direct and indirect effects of viral pathogens and the environment on invasive grass fecundity in...

Direct and indirect effects of viral pathogens and the

environment on invasive grass fecundity in Pacific

Coast grasslands

Eric W. Seabloom1*, Elizabeth T. Borer1, Anna Jolles2 and Charles E. Mitchell3

1Department of Zoology, Oregon State University, Corvallis, OR 97330, USA; 2Department of Biomedical Sciences,

Oregon State University, Corvallis, OR 97330, USA; and 3Department of Biology, University of North Carolina,

Chapel Hill, NC 27599, USA

Summary

1. Pathogens can have strong effects on their hosts and can be important determinants of biological

invasions. In natural systems, host–pathogen interactions may bemediated by direct environmental

effects on pathogen communities and host fitness.

2. While environmental mediation of host–pathogen interactions has been investigated

experimentally and at single sites, there have been few studies tracking pathogen effects on lifetime

host fecundity across large naturally occurring environmental gradients.

3. If environmental factors directly mediate both pathogen transmission and host fecundity, labo-

ratory and local-scale studiesmay not predict pathogen effects across large spatial scales.

4. Here we investigate the relationship between host fecundity and infection by a suite of RNA viral

pathogens, by surveying two invasive annual grasses at 18 locations along a 1200-km latitudinal

gradient on the west coast of North America.

5. Infected hosts of both species had 28–30% lower fecundity than uninfected hosts in our field sur-

veys. However, the correlation of reduced fecundity to infection arose from indirect effects of the

environment on both host fecundity and pathogen prevalence, rather than direct effects of the

pathogen on the host. Pathogen prevalence was highest at sites where uninfected hosts had lowest

fecundity.

6. Synthesis. In past experimental inoculations, virus infection reduced fecundity of these host spe-

cies. Against this background, the results of our geographic-scale survey demonstrate the challenges

not only of inferring cause from correlation, but also of extrapolating from local studies and experi-

mental inoculations to larger spatial scales. Our results highlight a need for experimentally manipu-

lating infection across environmental gradients. Such an integrated approach would allow

quantification of the fitness impacts of infection, even when the environment directly affects both

prevalence and host fecundity.

Key-words: Avena fatua, Bromus hordeaceus, community ecology, disease ecology, grasslands,

invasion

Introduction

Pathogens can have strong effects on the composition of natu-

ral communities. Determining the effects of pathogens on

hosts is of particular relevance for invasive species, as patho-

gens can mediate biological invasions (Anderson &May 1986;

Dobson & Crawley 1994; Tompkins, White & Boots 2003;

Torchin & Mitchell 2004; Mitchell et al. 2006; Borer et al.

2007b). However, pathogen effects on hosts occur within a

larger abiotic andbiotic environmental context.Environmental

gradients in resources, competitors and consumers all directly

alter host vital rates, independent of pathogens (Mitchell et al.

2006). In addition, environmental factors can alter pathogen

prevalence by changing pathogen transmission (Gregory 1973;

Agrios 1978; Fitt, McCartney & Walklate 1989; Madden,

Yang &Wilson 1996; Aylor 1999), vector communities (Cum-

ming & Guegan 2006), resource supply rates (Mitchell et al.

2006), host community composition (Power & Mitchell 2004;

Keesing, Holt & Ostfeld 2006) and abundance of natural ene-

mies (Packer et al. 2003; Malmstrom et al. 2006). As a result,

pathogen and environmental effects on host fitness are*Correspondence author. E-mail: [email protected].

edu

Journal of Ecology 2009, 97, 1264–1273 doi: 10.1111/j.1365-2745.2009.01550.x

� 2009 The Authors. Journal compilation � 2009 British Ecological Society

potentially confounded (Hassell et al. 1982; Holmes 1982;

Thrusfield 2005).

There are at least two types of effects on host fecundity that

could be obscured by such environmental confounding. Envi-

ronmental conditions can modulate pathogen effects on host

fecundity. This phenomenon of environmental modulation is

well studied using experimental inoculations or natural infec-

tions in the laboratory or single field sites (Park 1948; Malm-

strom et al. 2006). Conversely, pathogens may mediate

environmental effects on host fecundity. This phenomenon

occurs when infected and uninfected hosts respond differently

to abiotic gradients (Malmstrom et al. 2006), competition

(Park 1948; Malmstrom et al. 2006; Borer et al. 2007b) or con-

sumer effects (Packer et al. 2003;Malmstrom et al. 2006).

However, few studies have attempted to separate these

effects on large-scale, naturally occurring environmental gradi-

ents. This may be because of the difficulty of assembling a data

set in which the infection status and lifetime fate and fecundity

of individual hosts are known for a spatially extensive set of

sites with known environmental conditions. While there are

many large-scale studies of pathogen prevalence (Berger et al.

1998; Smith et al. 2002), these rarely measure host fecundity or

fitness (but see Pioz et al. 2008). As a result, it remains unclear

whether local-scale studies of pathogen effects can be scaled up

to mesh with larger-scale observations of disease prevalence.

