Modeling dose-response relationships in biological control: partitioning host responses to the pathogen and...

ABSTRACT Breeding plants to improve the effectiveness of biocontrol agents is a promising approach to enhance disease suppression by microorganisms. Differences in biocontrol efficacy among cultivars suggest there is genetic variation for this trait within crop germplasm. The ability to quantify host differences in support of biological control is influenced by variation in host response to the pathogen and the dose of pathogen and biocontrol agent applied to the host. To assess the contribution of each of these factors to successful biocontrol interactions, we measured disease over a range of pathogen (Pythium) and biocontrol agent (Bacillus cereus UW85) inoculum doses. We fit dose-response models to these data and used model parameter estimates to quantify host differences in response to the pathogen and biocontrol agent. We first inoculated eight plant species separately with three species of Pythium and evaluated three dose-response models for their ability to describe the disease response to pathogen inoculum level. All three models fit well to at least some of the host-pathogen combinations; the hyperbolic saturation model provided the best overall fit. To quantify the host contribution to biological control, we next evaluated these models with data from a tomato assay, using six inbred tomato lines, P. torulosum, and UW85. The lowest dose of pathogen applied revealed the greatest differences in seedling mortality among the inbred lines, ranging from 40 to 80%. The negative exponential (NE) pathogen model gave the best fit to these pathogen data, and these differences corresponded to model parameter values, which quantify pathogen efficiency, of 0.023 and 0.091. At a high pathogen dose, we detected the greatest differences in biocontrol efficacy among the inbred lines, ranging from no effect to a 68% reduction in mortality. The NE pathogen model with a NE biocontrol component, the NE/NE biocontrol model, gave the best fit to these biocontrol data, and these reductions corresponded to model parameter values, which quantify biocontrol efficiency, of 0.00 and 0.038, respectively. There was no correlation between the host response to the pathogen and biocontrol agent for these inbred lines. This work demonstrates the utility of epidemiological modeling approaches for the study of biological control and lays the groundwork to employ manipulation of host genetics to improve biocontrol efficacy.