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

Abstract

Ecological decision problems, such as those encountered in agriculture, often require managing conflicts between short‐term costs and long‐term benefits. Dynamic programming is an ideal method for optimally solving such problems but agricultural problems are often subject to additional complexities that produce state spaces intractable to exact solutions. In contrast, look‐ahead policies, a class of approximate dynamic programming (ADP) algorithm, may attempt to solve problems of arbitrary magnitude. However, these algorithms focus on a temporally truncated caricature of the full decision problem over a defined planning horizon and as such are not guaranteed to suggest optimal actions. Thus, look‐ahead policies may offer promising means of addressing detail‐rich ecological decision problems but may not be capable of fully utilizing the information available to them, especially in scenarios where the best short‐ and long‐term solutions may differ. We constructed and applied look‐ahead policies to the management of a hypothetical, stage‐structured, continually reproducing, agricultural insect pest. The management objective was to minimize the combined costs of management actions and crop damage over a 16‐week growing season. The manager could elect to utilize insecticidal sprays or one of six release ratios of male‐selecting transgenic insects where the release ratio determines the number of transgenic insects to be released for each wild‐type male insect in the population. Complicating matters was the expression of insecticide resistance at non‐trivial frequencies in the pest population. We assessed the extent to which look‐ahead policies were able to recognize the potential threat of insecticide resistance and successfully integrate insecticides and transgenic releases to capitalize upon their respective benefits. Look‐ahead policies were competent at anticipating and responding to ecological and economic information. Policies with longer planning horizons made fewer, better‐timed insecticidal sprays and made more frequent transgenic releases, which consequently facilitated lower resistance allele frequencies. However, look‐ahead policies were ultimately inefficient resistance managers, and directly responded to resistance only when it was dominant and prevalent. Effective long‐term agricultural management requires the capacity to anticipate and respond to the evolution of resistance. Look‐ahead policies can accommodate all the information pertinent to making the best long‐term decision but may lack the perspective to actually do so.

Combining refuges with transgenic insect releases for the management of an insect pest with non-recessive resistance to Bt crops in agricultural landscapes

“…The decreased frequency of releases reduced the introgression of susceptibility alleles and subjected the population to longer periods of selection for resistance between releases, enabling the resistance allele to reach greater frequencies. While our model shows that population-centred release strategies can enhance resistance management efforts, even when assumptions of the HDR strategy are violated, developing more nuanced management programmes that find optimal solutions which minimise both the short-term crop losses and longer term resistance costs (Hackett and Bonsall, 2019) would greatly enhance the benefits to both population and resistance management benefits of transgenic insect releases.…”

The sterile insect technique is protected from evolution of mate discrimination

“…The success of the screw worm program may attest to the robustness of suppression by constant sterile releases and easy increases in the release rates when needed. In this sense, the SIT may be a good candidate for management through dynamic programming ( Hackett & Bonsall, 2019 ).…”

AlepPBP2, but not AlepPBP3, may involve in the recognition of sex pheromones and maize volatiles inAthetis lepigone

“…Synthetic insecticides are now the primary agents for their control, but the excessive use of insecticides may lead to the damage of ecosystems and increase the resistance levels of the target pest. Therefore, it is necessary to strengthen dynamic monitoring, timely make pest predictions to reduce the damage or develop novel green pest control strategies in modern pest management (Sarfraz et al ., 2005; Choo et al ., 2018; Hackett and Bonsall, 2019). The development of novel green behavioral inhibitors based on olfactory genes has become a hot spot research field for pest control (Pelosi et al ., 2018; Caballero-Vidal et al ., 2021).…”