Maximizing the durability of crop disease resistance genes in the face of pathogen evolution is a major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not well-specialized. The factors that interact with and determine the magnitude of this spatial suppressive effect are not currently well understood, however, leading to uncertainty over the pathosystems where such a strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant cultivars, and the way in which this effect depends on the parameters governing the pathosystem of interest. Our multiseason semidiscrete epidemiological model tracks spatial spread of wild-type and resistance-breaking pathogen strains, and incorporates a localized reservoir of inoculum, as well as the effects of within and between field transmission. The pathogen dispersal characteristics, any fitness cost(s) of the resistance-breaking trait, the efficacy of host resistance, and the length of the timeframe of interest all influence the strength of the spatial diversification effect. A key result is that spatial diversification has the strongest beneficial effect at intermediate fitness costs of the resistance-breaking trait, an effect driven by a complex set of nonlinear interactions. On the other hand, however, if the resistance-breaking strain is not fit enough to invade the landscape, then a partially effective resistance gene can result in spatial diversification actually worsening the epidemic. These results allow us to make general predictions of the types of system for which spatial diversification is most likely to be cost-effective, paving the way for potential economic modeling and pathosystem specific evaluation. These results highlight the importance of studying the effect of genetics on landscape scale spatial dynamics within host−pathogen disease systems. [Formula: see text] Copyright © 2020 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .
Genetic insect control, such as self-limiting RIDL2 (Release of Insects Carrying a Dominant Lethal) technology, is a development of the sterile insect technique which is proposed to suppress wild populations of a number of major agricultural and public health insect pests. This is achieved by mass rearing and releasing male insects that are homozygous for a repressible dominant lethal genetic construct, which causes death in progeny when inherited. The released genetically engineered (‘GE’) insects compete for mates with wild individuals, resulting in population suppression. A previous study modelled the evolution of a hypothetical resistance to the lethal construct using a frequency-dependent population genetic and population dynamic approach. This found that proliferation of resistance is possible but can be diluted by the introgression of susceptible alleles from the released homozygous-susceptible GE males. We develop this approach within a spatial context by modelling the spread of a lethal construct and resistance trait, and the effect on population control, in a two deme metapopulation, with GE release in one deme. Results show that spatial effects can drive an increased or decreased evolution of resistance in both the target and non-target demes, depending on the effectiveness and associated costs of the resistant trait, and on the rate of dispersal. A recurrent theme is the potential for the non-target deme to act as a source of resistant or susceptible alleles for the target deme through dispersal. This can in turn have a major impact on the effectiveness of insect population control.
Abstract1 Maximising the durability of crop disease resistance genes in the face of pathogen evolution is a 2 major challenge in modern agricultural epidemiology. Spatial diversification in the deployment of 3 resistance genes, where susceptible and resistant fields are more closely intermixed, is predicted 4 to drive lower epidemic intensities over evolutionary timescales. This is due to an increase in the 5 strength of dilution effects, caused by pathogen inoculum challenging host tissue to which it is not 6 well-specialised. The factors that interact with and determine the magnitude of this spatial effect are 7 not currently well understood however, leading to uncertainty over the pathosystems where such a 8 strategy is most likely to be cost-effective. We model the effect on landscape scale disease dynamics 9 of spatial heterogeneity in the arrangement of fields planted with either susceptible or resistant 10 cultivars, and the way in which this effect depends on the parameters governing the pathosystem 11 of interest. Our multi-season semi-discrete epidemiological model tracks spatial spread of wild-type 12 and resistance breaking pathogen strains, and incorporates a localised reservoir of inoculum, as well 13 as the effects of within and between field transmission. The pathogen dispersal characteristics, 14 any fitness cost(s) of the resistance breaking trait, the efficacy of host resistance, and the length 15 of the timeframe of interest, all influence the strength of the spatial diversification effect. These 16interactions, which are often complex and non-linear in nature, produce substantial variation in the 17 predicted yield gain from the use of a spatial diversification strategy. This in turn allows us to 18 make general predictions of the types of system for which spatial diversification is most likely to be 19 cost-effective, paving the way for potential economic modelling and pathosystem specific evaluation. 20 1 These results highlight the importance of studying the effect of genetics on landscape scale spatial 21 dynamics within host-pathogen disease systems. 22
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