approximately one month in a
heated glasshouse. The plants were surface-irrigated with tap water. Of the initial 120
pots, 108 pots containing the most uniform plants (based on general appearance and
number of plants) by cultivar and density were selected for experimentation. The selected
pots were transferred to the FERF open-top ozone exposure chambers (Hogsett et al.
1985).
78
The experiment was arranged in a split-plot design with four factors. At the
whole plot level, two levels of ozone (ozone at 90 ppb and no ozone) and three levels of
leaf rust disease (high disease, low disease, and no disease) were investigated in all
combinations. Twelve chambers were used, with each containing nine pots. A
completely randomized design was used to assign the six treatment combinations to the
twelve chambers, allowing for two replications of each treatment level. Each chamber
contained three combinations of two factors, each at two levels: cultivar (Twin and
Yecora Rojo) and planting density (low and high). The Yecora Rojo-low density
combination was excluded. Three pots of each cultivar and density were randomly
located inside each chamber. Pots selected for each chamber represented the variation in
plant size and number found in the total population. The plants were allowed to
acclimate for two to three days prior to the start of the ozone exposure. An exposure
regime of 90 ppb episodic ozone was used (Lefohn et al. 1986). The daily level of ozone
varies (0-155 ppb) under this exposure regime.
Wheat plants were inoculated at flag leaf emergence with a mixture of talc powder
and uredospores of P. recondita collected previously from greenhouse-inoculated wheat
plants. The inoculation was done on a day when there were low levels of ozone exposure
by shaking over the plants the uredospore mixture contained within several layers of
cheese cloth. In 1996 (April 5), wheat in the high disease treatments was inoculated at a
rate of 2,807 g/ha of urediospores, while the low disease treatments received a rate of 175
g/ha. In 1997 (April 10), both inoculation rates were doubled to increase the initial level
of disease. Successful infection requires greater than four hours of free moisture (Rowell
et al. 1958). Because the exposure chambers prevented significant dew formation, each
chamber was misted with a fine spray for approximately 30 seconds and the plants were
covered overnight with plastic sheeting.
In addition, a heated vaporizer was added to
each chamber during the initial inoculation period to maintain optimal overnight
humidity and increase the temperature. Non-inoculated chambers received the same
treatment, minus the uredospores. Approximate weekly, reinoculation occurred in the
late evening by shaking the wheat leaves to disseminate the uredospores within the
chambers, followed by misting with water. During all inoculation events the chamber
79
blowers remained off overnight. All twelve chambers were treated similarly except for
the various amounts of uredospores.
Disease severity readings were taken three or four times during each growing
season by estimating the percent of leaf area covered by pustules and associated chlorosis
on the flag leaf. Three leaves from each pot were randomly selected for reading. All
disease readings were performed by the senior author. Prior to each assessment, he was
retrained using the DISTRAIN computer program (Tomerlin and Howell 1988)
At completion of grain-fill, the plants were removed from the chambers to a
greenhouse where they remained for approximately two weeks to dry. The aboveground
biomass was then harvested and air-dried to constant weight. Total weight, number of
stems, and mean length of the stems for each cultivar per pot was measured. Stem length
was measured from just above the soil-mix to the top of the fruiting head. Awns were not
included in the height measurements. In addition, grain yield (total seed weight per pot)
and mean seed weight were determined after threshing with a portable stationary thresher.
Disease readings were recorded and plotted over time to form disease progression
curves. The area under each disease-progression curve was calculated (Shaner and
Finney 1977) by pot and subjected to analysis of variance (ANOVA) using the Statistical
Analysis System (SAS Institute Inc., Cary, NC). ANOVAs were performed using the
SAS general linear models procedure (PROC GLM) and Dunnett's multiple comparison
procedure was used to determine if the area under the disease progression curves for each
treatment was significantly less than the area of the high disease, no ozone treatment
curve (P = 0.05).
Harvest data were entered directly into a data logger (MC-V) and transferred to a
computer spreadsheet for preliminary statistical measures. Final statistical analyses were
performed using SAS. ANOVAs were performed using the SAS PROC GLM procedure
and Dunnett's multiple range test was used to determine if treatment means for yield
responses were significantly less than controls (no ozone or disease) (P = 0.05). Simple
mean comparisons for cultivar differences at low density and for density differences for
the cultivar Twin were tested using the F-test with the split-plot mean square error term in
the denominator. Interactions between the whole plot treatment factors (ozone and
80
disease) and the split-plot factors (cultivar and planting density) were also tested against
the split-plot mean squared error term. The assumptions of ANOVA were checked with
PROC UNIVARIATE and residual plots. Transformations were not necessary.
Results
For both years, ozone exposure and disease decreased wheat height (0 to 17
percent less than controls) and aboveground biomass (8 to 52 percent less than contols).
(Table IV-1A and IV-1B, Fig. IV-1A and IV-1B). In contrast, total grain weight was
significantly decreased by ozone exposure and disease only in 1997 (34 to 81 percent less
than controls) (Table IV-1C, Fig. IV-1C). Ozone exposure and leaf rust disease were
additive in their effect and showed no evidence of a synergistic interaction (Fig. IV-I).