Scaling up can lead to incorrect conclusions when the environ-

mental conditions at a local study site are not representative of

environmental conditions across the region. It also remains

unclear how large-scale surveys can be used to predict patho-

gen impacts on local host populations. Scaling down can lead

to incorrect conclusions when local sites vary environmentally,

and there is either environmental confounding of host fecun-

dity and infection, or environmental modulation of the fitness

impacts of infection.

The disconnect between studies of pathogens in the labora-

tory or at single sites and large-scale epidemics is particularly

relevant to studies of the role of pathogens in biological inva-

sions, because of the need to forecast pathogen impacts at large

spatial scales and in novel environments. The invasion of exo-

tic annual grasses into grasslands of western North America

presents a unique opportunity to examine pathogen–environ-

ment interactions within a system of great relevance to conser-

vation biology. The invasion of these grasslands by exotic

annual grasses from the Mediterranean region is one of the

most dramatic invasions world-wide, including over 9 million

ha in California alone (Heady 1977). Invasion of annual

grasses into native perennial grass communities may have been

facilitated by a suite of phloem-limited pathogens, collectively

referred to as barley and cereal yellow dwarf viruses

(B ⁄CYDVs) (Malmstrom et al. 2005b; Borer et al. 2007b).

B ⁄CYDVs in their exotic annual hosts are well suited for

large-scale surveys and assessment of lifetime fecundity effects

of pathogens in natural systems.Many exotic annual grass spe-

cies have quite extensive latitudinal distributions. Experimen-

tal inoculation by these viruses in laboratory and field trials

can significantly reduce annual grass biomass and fecundity

(D’Arcy 1995; Malmstrom et al. 2005a). For example,

greenhouse inoculations with BYDV-PAVdecreased total bio-

mass (above- and below-ground) of two widespread and com-

mon annual grass hosts that are the focus of the current study,

Avena fatua and Bromus hordeaceus (41% and 39% respec-

tively; J.P. Cronin, M.E. Welsh, M. Dekkers, C.E. Mitchell,

unpublished data).We surveyed populations of these two inva-

sive annual grass species at 18 grassland sites spanning a three-

fold gradient in rainfall (434–1448 mm year)1) and 1200 km

of latitude along thewest coast ofNorthAmerica.We screened

each of the 568 individual host plants for four common RNA

viral pathogen species (barley and cereal yellow dwarf viruses,

B ⁄CYDVs). In addition, the lifetime fecundity of annual

grasses is easily measurable in the field as the seed production

at the end of a growing season, so we could directly measure

lifetime fecundity and infection status in naturally occurring

individuals.

We use these data to investigate the following three ques-

tions to clarify the roles of the environment and pathogen

infection on host fecundity:

1. What is the relationship between infection status and host

fecundity? To answer this question, we first compare the

fecundity of infected and uninfected hosts across all sites.

We then compare fecundity of infected and uninfected

hosts after controlling for among-site variability (i.e. we

determine the effect of pathogen nested within a site) to

test whether infection status explains any residual varia-

tion in host fecundity.

2. What is the direct effect of the environment on host

fecundity? To answer this question, we use regression to

find environmental determinants of fecundity indepen-

dent of the pathogen (i.e. fecundity in uninfected hosts

across all sites).

3. What is the relationship between environmental quality

and pathogen prevalence? To answer this question, we

compare our pathogen-independent measure of site qual-

ity (fecundity of uninfected hosts) with site-level patho-

gen prevalence.

Materials and methods

STUDY SYSTEM

B ⁄CYDVs are a suite of aphid-vectored viruses in the family Luteo-

viridae that are known from over 150 grass hosts (Irwin & Thresh

1990; D’Arcy 1995). These viral pathogens cause one of the most eco-

nomically important viral diseases of cereal crops (barley yellow

dwarf) and are some of the most prevalent of all pathogens (Irwin &

Thresh 1990). Recent work suggests that the presence of these viruses

is a necessary precursor to one of the most widespread and persistent

plant invasions world-wide (Malmstrom et al. 2005b; Borer et al.

2007b), the conversion of 25% of the area of California to annual

grassland dominated by exotic annual species from the Mediterra-

nean region (Heady 1977; Seabloom et al. 2003).

Infection by a B ⁄CYDV leads to increased mortality, stunting and

decreased fecundity in crops and natural systems (Rochow 1970;

D’Arcy 1995; Malmstrom et al. 2005a). There is no vertical transmis-

sion of the viruses, so seedlings of infected parents are initially unin-

fected (Rochow 1970). B ⁄CYDVs are transmitted by at least 25

Pathogen and environmental effects on grasses 1265

� 2009 The Authors. Journal compilation � 2009 British Ecological Society, Journal of Ecology, 97, 1264–1273

different aphid species (Halbert & Voegtlin 1995). The viruses do not

replicate in the aphid vectors and are not transmitted to aphid off-

spring (Rochow 1970; Agrios 1978). Aphids can acquire the viruses in

as short a time as 15 min, and viruliferous aphids can inoculate a

plant in 2 h, although efficiency increases with acquisition and inocu-

lation time (Gray et al. 1991; Power & Gray 1995). BYDV and

CYDV species belong to distinct genera and the different viral species

differ in their virulence and the suite of aphids that serve as efficient

vectors (Miller &Rasochova 1997).

