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Transgenic crops producing insecticidal toxins are widely used to control insect pests. Their benefits would be lost if resistance to the toxins became widespread in pest populations. The most widely used resistance management method is the high-dose/refuge strategy. This requires toxin-free host plants as refuges near insecticidal crops, and toxin doses intended to be sufficiently high to kill insects heterozygous for a resistant allele, thereby rendering resistance functionally recessive. We have previously shown by mathematical modeling that mass-release of harmless susceptible (toxin-sensitive) insects engineered with repressible female-specific lethality using release of insects carrying a dominant lethal ([RIDL] Oxitec Limited, United Kingdom) technology could substantially delay or reverse the spread of resistance and reduce refuge sizes. Here, we explore this proposal in depth, studying a wide range of scenarios, considering impacts on population dynamics as well as evolution of allele frequencies, comparing with releases of natural fertile susceptible insects, and examining the effect of seasonality. We investigate the outcome for pest control for which the plant-incorporated toxins are not necessarily at a high dose (i.e., they might not kill all homozygous susceptible and all heterozygous insects). We demonstrate that a RIDL-based approach could form an effective component of a resistance management strategy in a wide range of genetic and ecological circumstances. Because there are significant threshold effects for several variables, we expect that a margin of error would be advisable in setting release ratios and refuge sizes, especially as the frequency and properties of resistant alleles may be difficult to measure accurately in the field.

Type

Journal article

Journal

J Econ Entomol

Publication Date

04/2009

Volume

102

Pages

717 - 732

Keywords

Animals, Bacterial Proteins, Computer Simulation, Crops, Agricultural, Endotoxins, Hemolysin Proteins, Insect Control, Insecta, Insecticide Resistance, Models, Biological, Organisms, Genetically Modified, Reproduction