There were no ozone by disease interactions in either year or with any response variable
(Table IV-1). The parallel nature of the plots of plant response versus disease level for
ozone or no ozone treatments further illustrates that ozone exposure and leaf rust disease
did not interact (Fig. IV-2). In contrast, disease level often interacted significantly with
cultivar and planting density, especially when measured by aboveground biomass and
grain weight (Table IV-1B and IV-1C).
Aboveground biomass was the most sensitive measure of plant response to stress,
with all treatments in 1996 being significantly less than the controls for mean response
(17 to 36 percent less than controls) and for Twin grown at low density (26 to 43 percent
less than controls) (Fig. IV-1B). A similar pattern was present in 1997. In contrast, the
biomass of the Yecora Rojo treatments were not significantly affected in 1996, although a
statistically significant decrease was present in 1997 with ozone and disease (19 to 42
percent less than controls) (Fig. IV-1B). The cultivar Twin at high density was most
consistently impacted by stress, with the combined ozone and disease treatments having
39 percent less biomass than the controls in 1996 and 45 percent less in 1997.
Table IV-1A. Analysis of variance for stem height (A), aboveground biomass (B), and grain weight (C) of two spring wheat cultivars
(Twin and Yecora Rojo) grown in pure stands. Twin was grown at two densities (81 and 323 seeds nr2) and Yecora Rojo was grown
at one density (323 seeds m-2).
(A) Height
Source of Variation
Ozone
Disease
Ozone X Disease
Chamber
Disease X Cultivar
Disease X Density
Ozone X Cultivar
Ozone X Density
Ozone X Disease X Cultivar
Ozone X Disease X Density
Yecora Rojo vs. Twin High
Twin High vs. Twin Low
1996
DF ms
1997
p__?_F
pi. ms.
1997t
p__>/:
DE ms
1
65008.4 0.0023*
18878.7 0.0236*
1391.8 0.6034
2529.6 0.4314
8650.5 0.0374*
1205.2 0.6229
3582.8 0.2375
11428.2 0.0365*
906.2 0.7002
1169.9 0.6315
1920310 0.0001*
33977.9 0.0004*
Error
84
2531.5
Total
107
107
98
R2
0.92
0.94
0.94
1
2
2
6
2
2
1
1
2
2
1
1
20148.8 0.0669
19190 0.0580
468.9
0.8923
4039
0.0003*
231.1
0.7612
1467.2 0.1821
196.8
0.6304
5058.5 0.0164*
967.1
0.3229
5995.4 0.0014*
764270.4 0.0001*
133.1
0.6923
84
844
1
2
2
6
2
2
1
1
2
2
1
p>i
1
27742.8 0.0307*
23511.0 0.0309*
222.0
0.9322
3117.6 0.0052*
602.9
0.4971
746.0
0.4219
0.2800
0.9856
3125.6 0.0596
972.5
0.3258
3709.2 0.0164*
675414.2 0.0001*
0.5505 0.9798
76
854.6
1
2
2
5
2
2
1
1
1
2
1
.
Table IV-1 cont.
(B) Aboveground Biomass
Source of Variation
Ozone
Disease
Ozone X Disease
Chamber
Disease X Cultivar
Disease X Density
Ozone X Cultivar
Ozone X Density
Ozone X Disease X Cultivar
Ozone X Disease X Density
Yecora Rojo vs. Twin High
Twin High vs. Twin Low
1996
DF MS
P>F
1997
DF MS
P>F
1997t
DE MS P >F
1
172765.2
161058.6
3133.6
6071.7
33000.2
4248.1
21492.4
0.751
9567.6
10428.2
1264923
98550.6
Error
82
5963.1
Total
107
107
98
RZ
0.81
0.91
0.92
1
2
2
6
2
2
1
1
2
2
1
0.0018*
0.0010*
0.6211
0.4194
0.0056*
0.4935
0.0612
0.9911
0.2072
0.1804
0.0001*
0.0001*
1
193548 0.0632
290122.1 0.0217*
3482.6 0.9123
37383.1 0.0001*
48785.5 0.0001*
16454
0.0080*
87.3
0.8391
6811.4 0.0757
691.6
0.7211
4429.8 0.1285
600735.9 0.0001*
201178.8 0.0001*
84
2106.5
1
2
2
6
2
2
1
1
2
2
1
1
1
2
5
2
2
1
1
2
2
1
1
76
282163.5 0.0178*
339897.8 0.0082*
4903.3 0.8175
23373.4 0.0001*
55110.1 0.0001*
13369.6 0.0014*
1373.8 0.3942
6391.1 0.0685
1087.8 0.5616
4343.1
0.1051
582616.9 0.0001*
175387.5 0.0001*
1871.3
Table IV-1 cont.
