Average seed viability as a function of watering and fungicide treatments, from the laboratory experiment.

<p>The top left panel is all species combined, and the rest are individual species. Moisture treatments are dry, pulse, and wet, and fungicide treatments are with (gray points) and without (black points) fungicide. Error bars are ±1 standard error.</p

Estimates of regression parameters from the best fit models for the laboratory experiment.

<p>Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.</p><p>The intercept represents the mean for the baseline scenario of never watered with fungicide added. The subsequent parameters represent the change relative to the baseline. The first row lists the parameter estimates for the GLMM with species- and observation- level random effects. The subsequent rows list the best fit quasi-binomial GLM for each species separately. The dash indicates a factor that was not included in the best-fit model. Species listed in bold followed the survival pattern never watered > watered once > continuous watering.</p

Average germination fraction by species as a function of soil moisture, from the field survey.

<p>Points are the average germination fraction pooled by site. Black points are coastal sites and gray points are inland sites. Lines are the fitted soil moisture by location models for each species except <i>Brassica</i>, which shows the (moisture)² model because it was a better fit.</p

Appendix A. Additional methods and sensitivity analysis.

Additional methods and sensitivity analysis

Estimates of the regression parameters from the best fit models for the field survey.

<p>Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.</p><p>The intercept (<i>α</i> from equation 1) represents the baseline scenario of coastal location, CSS vegetation, and average soil moisture. The subsequent coefficients (<i>β</i>'s from equation 1) represent change in the intercept relative to the baseline. The moisture coefficients represent responses to deviations from the mean value. The first row lists the estimates of fixed effects from the best fit GLMM for all species combined. The subsequent rows list the best GLMM for each species separately. The dash indicates a factor that was not included in the best-fit model.</p

Estimates of regression parameters from the best fit models for the field experiment.

<p>Significance codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1.</p><p>The intercept represents the mean for the baseline scenario of thatch removed, summer, and fungicide added. The subsequent coefficients represent the change relative to the baseline. The first row lists the parameter estimates for the GLMM with species- and bag- level random effects. The subsequent rows list the best fit quasibinomial GLM for each species separately. The dash indicates a factor that was not included in the best-fit model.</p

Average germination fraction by species, from the field survey.

<p>Different points represent each combination of location (coastal vs. inland) and vegetation type (coastal sage scrub, gray points vs. grassland, black points) for each species. Error bars are ±1 standard error.</p

Above- and below-ground biomass versus number of viruses infecting a plant.

<p>A, above-ground biomass and B, below-ground biomass, both in grams. Black points are <i>Bromus hordeaceus</i> and gray points are <i>Nassella pulchra</i>. Data are from all plants in the experiment. Error bars are +/- 1 SE.</p

MAV inoculation success versus plant species and PAV (A) and RPV (B) infection status.

<p>Other plot features are the same as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0134355#pone.0134355.g001" target="_blank">Fig 1</a>.</p

Differential Impacts of Virus Diversity on Biomass Production of a Native and an Exotic Grass Host

<div><p>Pathogens are common and diverse in natural communities and have been implicated in the success of host invasions. Yet few studies have experimentally measured how pathogens impact native versus exotic hosts, particularly when individual hosts are simultaneously coinfected by diverse pathogens. To estimate effects of interactions among multiple pathogens within host individuals on both transmission of pathogens and fitness consequences for hosts, we conducted a greenhouse experiment using California grassland species: the native perennial grass <i>Nassella</i> (<i>Stipa</i>) <i>pulchra</i>, the exotic annual grass <i>Bromus hordeaceus</i>, and three virus species, <i>Barley yellow dwarf virus-PAV</i>, <i>Barley yellow dwarf virus-MAV</i>, and <i>Cereal yellow dwarf virus-RPV</i>. In terms of virus transmission, the native host was less susceptible than the exotic host to MAV. Coinfection of PAV and MAV did not occur in any of the 157 co-inoculated native host plants. In the exotic host, PAV infection most strongly reduced root and shoot biomass, and coinfections that included PAV severely reduced biomass. Infection with single or multiple viruses did not affect biomass in the native host. However, in this species the most potentially pathogenic coinfections (PAV + MAV and PAV + MAV + RPV) did not occur. Together, these results suggest that interactions among multiple pathogens can have important consequences for host health, which may not be predictable from interactions between hosts and individual pathogens. This work addresses a key empirical gap in understanding the impact of multiple generalist pathogens on competing host species, with potential implications for population and community dynamics of native and exotic species. It also demonstrates how pathogens with relatively mild impacts independently can more substantially reduce host performance in coinfection.</p></div