Modeling Insect Adaptation to Crop Rotation and Transgenic Crops

Managing infestations of certain pests through crop rotation (e.g., planting corn, Zea mays L., and a nonhost crop such as soybean, Glycine max L., in alternative years) has historically been an effective approach; however, the effectiveness of crop rotation for managing many pests (e.g., western corn rootworm, Diabrotica virgifera virgifera LeConte) has begun to fail in many areas of the United States. Recent experimental investigations and model development efforts have shed some light on the population dynamics and behavior of the rotation-resistant phenotype. In addition, there has been growing interest in understanding and modeling pest resistance mechanisms against genetically modified insecticidal pest-resistant crops. Transgenic crops express Bt genes which produce insecticidal proteins (Bt toxins) and provide pest control; however, widespread and long-term planting of Bt crops makes pest resistance more probable because it increases pest exposure to Bt toxins in space and over time.

Computer simulations are being increasingly relied upon to reveal key factors and integrate them to evaluate the impacts of invasions from crop rotation and Bt-resistant insects. Initial modeling attempts involved the creation of simple spreadsheet-based models of adult insect behavior and population genetics to study the development of insect resistance to transgenic corn. Some models explicitly considered spatiotemporal dynamics in an agroecosystem consisting of transgenic Bt plants, insects susceptible to Bt toxins, and adapted Bt-resistant insects that can grow on Bt plants. Model results showed that invasion of Bt-resistant insects leads to spatially inhomogeneous distributions of plants and insects. In addition, spatially averaged plant biomass was shown to be strongly dependent on the duration of the Bt-resistant insect reproduction period. Concurrent to modeling insect resistance to transgenic corn, modeling efforts also focused on explaining how rotation resistance may have developed in a landscape with multiple crops. Simulation results indicated that behavioral resistance only developed at high levels of rotation, and that diverse landscapes may delay resistance to crop rotation depending on the nature of the genetic system. Model shortcomings included the assumption that a very simple genetic system is responsible for evolution of the behavioral changes and rotation resistance.

Until very recently, no models focused on the simultaneous evolution of resistance to both crop rotation and transgenic crops. However, new modeling efforts in the past few years have expanded the simulation models described above to evaluate the risk of resistance by pests to both transgenic crops and crop rotations in areas with or without rotation-resistant

Intake phenotypes. Model analysis focused on several aspects including: (1) simulating the use of transgenic corn in rotated cornfields, continuous cornfields, or both; (2) simulating whether crop rotation can affect resistance to transgenic corn (and vice versa); and (3) simulating the use of transgenic corn in areas without rotation-resistant phenotypes. Studies were conducted in a hypothetical model landscape similar to that shown in Figure 2 where transgenic corn is planted to both continuous cropping and rotated (corn-soybean in a 2 year rotation) fields. The location of the refuge and transgenic fields remain the same in continuous corn and are randomly determined in the rotated cornfield. Model results indicated that transgenic corn, when planted in only rotated cornfields, is a robust strategy to prevent resistance to both crop rotation and transgenic corn in areas with rotation-resistant phenotypes. This strategy was found to be effective at preventing resistance to crop rotation because it places increased selection pressure on rotation-resistant pests emerging in first-year cornfields. In addition, the continuous corn helped serve as an additional refuge to prevent resistance to transgenic corn.

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