(C) Grain Weight
Source of Variation
Ozone
Disease
Ozone X Disease
Chamber
Disease X Cultivar
Disease X Density
Ozone X Cultivar
Ozone X Density
Ozone X Disease X Cultivar
Ozone X Disease X Density
Yecora Rojo vs. Twin High
Twin High vs. Twin Low
1996
DE MS
1
2
2
6
2
2
1
1
2
2
1
1
1997
r__?F_
0.6503
0.3120
0.4764
0.0047*
0.0027*
21314.7 0.0009*
14796.5 0.0242*
800.6
0.5947
11342.3 0.0211*
4409.4 0.2139
280193 0.0001*
276481.5 0.0001*
2170.1
13583.1
8028.3
9543.3
17807.2
DF MS
1
61526.1
157249.8
2803.1
22727.0
23448.3
9124.8
1302.8
9981.9
4.3
1153.5
12986.1
78154.2
84
1201.8
1
2
2
6
2
2
1
1
2
2
1
P>F
0.1510
0.0277*
0.8862
0.0001*
0.0001*
0.0009*
0.3008
0.0050*
0.9964
0.3871
0.0015*
0.0001*
1997t
DE MS
1
127341.0 0.0033*
213812.8 0.0006*
14244.6 0.3009
4619.2 0.0001*
27553.8 0.0001*
5280.4 0.0021*
5.1159 0.9360
6675.0 0.0048*
1234.7 0.2157
568.3
0.4898
19932.2 0.0001*
61516.6 0.0001*
76
788.9
1
2
2
5
2
2
1
1
2
2
1
Error
84
Total
107
107
98
R2
0.71
0.87
0.91
2807.2
* Significant at the 0.05 level
1997 result minus one chamber that gave aberant results
r_>_E
84
1996
1000
1997
SE = 16.3
Yecora Rojo
800
high density
600
400
SE
1000
25,1
Twin
800
high
density
400
1000
0.0
SE = 28.1
73 Twin
800
low
density 600
400
1000
Mean
SE = 11.8
800
600
400
Control Ozone
Low
Disease
High
Disease
Ozone
Ozone
Disease
Disease
Low
High
Control Ozone
Low
High
Disease D1Sease
Ozone
Ozone
Disease
Disease
Low
High
Figure IV-1A. Treatment means for stem height of two cultivars of wheat exposed to
two levels of ozone (0 and 90 ppb episodic) and/or three levels of Puccinia recondita
inoculum (none, low, and high) for two years. Yecora Rojo was not grown at the low
density. Standard errors are from the pooled standard error of the ANOVA. Dunnett's
multiple comparison procedure was used to determine treatment means (*) that were
significantly lower (P> 0.05) than the control means (no ozone, no inoculum).
Bonferroni's multiple comparison procedure results with one outlier removed for 1997
are indicated by O. The open bar on top of the 1997 control bar is the 1997 data with the
one outlier removed.
85
1996
1997
700
Yecora Rojo
high density
300
900
SE 43.5
-
Twin 700
,,,C#3
E
2
high
density 300
0:1
100
O
0
0
900
Twin 700
.0
'et
500
low
500
density
100
900
SE = 18.4
SE = 45.6
700
Mean
500
300
100
1
Control Ozone
I
Low
I
High
I
l
Ozone Ozone
Disease Disease Low
High
Disease Disease
I
Control Ozone
I
I
Low
High
Disease Disease
I
Ozone
Low
Disease
I
Ozone
High
Disease
Figure IV-1B. Treatment means for aboveground biomass of two cultivars of wheat
exposed to two levels of ozone (0 and 90 ppb episodic) and/or three levels of Puccinia
recondita inoculum (none, low, and high) for two years. Yecora Rojo was not grown at
the low density. Standard errors are from the pooled standard error of the ANOVA.
Dunnett's multiple comparison procedure was used to determine treatment means (*) that
were significantly lower (P> 0.05) than the control means (no ozone, no inoculum).
Bonferroni's multiple comparison procedure results with one outlier removed for 1997
are indicated by O. The open bar on top of the 1997 control bar is the 1997 data with the
one outlier removed.
86
1997
1996
400
SE = 23.0
300
Yecora Rojo
200
high density
100
0
400
Twin
300
high 200
OVA
density
4.
100
0
OA
400
Twin
low
300
200
74 density
O
100
Mean
300
200
Control Ozone
Low
High
Disease Disease
Ozone Ozone
Low
High
Disease Disease
Control Ozone
High
Low
Disease Diceas.
Ozone
Low
Disease
Ozone
High
Disease
Figure IV-1C. Treatment means for grain weight of two cultivars of wheat exposed to
two levels of ozone (0 and 90 ppb episodic) and/or three levels of Puccinia recondita
inoculum (none, low, and high) for two years. Yecora Rojo was not grown at the low
density. Standard errors are from the pooled standard error of the ANOVA. Dunnett's
multiple comparison procedure was used to determine treatment means (*) that were
significantly lower (P> 0.05) than the control means (no ozone, no inoculum).
Bonferroni's multiple comparison procedure results with one outlier removed for 1997
are indicated by . The open bar on top of the 1997 control bar is the 1997 data with the
one outlier removed.
