Biocontrol Traits Correlate With Resistance to Predation by Protists in Soil Pseudomonads.

Root-colonizing bacteria can support plant growth and help fend off pathogens. It is clear that such bacteria benefit from plant-derived carbon, but it remains ambiguous why they invest in plant-beneficial traits. We suggest that selection via protist predation contributes to recruitment of plant-beneficial traits in rhizosphere bacteria. To this end, we examined the extent to which bacterial traits associated with pathogen inhibition coincide with resistance to protist predation. We investigated the resistance to predation of a collection of Pseudomonas spp. against a range of representative soil protists covering three eukaryotic supergroups. We then examined whether patterns of resistance to predation could be explained by functional traits related to plant growth promotion, disease suppression and root colonization success. We observed a strong correlation between resistance to predation and phytopathogen inhibition. In addition, our analysis highlighted an important contribution of lytic enzymes and motility traits to resist predation by protists. We conclude that the widespread occurrence of plant-protective traits in the rhizosphere microbiome may be driven by the evolutionary pressure for resistance against predation by protists. Protists may therefore act as microbiome regulators promoting native bacteria involved in plant protection against diseases.

Predator traits determine food-web architecture across ecosystems.

Predator-prey interactions in natural ecosystems generate complex food webs that have a simple universal body-size architecture where predators are systematically larger than their prey. Food-web theory shows that the highest predator-prey body-mass ratios found in natural food webs may be especially important because they create weak interactions with slow dynamics that stabilize communities against perturbations and maintain ecosystem functioning. Identifying these vital interactions in real communities typically requires arduous identification of interactions in complex food webs. Here, we overcome this obstacle by developing predator-trait models to predict average body-mass ratios based on a database comprising 290 food webs from freshwater, marine and terrestrial ecosystems across all continents. We analysed how species traits constrain body-size architecture by changing the slope of the predator-prey body-mass scaling. Across ecosystems, we found high body-mass ratios for predator groups with specific trait combinations including (1) small vertebrates and (2) large swimming or flying predators. Including the metabolic and movement types of predators increased the accuracy of predicting which species are engaged in high body-mass ratio interactions. We demonstrate that species traits explain striking patterns in the body-size architecture of natural food webs that underpin the stability and functioning of ecosystems, paving the way for community-level management of the most complex natural ecosystems.

Analyzing pathogen suppressiveness in bioassays with natural soils using integrative maximum likelihood methods in R.

The potential of soils to naturally suppress inherent plant pathogens is an important ecosystem function. Usually, pathogen infection assays are used for estimating the suppressive potential of soils. In natural soils, however, co-occurring pathogens might simultaneously infect plants complicating the estimation of a focal pathogen's infection rate (initial slope of the infection-curve) as a measure of soil suppressiveness. Here, we present a method in R correcting for these unwanted effects by developing a two pathogen mono-molecular infection model. We fit the two pathogen mono-molecular infection model to data by using an integrative approach combining a numerical simulation of the model with an iterative maximum likelihood fit. We show that in presence of co-occurring pathogens using uncorrected data leads to a critical under- or overestimation of soil suppressiveness measures. In contrast, our new approach enables to precisely estimate soil suppressiveness measures such as plant infection rate and plant resistance time. Our method allows a correction of measured infection parameters that is necessary in case different pathogens are present. Moreover, our model can be (1) adapted to use other models such as the logistic or the Gompertz model; and (2) it could be extended by a facilitation parameter if infections in plants increase the susceptibility to new infections. We propose our method to be particularly useful for exploring soil suppressiveness of natural soils from different sites (e.g., in biodiversity experiments).

Unravelling Linkages between Plant Community Composition and the Pathogen-Suppressive Potential of Soils.

Plant diseases cause dramatic yield losses worldwide. Current disease control practices can be deleterious for the environment and human health, calling for alternative and sustainable management regimes. Soils harbour microorganisms that can efficiently suppress pathogens. Uncovering mediators driving their functioning in the field still remains challenging, but represents an essential step in order to develop strategies for increased soil health. We set up plant communities of varying richness to experimentally test the potential of soils differing in plant community history to suppress the pathogen Rhizoctonia solani. The results indicate that plant communities shape soil-disease suppression via changes in abiotic soil properties and the abundance of bacterial groups including species of the genera Actinomyces, Bacillus and Pseudomonas. Further, the results suggest that pairwise interactions between specific plant species strongly affect soil suppressiveness. Using structural equation modelling, we provide a pathway orientated framework showing how the complex interactions between plants, soil and microorganisms jointly shape soil suppressiveness. Our results stress the importance of plant community composition as a determinant of soil functioning, such as the disease suppressive potential of soils.