Analytical models of weed biocontrol with sterilizing fungi: the consequences of differences in weed and pathogen life‐histories

Abstract

A model of the population dynamics of healthy weed plants, weed seeds in the soil, pathogen-infected weed plants and pathogen spores in the soil, was devised to investigate interactions that are important for the success of biocontrol with pathogens that prevent seed set. Three particular features of the host-pathogen interaction were examined in detail: the form of the density dependent relationship which determined seed and spore production, the host life stage at which infection could occur, and the relative competitive abilities of healthy and infected host plants. It was found that, when both weed and pathogen coexist, the equilibrium abundance of the weed in the presence of the pathogen was independent of the form of the relationship between plant density and seed or spore production. However, the form of this relationship did affect estimated equilibrium densities in the absence of biocontrol, and also affected the parametrizations under which both host and pathogen could coexist. Parameters derived from experiments with isolated host plants may therefore be sufficient to assess the biocontrol potential of new pathogens along with knowledge of densities achievable when the weed is uncontrolled. The form of the relationship used to control seed and spore production also had a marked influence on the range of parameter values over which the pathogen could persist. Other control measures were represented by changes to appropriate parameter values, e.g. weeding was represented by a change in weed death rate. With one exception, the use of additional control measures was not antagonistic to biocontrol. Often, however, the combined effect was less than additive, and the existence of synergy (where the combined effects are more than additive) was critically dependent on the form of the relationship of the rate of seed production per plant with density and the efficiency of the other control measures.

Mathematical models are a powerful method to understand and control the spread of Huanglongbing

“…Models can compare, using a common currency, the potential impact of different interventions by examining sensitivity to parameters that represent different strategies. Previous studies have strategically used models to disentangle the potential role of vector migration, spillover from alternative hosts, and control measures (spraying, netting, phytosanitation) across a range of diseases (e.g., Fishman et al, 1983; Kendall, Brain & Chinn, 1992; Holt et al, 1997, Holt, Colvin & Muniyappa, 1999; Smith et al, 1998; Robert, Woodford & Ducray-Bourdin, 2000; Zhang, Holt & Colvin, 2001; Jeger, 2000; Smith & Holt, 1997). For illustration, we highlight a few key examples here.…”

Within‐population variation in resistance of Digitaria sanguinalis to Ustilago syntherismae resulting from different modes of seed germination and environment

“…Systemic infections of grasses by sterilizing fungi, such as many smut diseases, can have severe effects on host plant abundance and distribution, depending largely on the particular life histories of the plant and the pathogen (Smith & Holt, 1997;Antonovics, 2009). Systemic smuts can destroy the plant inflorescences by producing a mass of spores, and so the dynamics of the interaction could be very different in plant populations depending on whether the species are capable of clonal growth or not.…”

Original Article. Effects of loose kernel smut caused by Sporisorium cruentum onrhizomes of Sorghum halepense

“…Th ese researchers stated that in the presence of intraspecifi c competition, weeds retard the formation of rhizomes. Smith and Holt (1997) established that the use of additional weed control and biological control are not antagonistic, but rather, that there are synergistic eff ects (where the combined eff ects are more than additive) that depend on the reproductive rate of weeds and the density at which they grow. In the present work, we also noted that the ADM was reduced because the diseased plants had lower height than controls.…”