FIELD SURVEY OF PREVALENCE AND FECUNDITY

In May 2006, we sampled B ⁄CYDV prevalence in natural grasslands

at eight research reserves in California and Oregon (Fig. 1; Table S1

in the Supporting Information). At the larger reserves, we sampled up

to three populations per reserve that were separated by more than

500 m for a total of 18 sample sites. Selected sites were in open oak

woodland that was not actively grazed, although cattle and sheep

grazing occurs at some of the reserves.

We randomly collected up to 20 B. hordeaceus and 20 A. fatua

whole individual plants (568 total hosts assayed) from a site. Bromus

hordeaceuswas collected at all sites, while we were only able to collect

A. fatua at 10 of our sites (Table S1). Hosts were collected at peak

biomass while leaf tissue was still green. While this collection time

optimizes detection of the virus, seed mass is likely underestimated.

However, it is unlikely that this downward bias would change our

overall results, because at this point in the season, plants are not

producing new flowers, total seed number is fixed, and total seed

mass and seed number are tightly correlated in these types of

annual grasses (see Results).

Fresh leaf tissue from each host was tested for infection by BYDV-

PAV, BYDV-MAV, BYDV-SGV and CYDV-RPV via enzyme-

linked immunosorbent assay (ELISA) using antibodies provided by

Agdia (Elkhart, IN, USA) (Rochow 1986). In the rare cases where

potential infections by two serologically related viruses were associ-

ated nearly 1 : 1 within individuals of a host species at a site, we

regarded the potential infections of the virus with the weaker assay

response (relative to standard controls on each microplate) as cross-

reactions to the other virus, rather than as coinfections. The entire

seed head of each host with all seeds attached was removed from each

host, dried to a constant mass at 60 �C, and weighed to the nearest

0.01 g.

At each site, we quantified plant biomass by clipping, drying and

weighing two 10 · 1 m strips to the nearest 0.01 g. We estimated the

areal cover of each plant species present in two 0.5 · 1 m quadrats.

Cover was estimated independently for each species, so total cover

can sum to more than 100%. We collected and air-dried three

2.5 · 10 cm deep soil cores which were analysed for total phospho-

rous, nitrate, potassium, organic matter, sand, silt, clay and pH by A

&LWestern Agricultural Laboratories (Modesto, CA,USA).

All statistical analyses were conducted using r version 2.5.1

(R Foundation for Statistical Computing, Vienna, Austria).

Prevalence data were analysed with logistic regression using the glm

function in r. Among-site variability was treated as a random effect

with population nested within reserve using the lme function from the

nlme library in r. Note that we also fitted models in which site and

site-within-population were nested within state, but the addition of

this nesting factor did not improve model fit as there were no

consistent differences between the two states.

ASSESSMENT OF FECUNDITY METRICS

As part of a larger experiment (Seabloom et al. 2003), we established

a series of experimental plots in a restored grassland at Sedgwick

Reserve in Santa Barbara County in 2000. This is the location of one

of our southern collection sites (Table S1). These plots ranged in size

from 9 to 25 m2 and were planted with exotic annual and ⁄ or nativeperennial grasses. In addition, the plots were subjected either to a

single summer burn, nitrogen addition (4 g N m)2 year)1 as NH4NO3)

or watering to match the upper 95th percentile of the monthly rainfall

(see Seabloom et al. 2003). Here we do not investigate the specific

treatments, but use them as a broad range of environmental condi-

tions to examine how changes in the biotic and abiotic environment

alter seed production. In each of the 120 plots, we collected the

above-ground portion of 10 individual B. hordeaceus plants just

prior to seed fall in 2006. We counted all seeds from each plant

and weighed the total seed mass and mass of non-reproductive

tissue after drying to a constant mass at 60 �C.

QUANTIF ICATION OF HOST MORTALITY

As with all large-scale studies examining pathogen prevalence,

patterns in the data do not account for host mortality prior to the

sampling. This pre-sampling mortality could create a bias in the

results if pathogen-induced mortality is highly variable among sites

3

2

333

3

Oregon

California

Fig.1. Sites included in survey of B ⁄CYDV prevalence and invasive

grass fecundity conducted on the west coast of the United States.

Numbers indicate the number of sites nested within larger reserves

that have apparently overlapping points on this large-scale map.

