Optimal management strategy of insecticide resistance under various insect life histories: Heterogeneous timing of selection and interpatch dispersal

Abstract

Although theoretical studies have shown that the mixture strategy, which uses multiple toxins simultaneously, can effectively delay the evolution of insecticide resistance, whether it is the optimal management strategy under different insect life histories and insecticide types remains unknown. To test the robustness of this management strategy over different life histories, we developed a series of simulation models that cover almost all the diploid insect types and have the same basic structure describing pest population dynamics and resistance evolution with discrete time steps. For each of two insecticidal toxins, independent one‐locus two‐allele autosomal inheritance of resistance was assumed. The simulations demonstrated the optimality of the mixture strategy either when insecticide efficacy was incomplete or when some part of the population disperses between patches before mating. The rotation strategy, which uses one insecticide on one pest generation and a different one on the next, did not differ from sequential usage in the time to resistance, except when dominance was low. It was the optimal strategy when insecticide efficacy was high and premating selection and dispersal occur.

tau‐Fluvalinate and other pesticide residues in honey bees before overwintering

“…Thus, the pause of tau ‐fluvalinate use explains the low resistance of mites . Finally, it can be hypothesized that the use of a rotation of substances should be key in maintaining the efficacy of the active substances to which resistance develops …”

Effects of predator cues and pesticide resistance on the toxicity of a (bio)pesticide mixture

“…How stressors interact is crucial for the control of pest populations . We here provided evidence that resistance to the pesticide chlorpyrifos did not affect the interaction types between the pesticide chlorpyrifos and the biopesticide Bti (additive), between chlorpyrifos and predator cues (additive), and between Bti and predator cues (synergistic).…”

Insect pest control, approximate dynamic programming, and the management of the evolution of resistance

“…) and the timing and nature of insect dispersal and mating (Sudo et al. ). However, in addition to their near‐term consequences for pest population dynamics and the performance of management actions, these factors also have longer‐term implications for how resistance to control will evolve and spread (Téllez‐Rodríguez et al.…”

“…, Sudo et al. ) that may not be properly accounted for by a basic look‐ahead policy. Furthermore, the limited temporal scope of look‐ahead policies may unduly bias them against utilizing controls such as male‐selecting releases that may entail accepting short‐term costs in exchange for longer‐term gains.…”

“…Their agnosticism to the complexity of the state space allows for the specification of models as complex as required, enabling them to represent granular details, which might otherwise need to be summarized, streamlined, or omitted (Nicol et al 2010, Powell 2014. For example, lookahead policies can accommodate details such as how both wild-type and released insects distribute themselves in space, how management actions unfold across this space (Garcia et al 2016) and the timing and nature of insect dispersal and mating (Sudo et al 2018). However, in addition to their near-term consequences for pest population dynamics and the performance of management actions, these factors also have longerterm implications for how resistance to control will evolve and spread (T ellez-Rodr ıguez et al 2014, Garcia et al 2016, Sudo et al 2018) that may not be properly accounted for by a basic look-ahead policy.…”