87
1996
1997
900-
Zon
.6
800700600
700 600
500
400300
300250
20015010050
no
low
high
no
low
high
Inoculum Level
Figure IV-2. Plots of the treatment means for stem height (A), aboveground biomass
(B), and grain weight (C) of two cultivars of wheat exposed to two levels of ozone
exposure (0 and 90 ppb episodic) and/or three levels of Puccinia recondita inoculatum
(none, low, and high) for two years. Yecora Rojo was not grown at the low density. See
Table IV-1 for statistical analysis. = plants not exposed to ozone.
= plants exposed
to ozone. 0 = 1997, no disease, no ozone treatment with data from the outlier chamber
removed.
88
Plant height decreased due to treatment in both years, especially for the combined
treatments of ozone and disease (Fig. IV-1A). Only Twin at low density failed to
significantly respond to the treatments in either year (Fig. IV-1A). The amount (by
weight) of grain harvested was not significantly affected by treatments in 1996, whereas,
in 1997, the mean responses for the ozone and disease (low and high inoculation rates)
combined treatments were significantly less (70 and 76 percent) than the controls (Fig.
IV-1C). This was also true at the cultivar level (51 to 72 percent less than controls).
In 1997, one control chamber produced results that substantially increased
experiment-wide variability. Removing results of this chamber did not significantly alter
the ANOVA for plant height. However, the standard errors for biomass and grain weight
were reduced to levels similar to the 1996 experiment when the outlier was removed,
especially for the mean and Twin groups. This resulted in more ozone and disease
treatments being significantly different than the controls for the mean and Twin groups
for biomass and grain weight (Figs. IV-1B and IV-1C).
Plants initially inoculated with higher concentrations of spores developed higher
levels of disease (Fig. IV-3, Table IV-2). There were few statistical differences in disease
progression between ozone, and no ozone treatments in 1996. In 1997, however, Yecora
Rojo consistently had less disease in the presence of ozone than in its absence for all
levels of inoculation (Fig. IV-3B, Table IV-2). In the field, suitable conditions for rust
infection do not occur each night; our periodic inoculation method produced exponential
disease progression curves that are not atypical of those observed from field data (Mundt
personal observation).
Discussion
Interactions of Ozone with other Stresses: The most striking result of our study was
the general lack of interaction between ozone and wheat leaf rust. Sometimes, highly
significant main effects can produce a biologically unimportant but significant interaction
term. However, even this type of interaction did not occur in our experiment (Table IV-
89
1996
No Ozone
Ozone
Yecora Rojo High Density
100-
80-
6040
20
Iir-4
0
1
1
1
1
1
Twin High Density
100
80
60
40
20
0
Twin Low Density
100
8060 *
40
20
0
20
40
60
0
20
Days Post Inoculation
40
60
Figure IV-3A. Disease progression curves for wheat leaf rust for the 1996 disease and
ozone experiment. Two cultivars of wheat were exposed to two levels of ozone (0 and
90 ppb episodic) and/or three levels of Puccinia recondita inoculum (none, low, and high)
in open-top chambers. Yecora Rojo was not grown at the low density. Disease readings
were taken three times for the 1996 experiment.
= plants inoculated at high level. =
plants inoculated at low level.
= plants not inoculated.
90
1997
No Ozone
Yecora Rojo High Density
Ozone
100
80
60
4020
0
Twin High Density
100-
80-
60-
40200U
ar
Twin Low Density
100-
8060-i
40
20
0
I
0
20
40
60
0
20
40
60
Days Post Inoculation
Figure IV-3B. Disease progression curves for wheat leaf rust for the 1997 disease and
ozone experiment. Two cultivars of wheat were exposed to two levels of ozone (0 and
90 ppb episodic) and/or three levels of Puccinia recondita inoculum (none, low, and high)
in open-top chambers. Yecora Rojo was not grown at the low density. Disease readings
were taken four times for the 1997 experiment.
= plants inoculated at high level.
=
plants inoculated at low level.
= plants not inoculated.
91
Table IV-2. Area under the disease progression curve for leaf rust on spring wheat
cultivars Yecora Rojo and Twin in open-top chambers. The area under each diseaseprogression curve was calculated (Shaner and Finney 1977) by pot and subjected to
analysis of variance.
Cultivar
Density t
Disease /
Ozone §
1996
1997
Yecora Rojo
High
high
high
low
low
no
no
high
high
low
low
no
no
high
high
low
low
no
no
high
high
low
low
no
no
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
no
yes
1787.5
2401.7
1783.2
761.3*
1333.8*
1374.4*
646.3*
Twin
Twin
Mean
High
Low
1126.6*
9.3*
2.3*
2363.4
2611.0
1143.7
1306.9
4.2*
0.6*
1998.3
1717.9
810.2*
846.7*
2.3*
2.6*
2049.7
2037.4
905.1*
1093.4*
5.2*
1.8*
79.7*
57.5*
1564.5
1248.2
851.8*
903.2
16.5*
20.6*
1450.6
1146.5
942.4*
839.7*
16.9*
22.6*
1805.6
1242.8*
1056.2*
796.4*
37.7*
33.6*
* = significantly lower (P > 0.05) than high disease and no ozone treatment based on a
Dunnett's one-tailed, multiple comparison procedure.