1266 E. W. Seabloom et al.


and is correlated with potential explanatory factors. We used an out-

plant study to quantify among-site variability in host mortality dur-

ing the period of potential pathogen transmission (i.e. the period

between when aphids arrive and are active to plant senescence at the

end of the growing season). We conducted this study at a subset of

five of the sites sampled during our 2006 observational study, two in

Oregon (Finley and Baskett Slough) and three in California (Sierra

Foothill, Mclaughlin and Hopland; Table S1). As it was logistically

infeasible to do this study at all observational sites, we increased the

range of environmental conditions of this experiment by subjecting

the outplants to a factorial combination of nitrogen addition (control

or 10 g N m)2 year)1 added quarterly as calcium nitrate) and phos-

phorous addition (control or 10 g P m)2 year)1 added quarterly as

triple super phosphate).

The experiment was a complete randomized block design with two

blocks at each of the five sites for a total of 40 experimental units (5

sites · 2 blocks · 2 levels ofN · 2 levels of P).

Experimental units were 40 · 40 m. Locally collected seed of each

host species was germinated in flats with 25 · 25 mm soil plugs. After

c. 4 weeks, 25 uninfected plants of each host species were planted into

each of the 40 · 40 m plots in January of 2008 prior to the first aphid

flights. Individual hosts plants were planted within 20 · 50 cm quad-

rats placed 2 m apart along transects within each 40 · 40 m plot.

Plants were surveyed after c. 5 months just prior to the start of senes-

cence at the end of the growing season in lateMay or early June.

Results

INFECTION STATUS AND HOST FECUNDITY

Overall infection prevalence at our sites ranged from 0% to

70% for B. hordeaceus (mean = 13.8%) and 0% to 55% for

A. fatua (mean = 21%). The prevalences of the individual

viruses in B. hordeaceus in order of abundance were BYDV-

MAV (8.3%), CYDV-RPV (5.5%), BYDV-PAV (5.5%) and

BYDV-SGV (4.3%). The prevalences of the viruses inA. fatua

in order of abundance were BYDV-SGV (12.5%), BYDV-

PAV (9.8%), BYDV-MAV (5.2%) and CYDV-RPV (4.7%).

There were no significant differences among the two host spe-

cies in total prevalence (i.e. infection by any virus) or any of the

four individual viruses (P > 0.05 lme with site as a random

factor). The three viruses that share vectors with at least one

other virus (BYDV-MAV, BYDV-PAV and CYDV-RPV)

were significantly positively correlated at the site scale

(P < 0.05 and correlations ranging from 0.68 to 0.85), while

the virus with only a single specialist vector, BYDV-SGV, was

not significantly correlated with the other viruses (P > 0.05

and correlations ranging from 0.01 to 0.21), as has been

reported in other work on this system (Seabloom et al. 2009, in

press).

Bromus hordeaceus and A. fatua individuals infected with

BYDV-SGV had significantly lower fecundity than did unin-

fected individuals. InfectedB. hordeaceus hosts had 30% lower

seedmass than infected hosts. Similarly, infectedA. fatua hosts

had seed masses that were 28% lower than infected hosts.

These effects were predominantly associated with infection by

BYDV-SGV that resulted in a 45.2% reduction in B. hordeac-

eus hosts and a 42.1% reduction in A. fatua hosts (Fig. 2;

Table 1).

The fecundity differences between infected and uninfected

hosts of both species were explained by variation in seed mass

among sites. There were no significant relationships between

infection by any of the viruses and host fecundity in models

that accounted for fecundity differences among sites (i.e.

reserves and populations nested within reserves; Table 2).

DIRECT EFFECT OF ENVIRONMENT ON HOST

FECUNDITY

Our sample sites spanned a large range of abiotic and biotic

environmental variation. Soils were highly variable among

sites for phosphorous (4–47 ppm), nitrate (1–10 ppm), potas-

sium (55–485 ppm), organic matter (2.5–7%), sand (34–74%),

silt (12–48%), clay (14–26%) and pH (5–7.7). Among sites,

precipitation during the year of sampling (2006 rainfall year:

July 2005–July 2006) ranged from 434 to1448 mm year)1.

While precipitation generally increased with latitude

(r = 0.81), we specifically included a longitudinal gradient in

California to decouple latitude and precipitation, and our wet-

test site was in central California (Hopland; Table S1). Total

above-ground biomass ranged from 88 to 897 g m)2, propor-

tion host cover (grass cover per total cover) ranged from 18%

to 85%, annual grass cover ranged from 8% to 130% and

perennial grass cover ranged from 0% to 45%. The total rich-

ness of all grass hosts ranged from 3 to 6 species 0.5 m)2.

