Spatio-Temporal Variation in Landscape Composition May Speed Resistance Evolution of Pests to Bt Crops

Abstract

Transgenic crops that express insecticide genes from Bacillus thuringiensis (Bt) are used worldwide against moth and beetle pests. Because these engineered plants can kill over 95% of susceptible larvae, they can rapidly select for resistance. Here, we use a model for a pyramid two-toxin Bt crop to explore the consequences of spatio-temporal variation in the area of Bt crop and non-Bt refuge habitat. We show that variability over time in the proportion of suitable non-Bt breeding habitat, Q, or in the total area of Bt and suitable non-Bt habitat, K, can increase the overall rate of resistance evolution by causing short-term surges of intense selection. These surges can be exacerbated when temporal variation in Q and/or K cause high larval densities in refuges that increase density-dependent mortality; this will give resistant larvae in Bt fields a relative advantage over susceptible larvae that largely depend on refuges. We address the effects of spatio-temporal variation in a management setting for two bollworm pests of cotton, Helicoverpa armigera and H. punctigera, and field data on landscape crop distributions from Australia. Even a small proportion of Bt fields available to egg-laying females when refuges are sparse may result in high exposure to Bt for just a single generation per year and cause a surge in selection. Therefore, rapid resistance evolution can occur when Bt crops are rare rather than common in the landscape. These results highlight the need to understand spatio-temporal fluctuations in the landscape composition of Bt crops and non-Bt habitats in order to design effective resistance management strategies.

A perspective on management of Helicoverpa armigera: transgenic Bt cotton, IPM, and landscapes

“…Recently, models considered the consequences of spatio‐temporal drivers of H. armigera populations and the implications for resistance evolution and management at the landscape scale . Results from an analytical model showed that variability over time in the proportion of suitable non‐Bt breeding habitat or changes over time in the total area of Bt cotton, a regular and real occurrence in Australia, can increase the overall rate of resistance evolution by causing short‐term bursts of intense selection . This has implications for the Resistance Management Plan, and critical importance for sufficient and well‐functioning refuges.…”

“…66 -68 Results from an analytical model showed that variability over time in the proportion of suitable non-Bt breeding habitat or changes over time in the total area of Bt cotton, a regular and real occurrence in Australia, can increase the overall rate of resistance evolution by causing short-term bursts of intense selection. 68 This has implications for the Resistance Management Plan, and critical importance for sufficient and well-functioning refuges. To explore the behavioural mechanisms driving these patterns, spatially explicit individual-based landscape scale simulation models were developed to link moth movement and oviposition to landscape structure (e.g.…”

Genetically modified soybean expressing insecticidal protein (Cry1Ac): Management risk and perspectives

“…The concern with the effects of other factors is not exclusive to Bt soybeans, but to all Bt plants. It is extremely important to adopt adequate management procedures in each region, because it is necessary to consider the differences in geography, climate, size of cultivated area, number of transgenic species cultivated, biology of insect pests, and spatial and temporal genetic variability of the target species (Omoto et al 2016;Ives et al 2017). …”

“…A more complex solution requires a more accurate assessment of the effective contribution of natural enemies in reducing the impact of primary and secondary pests as well as changes in the methods of large-scale plant cultivation. In addition, it should take into account other information, such as geographical aspects of cultivated areas, target pest biology, and genetic variability (Omoto et al 2016;Ives et al 2017). …”

An agent-based model of insect resistance management and mitigation for Bt maize: A social science perspective

“…This paper focuses on deriving new insights into policy options for mitigating insect resistance once it has evolved, and emphasizes the significance of social factors for questions relevant to policymakers [22]. As a result, social components are richer than existing models that use individual-based modeling to incorporate social factors [25], while the biological aspects of the model are simpler than other models focusing on biological processes [26–28].…”