Trophic network architecture of root-associated bacterial communities determines pathogen invasion and plant health

Abstract

Host-associated bacterial communities can function as an important line of defence against pathogens in animals and plants. Empirical evidence and theoretical predictions suggest that species-rich communities are more resistant to pathogen invasions. Yet, the underlying mechanisms are unclear. Here, we experimentally test how the underlying resource competition networks of resident bacterial communities affect invasion resistance to the plant pathogen Ralstonia solanacearum in microcosms and in tomato plant rhizosphere. We find that bipartite resource competition networks are better predictors of invasion resistance compared with resident community diversity. Specifically, communities with a combination of stabilizing configurations (low nestedness and high connectance), and a clear niche overlap with the pathogen, reduce pathogen invasion success, constrain pathogen growth within invaded communities and have lower levels of diseased plants in greenhouse experiments. Bacterial resource competition network characteristics can thus be important in explaining positive diversity-invasion resistance relationships in bacterial rhizosphere communities.

Figure 1. Conceptual framework and experimental design.

We first characterized resource consumption patterns for both resident community species and the invading pathogen on carbon sources representative for conditions prevailing around tomato roots (a). We then assembled resident communities in all possible species combinations (b), and defined resource competition networks characteristics (connectance, nestedness and niche overlap) for all assembled communities. Filled squares denote consumed and white squares unconsumed resource, respectively. (c). Finally, every assembled community was exposed to pathogen invasion in laboratory microcosms and tomato plant rhizosphere to link resident community invasion resistance with network characteristics (d). P denotes for pathogen, B1–B5 denotes different resident community members, C1–C31 denotes different possible resident community compositions and R1–R5 denotes different tomato root exudate environments.

Figure 2. Pathogen invasion success measured in microcosm experiments.

(a) A schematic matrix capturing resource competition interactions between the pathogen (red boxes) and resident community species (black boxes); filled squares indicate that the given bacteria consume a given resource. (b) Pathogen invasion success (probability of invader establishment, visualized as heatmap showing the results of the used GLM) was lowest in non-nested and highly connected resident communities. (c) Pathogen growth in successfully invaded communities was constrained most when the resident communities had high niche overlap with the pathogen.

Figure 3. Pathogen invasion success measured in tomato plant rhizosphere.

(a) Spread of bacterial wilt plant disease in the absence and presence of resident communities (control denotes for treatment without any bacteria). Disease spread was fitted with the data by using a logistic regression to obtain three variables describing the dynamics of disease dynamics: lag time before disease onset (early stage), the exponential rate of disease spread (intermediate stage) and the asymptotic disease saturation (late stage). (b–d) Main effects and interactions between connectance, niche overlap and nestedness on disease development during each stage of infection. The R² and P values refer to the most parsimonious model fitted for each disease stage.

Figure 4. Structural equation models linking diversity and network variables with pathogen invasion success and subsequent spread of bacterial wilt plant disease.

(a,b) Results from microcosm invasion experiments (probability of invasion and pathogen density in invaded communities). (c) Results from rhizosphere invasion experiment (disease spread during the late stage of the infection). Nestedness, connectance and niche overlap with the pathogen explained most of the invasion process in microcosm invasion experiments. In the rhizosphere invasion experiment, the direct effect of diversity was important for bacterial wilt disease spread. Grey circles left of each panel denote for the proportion of the total variance explained and the numbers on the arrows denote standardized correlation coefficients. Red arrows denote for negative effect on invasion process by the high value of the given variable, and black arrows denote for positive effect on invasion process by the high value of the given variable; arrow widths correspond with the relative effect size of each variable.