We looked for direct environmental covariates of unin-

fected host fecundity using backward selection to remove

1.6

1.8

2.0

Bromus hordeaceus

Type of infection

Tot

al s

eed

mas

s lo

g 10

(mg)

Any SGV MAV PAV RPV

Type of infectionAny SGV MAV PAV RPV

Uninfected

(a)

(b)

Infected

2.3

2.5

2.7

Avena fatua

Tot

al s

eed

mas

s lo

g 10

(mg)

Fig. 2. Overall fecundity of two invasive annual grasses ((a) B. hor-

deaceus and (b) A. fatua) across populations infected by four species

of RNA viral pathogens. Error bars show one SE.



non-significant terms from an initial model that included July

2005–July 2006 rainfall, mean annual precipitation, latitude,

host (i.e. grass) species richness, total grass cover, annual grass

cover, perennial grass cover and proportional grass cover

(grass cover per total cover). Fecundity of both uninfected

A. fatua and B. hordeaceus increased with increasing host rich-

ness and precipitation, although the relative importance of

these factors differed between our two study species. Fecundity

of uninfected B. hordeaceus hosts was highest at sites with high

host (i.e. grass) species richness (Fig. 3, Table 3), whereas

fecundity of uninfected A. fatua hosts was highest at sites with

high precipitation during the 2006 growing season (Fig. 3,

Table 3). Biomass and soil data were unavailable from a few

sites, however these variables were not significant in any statis-

tical model, so we present results here on the wider range of

sites including those withmissing biomass and soil data.

DIRECT EFFECT OF ENVIRONMENT ON PATHOGEN

PREVALENCE

For both hosts, BYDV-SGV prevalence declined with increas-

ing site quality, as measured by the fecundity of uninfected

hosts (Figs 4 and 5). BYDV-SGV infection prevalence also

declined with precipitation (r = )0.70), host richness

(r = )0.41) and latitude (r = )0.46). Similarly, prevalence of

BYDV-PAV and BYDV-MAV in B. hordeaceus was lower at

high-quality sites (Table 4). CYDV-RPV prevalence was unre-

lated to environmental quality for either host (Figs 4 and 5).

It is possible that the fecundity of uninfected hosts is not

independent of prevalence. For example, uninfected hosts

could experience competitive release at sites with high levels of

infection (Borer et al. 2007a). Furthermore, it is possible that

the effects of infection may depend on environmental condi-

tions at a site. There was no evidence in our data of these types

of interactions between site quality, infection status, and effects

of infection on fecundity. Regressions of seed mass of infected

hosts on the seed mass of uninfected hosts had slopes indistin-

guishable from 1.0 for A. fatua (slope = 0.987, SE = 0.184)

and B. hordeaceus (slope = 0.885, SE = 0.170). Thus, the

fecundity of both infected and uninfected hosts was strictly a

function of the conditions at the local site.

ASSESSMENT OF FECUNDITY METRICS

Our metric of fecundity is the total mass of seed produced by

each host plant. Total seed mass is tightly correlated with seed

number and total plant size for annual grasses. For example,

inB. hordeaceus log10(total seedmass per plant) and log10(total

Table 1. Overall test of relationship between infection status and fecundity [total seedmass; log10(mg)] of two annual grasses:Bromus hordeaceus

(total d.f. = 344) andAvena fatua (total d.f. = 170)

Host d.f. Source Estimate Standard error t-value P

Bromus hordeaceus 1 Intercept 2.0035 0.0205 97.5860 0.0000

1 BYDV-PAV 0.0273 0.1525 0.1790 0.8581

1 BYDV-MAV )0.1627 0.1096 )1.4840 0.1386

1 BYDV-RPV 0.0739 0.1195 0.6180 0.5369

1 BYDV-SGV )0.2648 0.0973 )2.7220 0.0068

Avena fatua 1 Intercept 2.7307 0.0292 93.4920 0.0000

1 BYDV-PAV )0.0293 0.1098 )0.2670 0.7899

1 BYDV-MAV )0.1575 0.1329 )1.1850 0.2377

1 BYDV-RPV 0.0814 0.1382 0.5890 0.5566

1 BYDV-SGV )0.2375 0.0781 )3.0410 0.0027

Table 2. Results of linear mixed-effects model relating infection status of four viral pathogens and fecundity [total seed mass; log10(mg)] of two

annual grasses:Bromus hordeaceus (total d.f. = 344) andAvena fatua (total d.f. = 170). Site and populations are treated as random effects with

populations being nested within site. Bromus hordeaceus had an estimated standard deviation among sites of 0.1810 and among populations

within sites of 0.1816. Avena fatua had an estimated standard deviation among sites of 0.2451 and among populations within sites of 0.1438.

Note that the addition of state as a random effect did not improvemodel fit, so the simplermodels are presented here

Host d.f. Source Estimate Standard error t-value P


1 BYDV-PAV 0.0988 0.1128 0.8759 0.3817

1 BYDV-MAV )0.0275 0.0919 )0.2997 0.7646

1 BYDV-RPV )0.0032 0.0898 )0.0360 0.9713

1 BYDV-SGV 0.0001 0.0753 0.0011 0.9991


1 BYDV-PAV )0.0290 0.0800 )0.3628 0.7172

1 BYDV-MAV )0.0874 0.0990 )0.8827 0.3787

1 BYDV-RPV 0.0742 0.1011 0.7339 0.4641

1 BYDV-SGV )0.0221 0.0623 )0.3543 0.7236



400 600

(a)

(c)

(b)

(d)

800 1200

1.6

2.0

2.4

Bromus hordeaceus

2006 precipitation (mm)

See

d m

ass

unin

fect

eds

log 1

0 (m

g)

See

d m

ass

unin

fect

eds

log 1

0 (m

g)1.