Wheat seeded at densities of 81 (Low) or 323 (High) seeds m-2.
/ In 1996, high disease treatments were inoculated at a rate of 2,807g/ha, and low disease
treatments received a rate of 175 g/ha of uredospores. In 1997, the inoculation rates
were doubled.
An exposure regime of 90 ppb episodic ozone (Lefohn et al. 1986) was used.
92
1). Interactions of ozone with biological, chemical, and physical factors have resulted in
a variety of plant responses with no single pattern. In general, however, synergism
appears rare. A biological factor such as the increased feeding preference for a range of
insect species can result from ozone-induced changes in host plants (US EPA 1996).
When ozone exposures were combined with chemical factors such as other air pollutant
exposures, for example PAN (peroxyacetyl nitrate), the two gases tended to act
antagonistically (Tonneijck 1984; Nouchi et al. 1984). No interactions were found with
combined sulfur dioxide and ozone exposures on wheat (Amundson et el. 1987; Kohut et
al. 1987). Interactions between such heavy metals as cadmium, nickel, and zinc almost
invariably enhanced adverse effects when combined with ozone exposure. These effects
were usually additive but occasionally they were greater (US EPA 1996). It is interesting
to note that the interactions between ozone exposure and agricultural chemicals, in
particular the fungicide benomyl, resulted in increased plant protection probably because
of the antioxidant properties of the carbamate fungicides (US EPA 1986; US EPA 1996).
Physical factors like water stress affect the response of plants to ozone by altering the
degree of stomatal opening, which affects the quantity of ozone entering the plant
(Flagler et al. 1987). This interaction ameliorates the effects of ozone in some cases
(Flagler et al. 1987). However, in general, any "protective " benefit of water stress by
reducing ozone exposure will be offset by the direct effects of water stress (US EPA
1986; US EPA 1996). Interestingly, loblolly pine seedlings exposed to higher than
ambient levels of ozone had increased water use efficiency, suggesting that trees exposed
continuously to low ozone levels may be more sensitive to recurrent drought stress than
those grown under higher exposure levels (Johnson and Taylor 1989).
Cultivar / Density Effects: The cultivar Yecora Rojo exhibited ozone resistance similar
to the cultivar Twin, even though Yecora Rojo became the dominant wheat cultivar in the
San Joaquin and Imperial Valleys of California soon after its release there in 1975
(Qualset et al. 1985), suggesting that it might have higher levels of ozone resistance.
This was a reasonable assumption as it appears that native plants have likely been
selected for ozone resistance over the past 60 to 80 years (Berrang et al. 1986) and plant
93
breeders have also inadvertently selected for ozone resistance in developing some
cultivars of economically important plant species (Reinert et al. 1982). While wheat
exhibits considerable variability in response to ozone exposure (Heck et al. 1984; Adaros
et al. 1991), some breeding programs have selected for other traits and have inadvertently
increased the susceptibility of wheat to ozone (Barnes et al. 1990; Velissariou et al.
1992). The increased sensitivity of some wheat cultivars may not be exhibited because of
the antioxidant properties of some agrochemicals that are applied routinely (Velissariou
et al. 1992). In the case of Yecora Rojo, which was developed in Mexico at the
International Maize and Wheat Improvement Center, there might not have been
significant selection pressure for ozone resistance or, as an earlier maturing cultivar, it
might mature prior to the formation of the greater, summer-time levels of ozone.
While both cultivars responded similarly to ozone in this study, there was an
indication that ozone slowed the rate of disease progression for Yecora Rojo, particularly
in 1997 (Fig. IV-3, Table IV-2). Other researchers have found either decreased (Dohmen
1987) or increased ( Tiedermann 1992) number of pustules on ozone-exposed plants as
compared to non-exposed controls. Tiedermann (1992) suggested that these contrary
results may be related to wheat age, with younger plants being less sensitive to wheat leaf
rust than older plants when exposed to ozone.
As expected, there were significant cultivar differences for the growth response
variables between Yecora Rojo and Twin (Table IV-1). In addition, density differences,
with the cultivar Twin, were also consistently significant (Table IV-1). Wheat grown at
the lower density appeared more robust, but was shorter (Fig. IV-1A), probably due to
less interplant competition. Also, there was a significant ozone by density interaction for
both years in height, and for total grain weight in 1997 (Table IV-1). However, little is
known about the interaction between intraspecific competition and ozone exposure (US
EPA 1996).
In comparison to ozone and density interactions, considerable, although limited,
effort has been given to disease by density interactions. From these studies, it appears
that there is a tendency for disease to increase with plant density for fungal pathogens
94
(Boudreau and Mundt 1997). In our experiment, there was a highly significant
interaction between disease and plant density for both years as measured by total grain
yield (Table IV-1C) and in 1997 for aboveground biomass (Table IV-1B). This may be
explained by the lower levels of wheat leaf rust in the low density stands resulting in less
impact on plant growth (Fig. IV-3).
