bioRxivEvolutionary BiologyPreprint
Entomopathogen-host evolution and implications for biopesticide resistance management
Abstract
Insecticide resistance evolution is becoming increasingly problematic globally. With chemical insecticides, attempts to combat resistance involves developing compounds with novel modes of action, or increasing rates to overcome partial resistance. While pests can develop resistance to pathogens used as biopesticides, these "pesticides" can be subjected to evolutionary selection pressure as well and may be able to adapt countermeasures to overcome pest resistance. Here, we consider two scenarios: 1) a single trait governs an arms race between pest and parasite, and 2) an epidemiological scenario where each, pathogen transmission and virulence, are governed by host and pathogen traits. Considering the single-trait parasite attack scenario, the evolving parasite is able to overcome resistance in the pest population and effectively suppress host population abundance. In this case, overcoming biopesticide resistance may be possible from parasite evolution to resistant hosts. In contrast, when transmission and abundance are allowed to vary independently in an epidemiological model, different pathogen traits promote different types of resistance development in the host - more contagious pathogens promote pathogen-tolerant (low mortality susceptibility) hosts, while less contagious pathogens promote pathogen-resistant (low transmission susceptibility) hosts. Pathogen-tolerant hosts are particularly detrimental to control programs, because they can quickly outcompete wild types by promoting infection in wild type populations. Furthermore, because evolution of pathogen-tolerance in pests can benefit pathogens through increasing infection prevalence, we do not expect pathogen evolution to improve control. Thus, the keys to biopesticide management depend on the virulence-transmission trade-off and whether hosts evolve to better prevent or survive infection.
- research focus: host-pathogen & immune response
- approach: statistical model
- approach: mechanistic / dynamical model
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