62.

02.

4

3.0 4.0 5.0 6.0

Bromus hordeaceus

Host richness

500 700 900

2.4

2.6

2.8

3.0

Avena fatua

2006 precipitation (mm)

See

d m

ass

unin

fect

eds

log 1

0 (m

g)

2.4

2.6

2.8

3.0

See

d m

ass

unin

fect

eds

log 1

0 (m

g)

3.0 4.0 5.0 6.0

Avena fatua

Host richness

Fig.3. Primary environmental drivers of

fecundity of uninfected host species, B. hor-

deaceus (a and b) and A. fatua (c and d).

Note that precipitation and richness are posi-

tively correlated (r = 0.46), so both terms

are not included in the same model after

model selection.However, trends are concor-

dant for fecundity of both hosts along rich-

ness (b and d) and precipitation gradients

(a and c).

Table 3. Backwards-selected regression models of environmental effects on fecundity of uninfected hosts of two annual grasses at 18 sites. Note

that Bromus hordeaceus was found at all sites while Avena fatua was found at 10 sites. The total model included July 2005–July 2006 rainfall,

mean annual precipitation, latitude, host (i.e. grass) species richness, total grass cover, annual grass cover, perennial grass cover and proportional

grass cover (grass cover per total cover).Model selection was based onAIC using step procedure in R on the total model described above

Host d.f. Source Estimate Standard Error t-value P


1 Host richness 0.164 0.047 3.521 0.003


1 Precipitation (mm) 0.001 0.000 4.662 0.002

1.6 1.8

(a)

(c)

(b)

(d)

2.0 2.2 2.4

0.0

0.1

0.2

0.3

0.4

PAV Bromus hordeaceus

Seed mass uninfecteds log10 (mg)

Site

-leve

l pre

vale

nce

1.6 1.8 2.0 2.2 2.4

0.0

0.2

0.4

0.6


Site

-leve

l pre

vale

nce

MAV Bromus hordeaceus

1.6 1.8 2.0 2.2 2.4Seed mass uninfecteds log10 (mg)

0.00

0.10

0.20

0.30

RPV in Bromus hordeaceus

Site

-leve

l pre

vale

nce

1.6 1.8 2.0 2.2 2.4Seed mass uninfecteds log10 (mg)

0.00

0.10

0.20

Site

-leve

l pre

vale

nce

SGV in Bromus hordeaceus

Fig.4. Relationship between environmental

quality, as measured by uninfected host

fecundity, and site-level prevalence of four

viral pathogen species ((a) BYDV-PAV,

(b) BYDV-MAV, (c) CYDV-RPV and

(d) BYDV-SGV) inBromus hordeaceus.



number of seeds per plant) have a correlation of 0.932

(P < 0.0001 on 119 d.f.). Similarly, in B. hordeaceus, log10(total seed mass per plant) and log10(total plant mass) have a

correlation of 0.937 (P < 0.0001 on 119 d.f.). Thus our mea-

sure of fecundity accurately reflects both total energetic allo-

cation to reproduction (total seed mass), number of offspring

(total number of seeds) and host vigour (total mass per plant).

QUANTIF ICATION OF HOST MORTALITY

Our outplant study tracked the fate of 985 A. fatua and 1083

B. hordeaceus individuals. At the end of the growing season

after 5 months in the field, 86% (0.03 SE among all forty

40 · 40 m plots) ofB. hordeaceus and 79% (0.03 SE among all

forty 40 · 40 m plots) of the A. fatua were still alive. Thus,

mortality due to all causes (including all pathogens, herbivory,

competition) was only 14–21% during the period when

B ⁄CYDV can be transmitted. Based on these results, direct

mortality from B ⁄CYDV in the field did not exceed 14–21%

and is certainly much less than this, given that some significant

fraction of the mortality was likely due to competition from

the existing plants, herbivores, disturbance and other patho-

gens. More importantly for this work, there is little variability

in survival among sites even with the experimental nutrient

Table 4. Logistic regressions of site-level prevalence of four viral pathogens in two annual grass hosts.Bromus hordeaceuswas sampled at 18 sites

andAvena fatuawas sampled at 10 sites

Host Pathogen d.f. Source Estimate Standard error P

Bromus hordeaceus BYDV-MAV 1 Intercept 2.8024 1.6102 0.0818

1 log seed mass )2.6923 0.8676 0.0019

BYDV-PAV 1 Intercept 2.0520 1.9020 0.2807

1 log seed mass )2.5190 1.0250 0.0140

CYDV-RPV 1 Intercept )0.2756 1.7689 0.8760

1 log seed mass )1.2874 0.9230 0.1630

BYDV-SGV 1 Intercept 8.4980 3.1390 0.0068

1 log seed mass )6.3400 1.8110 0.0005

Avena fatua BYDV-MAV 1 Intercept )2.1652 3.5050 0.5370

1 log seed mass )0.2573 1.3001 0.8430

BYDV-PAV 1 Intercept )1.2209 2.6086 0.6400

1 log seed mass )0.3483 0.9686 0.7190

CYDV-RPV 1 Intercept )3.6526 3.9102 0.3500

1 log seed mass 0.2036 1.4374 0.8870

BYDV-SGV 1 Intercept 11.9240 3.6530 0.0011

1 log seed mass )5.4000 1.4510 0.0002

2.4 2.6

(a)