Ozone Effects on Obligate versus Facultative Pathogens: There have been at least two
hypotheses proposed for generalizing pathogen, host, and ozone interactions. First,
Heagle (1973, 1982) suggested that obligate pathogens are generally inhibited by air
pollutants, as there is less host tissue for colonization. In contrast, facultative pathogens
do not require host tissue for survival and have shown more variable responses to ozone
exposure (Heagle 1973; 1982). Second, Dowding (1988) suggested that necrotrophic
pathogens (pathogens that use dead tissue as an energy source) would be encouraged by
host exposure to ozone as compared to biotrophic pathogens (pathogens that use live
tissue as an energy source) for three reasons. First, ozone exposure may make more
soluble and easily degraded plant carbohydrates available as a carbon resource for
necrotrophic pathogens. Second, necrotrophic pathogens have processes for the
detoxification of phytoalexin or polyphenol oxidase, which can be induced in plants in
response to ozone exposure. Third, hyphae of biotrophic pathogens are more exposed to
ozone in the gas phase than cells of necrotrophic pathogens because necrotrophic
pathogens cause tissue collapse in their hosts.
These two hypotheses are not necessarily in conflict with our results with wheat
leaf rust, which is caused by an obligate, biotrophic pathogen. Disease severity was not
enhanced by exposure of wheat to ozone (Table IV-2, Fig. IV-3) and in some cases
lessened, supporting Dowding's (1988) view. Also, there was observational evidence
that pustule size was reduced on ozone-exposed leaves, supporting Heagle's hypothesis
(1982). Although pustule size was decreased, it did not appear to be sufficient to
consistently reduce epidemic progression.
95
However, Dowding (1988) also suggested that whether a disease is influenced by
ozone, and the magnitude of that effect, depends on the timing of pollutant exposure and
the infective periods of the host/pathogen. In our study, the infective period was
temporally separated from ozone exposure, as discussed below, and this may have
reduced the impact of ozone on leaf rust. The complexity of these temporal interactions
also supports the conclusions of several investigations that it is impossible to generalize
and predict the effects of pathogens based on our present knowledge (US EPA 1986,
1996; Manning and Keane 1988; Tiedemann et al. 1990, 1991).
Ozone Effects on Cereal Rust Pathogens: There is limited information about the
interaction between ozone and cereal pathogens. The early work of Heagle (1970) with
oat crown rust (Puccinia coronata) indicated a resistance of uredospores to ozone
damage. In addition, he found little effect on uredospore germination, appressoria
formation, or penetration. He did note that pustules were significantly smaller on leaves
exposed to ozone. We also observed smaller pustules on plants exposed to ozone,
although this was not quantified. Heagle and Key (1973) found that fewer spores were
produced from uredia of Puccinia graminis formed on ozone-exposed wheat plants, but
that these spores were as viable as those on nonexposed plants. While we did not
measure spore production, we did allow reinoculation with the uredospores produced
within each chamber and saw little, if any, reduction in infection rates from spores
developed under ozone exposures (Fig. IV-3).
Ozone probably had little direct effect on Puccinia recondita because ozone
exposure and inculation were temporally separated from each other. When uredospores
of Puccinia land on wheat plants in the presence of free moisture, they germinate and
their germ tubes enter the stomata. These events occur at night or early morning prior to
significant ozone exposures. The rate of spore germination, hyphal growth, and cell
penetration is temperature-dependent, taking between six and 14 hours (Politowski and
Browning 1975). In addition, P. recondita does not require open stomata for penetration
(Caldwell and Stone 1936). So, even if ozone did induce closure of stomata (Winner et
96
al. 1988), it should have little impact on P. recondita's ability to infect wheat. In our
experiment, the plants were covered overnight with plastic sheeting following inoculation
and a heated vaporizer was used to increase the water vapor and temperature around the
leaf environment. Thus, the fungus was most likely not exposed to ozone during the
germination and penetration phases. Direct effects of ozone exposures on Puccinia, if
they occur, would most likely occur in the leaf interior.
Ozone Effects on Cereals as Hosts for Pathogens: The reduction of disease found in
seedlings exposed to both ozone and P. recondita (Dohmen 1987) may not necessarily be
extrapolated to mature plants. When wheat was exposed at four different growth stages
to three levels of ozone prior to inoculation with P. recondita, Tiedemann (1992) found
that leaf rust was enhanced by ozone at all growth stages, with the effects becoming
stronger with more developed plants. However, the highest ozone levels reduced rust
severity in younger plants, whereas, on the older plants, the highest ozone exposures
induced the highest levels of disease severity (Tiedemann 1992). The current study
resulted in a midpoint between the findings of Dohmen (1987) and Tiedemann (1992).
Generally, we found few effects of ozone on disease severity (Table IV-2) other than
observations of smaller pustules and a reduced amount of tissue to colonize, which we
did not measure.
Lack of an ozone effect on disease severity may be due to a lack of direct effects
of ozone on the pathogen. Lack of impact of ozone on P. graminis was due to the partial
protection of the infecting structures arising from the appressoria, which formed in the
substomatal cavity of wheat (Heagle and Kay 1973)
.