(c)

(b)

(d)

2.8 3.0

0.00

0.10

0.20

PAV Avena fatua

Seed mass uninfecteds (g)

Site

-leve

l pre

vale

nce

2.4 2.6 2.8 3.0

0.00

0.10

0.20

Site

-leve

l pre

vale

nce

MAV Avena fatua

2.4 2.6 2.8 3.0

0.00

0.04

0.08

0.12

RPV in Avena fatua


Site

-leve

l pre

vale

nce

2.4 2.6 2.8 3.0Seed mass uninfecteds log10 (mg)


0.0

0.2

0.4

SGV in Avena fatua

Site

-leve

l pre

vale

nce

Fig.5. Relationship between environmental

quality, as measured by uninfected host

fecundity, and site-level prevalence of four

viral pathogen species ((a) BYDV-PAV,

(b) BYDV-MAV, (c) CYDV-RPV and

(d) BYDV-SGV) inAvena fatua.



addition included as indicated by the small standard errors in

survival. The relatively high survival of the host plants and lack

of variability among sites makes it unlikely that the observed

patterns of prevalence are highly skewed by strong differences

in pathogen-inducedmortality among sample sites.

Discussion

Taken as a whole, we found that invasive annual grass hosts

infected by BYDV-SGVhad 42–45% lower fecundity than did

uninfected hosts. To a lesser degree, infection by other

B ⁄CYDV viruses followed a similar pattern (28–30% reduc-

tion in host fecundity). However, the fecundity effects in the

field surveys were due to site-level variability in environmental

conditions rather than direct pathogen effects. There were no

residual differences between the fecundity of infected and unin-

fected hosts after accounting for site-level variation in fecun-

dity. Similarly, a non-manipulative study at a local site also did

not find a consistent negative correlation between B ⁄CYDV

infection and host fecundity across three host species including

B. hordeaceus (Remold 2002). In contrast, laboratory and field

experiments conducted at single sites have demonstrated

strong effects of B ⁄CYDV inoculation on fecundity of our two

focal host species (Griesbach et al. 1990; Malmstrom et al.

2005a). The lack of coupling between experimental manipula-

tions of pathogens at single sites and both local and large-scale

surveys demonstrates that results of large-scale pathogen sur-

veys must be considered within an environmental context. If

the environment drives prevalence and host fecundity, then

population-level impacts in natural epidemics may not be

detectable without experimental inoculations.

Individual host fecundity was strongly correlated with both

the abiotic and biotic environment. In particular, high-quality

sites, where uninfected B. hordeaceus and A. fatua plants had

highest fecundity, had high rainfall and high numbers of grass

species. This may have arisen from the sites being favourable

for grasses generally as indicated by the more diverse grass

flora. Given the observational nature of these data, we are not

fully able to assign causation to any environmental covariates.

For example, host richness and precipitation are positively cor-

related, as sites with higher rainfall were also located further

north and had higher overall grass diversity. Fecundity of our

focal species was not correlated with total biomass, soil nutri-

ent levels or abundance of competitive dominant hosts (peren-

nial grasses; Seabloom et al. 2003).

Several limitations of large-scale surveys limit the ability of

this study to establish direct causal links between specific envi-

ronmental factors and the fecundity of infected and uninfected

hosts. Ultimately, host fecundity and pathogen prevalence

may be driven by factors not included in our model, such as

the presence of irrigated agricultural fields (Griesbach et al.

1990; Hewings & Eastman 1995). Furthermore, our data pres-

ent a single snapshot in time, while B ⁄CYDV prevalence and

impacts can vary widely from year to year (Hewings & East-

man 1995; Seabloom et al. 2009). Because of these limitations,

we focus the current work on the more limited goal of examin-

ing the correlation between infection status, environment and

host fecundity along a gradient in site quality, measured by

fecundity of uninfected hosts.

Pathogen prevalence, particularly BYDV-SGV, declined

strongly with site quality. For both host species, prevalence of

BYDV-SGV declined similarly with increasing precipitation,

host abundance and fecundity of uninfected hosts. The

observed negative relationship between pathogen prevalence

and site quality could arise from either host or vector

responses. From the perspective of the host, weakened hosts

with low fecundity may also be less able to mount successful

countermeasures against pathogen infections or vector attacks.

This is unlikely in the case of the B ⁄CYDVs as there has been

little evidence of general host resistance in grasses (Wang,

Abbott & Waterhouse 2000). However, this is a well-known

phenomenon in other plant and animal systems (Lochmiller,

Vestey & Boren 1993; Saino, Calza & Moller 1997; Klasing

1998; Fargallo et al. 2002; Kidd 2004; Cunningham-Rundles,

McNeeley&Moon 2005; Smith, Jones & Smith 2005).