In contrast, a reduction in wheat
leaf rust virulence was found with wheat that had been exposed to ozone, but this was
attributed to changes in plant metabolism in response to ozone exposure ( Dohmen 1987) .
With an obligate biotroph, it is difficult to separate the direct from the indirect effects of
ozone.
Pathogen success depends in part on the physiological state of the host. The
extracellular environment in plants exposed to ozone is significantly different from that of
97
nonexposed leaves, having a higher ascorbic acid concentration and cationic peroxidase
activity (Castillo et al. 1984; Castillo and Greppin 1986). A common effect of ozone
exposure is an increase in membrane leakage, especially the plasmalemma (Heath 1987),
leading to the loss of metabolites (Weber et al. 1997). There are also changes in the
hormonal balance in plants for both auxins and ethylene (Weber et al. 1997). However,
there is little evidence in the literature to suggest how ozone-induced changes in a plant
affect disease severity.
The slower rate of disease progression in Yecora Rojo with ozone exposure in
1997 (Fig. IV-3) could be due to less living tissue being available for colonization. The
initial microscopic evidence of ozone injury in wheat was the collapse of mesophyll cells
adjacent to substomatal cavities (Heagle and Kay 1973). With time, more mesophyll
cells died and injury appeared macroscopically as small white or tan necrotic areas. We
also observed similar areas that were initially white or gray, which turned necrotic in
time. Hyphal growth was limited in areas near injured mesophyll cells and retardation of
infection and hyphal growth was not found unless evidence of mesophyll cell injury was
observed (Heagle and Kay 1973).
Ecological Significance: While this study was done under semi-controlled conditions of
open-top chambers, the findings need be placed in an ecosystem context. Concern about
the relevancy of plant response to ozone exposures from plants grown in chambers has
existed for many years (Lewis and Brennan 1977). However, open-top chamber systems
are considered the best compromise in simulating natural systems because the
experimental unit is replicable, a range of treatment levels are available, control of
extraneous factors is possible, and the simulation of field losses due to ozone is relatively
accurate (US EPA 1996). One major concern about open-top chambers has been their
modification of microclimatic conditions. In open-top chambers, daytime temperatures
can increase, while photosynthetically active radiation and wind speed can decrease
(Heagle et al. 1988). However, modification of microclimatic conditions does not appear
to affect relative plant response to ozone (US EPA 1996).
98
Fitness of annual plants and many sexually reproducing perennial plants has
often been measured in terms of quantity and size of seed produced and this has been
supported by findings suggesting that within a species, plants with the most and/or largest
seeds may have a selective advantage in establishment, depending on environmental
conditions (Harper 1977). Yearly variability of seed production is common in perennial
species and can be important in their maintenance in plant communities (Ostfeld et al.
1996; Sork et al. 1993; Lalonde and Roitberg 1992; Silvertown 1980). Annual plants
also have large variances in reproductive output among years, and it is not surprising to
find year-to-year variability in our results. Similar, year-to-year variability has been
found in other studies with wheat yield parameters and ozone exposure (Kress et al. 1985;
Koburt et al. 1987). The year-to-year variability in grain weight reported here suggests
that open-top chambers may capture at least some of the variability found in natural
systems.
At a larger scale, successional trajectories are often determined by catastrophic
events (Sousa 1984; Egler 1977). The success of a species depends, in part, on its ability
to withstand year-to-year variability while at the same time surviving catastrophic events.
In this experiment, ozone or disease alone caused a reduction in seed production in some
cases (1997), but in most cases the reduction was not significant compared to controls
(Fig. IV-1C). However, when ozone and disease exposure were combined, seed
production was further reduced. Such losses could place populations at increased risk of
extinction, especially in the face of other catastrophic events. Populations already at low
densities may especially be at risk. Ashmore and Ainsworth (1995) demonstrated a
composition change in a grass community due to ozone exposure, and that change was
enhanced by cutting. Miller et al. (1982) found that ponderosa pine stands exposed to
ozone were more susceptible to root rot (caused by Fomes annosus) and pine beetles
(Dendroctonus brevicomis), which resulted in increased mortality. This increased fuel
load led to stand-replacing fires that changed the pine forest to one of fire-resistant shrubs
and oaks (Miller et al. 1982).
99
In summary, ozone exposure and wheat leaf rust decreased vegetative and
reproductive success of wheat in both years. We found that the effects of ozone and
disease exposure in combination were greater than each stress individually as we had
hypothesized but, contrary to expectation, there was no significant interaction between
the two stressors. Ozone exposure and leaf rust disease were additive in their effect and
show no evidence of a synergistic interaction. Disease progression was modified by
inoculation levels and planting density. However, the complexity of the interactions
between Puccina recondita, wheat genotypes, and biotic / abotic environments limits the
generalizations possible concerning plant pathogens and ozone exposure effects on
plants.