Vector responses also could control this pattern, if vector

movement rates change with host quality (Kilpatrick et al.

2006). In our system, plant chemistry, particularly free amino

acid content, is extremely important for foraging aphid prefer-

ence, host selection andmovement among individuals (Powell,

Tosh & Hardie 2006). Stressed plants tend to accumulate rela-

tively high levels of free amino acids (Barnett & Naylor 1966;

White 1984), the primary source of dietary nitrogen for aphids

(Terra 1988). Thus plants at lower quality sites (e.g. drought-

stressed) are likely to have phloem with relatively high free

amino acid content which could lead to increased preference

by foraging aphid vectors (Powell, Tosh &Hardie 2006; Borer

et al. 2009), elevated aphid reproductive output, and ultimately

higher aphid densities at poor quality sites (Huberty & Denno

2004; Borer et al. 2009). High densities of aphids tend to

increase both short and long-distance aphid movement (Muel-

ler, Williams & Hardie 2001), a primary determinant of trans-

mission rates among hosts (Power & Gray 1995; Borer et al.

2009). Thus, abiotic drivers such as precipitation may have

indirectly increased pathogen prevalence by increasing host

stress, which would also decrease host fecundity.

We acknowledge that there are limitations inherent in the

inferences that are possible from observational data on patho-

gen prevalence. Ultimately, determining the role of vectors

would require surveys of aphid density and fecundity onmulti-

ple host species at each site. While this can be accomplished at

single sites or in a laboratory setting (Malmstrom et al. 2005b;

Borer et al. 2009), it is intractable in a survey of this spatial

scale given the irruptive nature of aphid populations. Further-

more, our prevalence data do not account for pre-sampling

mortality. As with all prevalence data, it is important to con-

sider potential biases created by pre-sampling mortality. While

the scale of this work precluded tracking the mortality of each

host throughout the season, our outplant study suggests that

roughly 80% of hosts survive the period during which

B ⁄CYDVs can be transmitted. More importantly, there was

relative little variability in survival among sites. The high

survival and low among-site variability make it unlikely that

pre-samplingmortality created substantial biases in our analyses.



While not the case in the data presented here, it is also possi-

ble to find positive correlations between pathogen prevalence

and host fecundity. For example, vectors may preferentially

select or reproduce more quickly on larger, fitter hosts

(Remold 2002). Infection by a mild pathogen may also alter

host susceptibility to secondary attack by other enemies, creat-

ing a positive correlation between fecundity and primary infec-

tion (i.e. systemic acquired resistance Agrios 1978; Durrant &

Dong 2004; Apriyanto & Potter 1990; Gibbs 1980). If these

processes occurred within each site, this would contribute to

masking any negative effects of the virus on host fecundity,

perhaps explaining the lack of correlation after controlling for

site-level variation.

Environmental mediation of pathogen impacts is a well-

known principle in epidemiology and is well studied in crop,

human and domestic animal systems (Hassell et al. 1982;

Holmes 1982; Thrusfield 2005). While studies of pathogens in

natural systems have proliferated, studies rarely measure path-

ogen impacts on host fecundity across large-scale environmen-

tal gradients. Large surveys of pathogen prevalence exist, yet it

remains unclear how to relate local-scale studies of pathogen

impacts with larger-scale studies of prevalence. Our results

demonstrate that environmental effects on host fecundity and

pathogen prevalence can confound measures of pathogen

impacts across environmental gradients. Thus, using measure-

ments of pathogen impacts from laboratory and single-site

studies to predict large-scale impacts requires integration with

the study of large-scale drivers of infection rates and fecundity

in natural systems. Similarly, observational surveys are

improvedwhen coupled withmanipulative experiments. Given

our growing awareness of the impacts of pathogens on the fate

of invasive and imperilled species (Torchin &Mitchell 2004), a

clearer understanding of pathogen impacts spanning entire

host species’ ranges is increasingly pressing.

Acknowledgements

Support for this project was provided, in part, by NSF ⁄ NIH EID 05-25666 to

E.T.B. and E.W.S. and NSF ⁄ NIHEID 05-25641 to C.E.M.We also thank the

UC Natural Reserve System (Hastings, Mclaughlin and Sedgwick), the UC

Research and Extension Centers (Hopland and Sierra Foothill) and the US

Fish and Wildlife Service Wildlife Refuge System (Finley and Baskett Slough).

E. Orling and A. Brandt assisted in the collection of field data. E. Pulley, S.

Waring andM.Welsh assisted in conducting the viral assays.

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Handling Editor: Jeremy Burdon

Supporting Information

Additional Supporting Information may be found in the online

version of this article:

Table S1. Collection site locations, viral prevalence, and mean seed

mass in uninfected hosts forBromus hordeaceus andAvena fatua. Pre-

cipitation runs fromAugust 2005 to July 2006.

Please note: Wiley-Blackwell is not responsible for the content or

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