100
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V. CONCLUSIONS
The goal of this study was to determine the effects of single and multiple
stresses on plant communities. Specific objectives were to determine: 1) the importance
of initial plant density and species proportion on wheat leaf rust severity, 2) the effects
of density and proportion on a species' productivity; 3) if effects of density and
proportion were modified by a pathogen, 4) if the interactions between species were
modified by seeding density, mixture proportions or disease, 5) the effect of wheat leaf
rust and ozone exposure on wheat productivity, and 6) if there was an interaction
between wheat leaf rust and ozone on wheat productivity. A model system was
developed comprised of spring wheat (Tritium aestivum), wild oats (Avena fatua), and
Puccinia recondita (the causal agent of wheat leaf rust). This system was chosen
because it consists of annual plants, allowing ease of replication, and because the
biology of both the plant and pathogen species are well understood.
Field study
Four blocks of wheat and wild oats were each planted at total densities of 81,
162, and 324 germinable seeds per m2 and at five proportions (100:0, 75:25, 50:50,
25:75, and 0:100) of wheat:wild oats in the field. One-half of each block was inoculated
with uredospores of P. recondita. Fungicide was routinely applied to the noninoculated plots to protect them from the disease.
Severity of wheat leaf rust was not significantly affected by seeding density,
probably due to compensatory tillering of wheat at the lower densities. However,
disease severity significantly increased with increasing proportion of wheat in wheat-
wild oats mixtures, usually in a linear manner. The wheat cultivar Twin was
significantly more susceptible than Penawawa for both years and at all samplings.
There was no evidence to suggest that the barrier effect of wild oats in mixtures was
significant in decreasing disease severity, though high variability in the data may have
106
masked such evidence. This suggests that wild oats effectively occupied space and
changed the density of wheat, resulting in reduced disease spread.
The most striking finding of this field study was the almost total lack of
interaction of disease with density or proportion. Wheat leaf rust generally had a
negative effect on wheat performance, while wild oats was not significantly impacted.
The pattern of smaller wheat from inoculated plots was also consistent at the cultivar
level for all response variables, although the pattern was stronger for Twin. The two
wheat cultivars differed in their growth and in their interactions with wild oats, with
Penawawa being smaller (less aboveground biomass and total grain weight) than Twin.
However, the mean grain weight from Penawawa was larger than for Twin. Wild oats
plants also responded to wheat cultivar by producing more biomass in mixtures with the
cultivar Penawawa, while having heavier seeds when grown with Twin. Planting
density was a consistent and significant factor (either linear or nonlinear) influencing
the performance of wheat and wild oats. With both wheat and wild oats, there was a
decrease in aboveground biomass per culm with increasing density, suggesting that the
plants responded to increasing density with smaller culms. The mean seed weight of
wild oats responded positively to increased density. Species proportion was
consistently the most significant treatment, regardless of species, cultivar, or year. As
expected, increasing the proportion of wheat or oats in mixtures lead to an increase in
the amount of aboveground biomass and total seed weight (measured in wheat only) for
that species. It also led to an increase in wheat mean seed weight regardless of cultivar,
but a decrease in wild oats mean seed weight. The shapes of the density and proportion
effects curves for wheat did not change in response to the inoculation treatment, but the
positions of the curves for inoculated wheat were generally lower. The lower
production of wheat in the presence of wild oats compared to wheat in monoculture
indicates that interspecific competition from wild oats is stronger than wheat
intraspecific competition.
107
Open-top chambers
A small subset of the above field treatments was exposed to ozone in open-top
chambers. The most surprising result from this study was the almost total lack of
interaction between ozone and wheat leaf rust. Ozone exposure and leaf rust disease
were additive in their effect and showed no evidence of a synergistic interaction. There
were no ozone by disease interactions in either year or with any response variable.
Plant height and aboveground biomass was decreased significantly by ozone and by rust
in both years, especially for the combined treatments of ozone and disease. In contrast,
total grain weight was only significantly decreased by ozone or disease in one year.
When ozone and disease exposure were combined, seed production was further reduced.
If similar results occur in natural ecosystems, such losses could place populations
already at low densities at increased risk of extinction, especially in the face of other
catastrophic events.
Disease level often interacted significantly with cultivar and planting density in
the ozone experiments, especially when measured by aboveground biomass and grain
weight. The cultivar Twin at high density was most consistently impacted by stress,
with the combined ozone and disease treatments having significantly less biomass than
the controls. The susceptible cultivar Yecora Rojo exhibited ozone resistance similar to
the cultivar Twin, even though Yecora Rojo became the dominant wheat cultivar in
areas of California with high levels of ozone. The complexity of the interactions
between Puccina recondita, wheat genotypes, and biotic / abotic environments limited
the generalizations possible concerning plant pathogens and ozone exposure effects on
plants.
Considerable research has been conducted on individual species response to
ozone exposure. The findings of intra-species variability in response to biologic factors
further strengthens the need to evaluate the relevance of current regulatory tests, which
use limited numbers of agricultural species. Significant opportunities exist for
investigating the effects of ozone on plant interactions such as competition, pathogens,
108
and herbivory. Because of the plastic response of plants to density, especially grasses,
future work investigating the effects of plant density should use larger differences in
density than were used here.
109
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