Spatial processes in the evolution of resistance in Helicoverpa zea (Lepidoptera: Noctuidae) to Bt transgenic corn and cotton in a mixed agroecosystem: a biology-rich stochastic simulation model.
A simulation model is developed to examine the role of spatial processes in the evolution of resistance in Helicoverpa zea populations to Bt corn and Bt cotton. The model is developed from the stochastic spatially explicit Heliothis virescens model described by Peck et al. (1999), to accommodate a spatial mix of two host crops (corn and cotton), and to reflect the agronomic practices, as well as the spatial and temporal population dynamics of H. zea, in eastern North Carolina. The model suggests that selection for resistance is more intense in Bt cotton fields than in Bt corn fields. It further suggests that local gene frequencies are highly dependent on local deployment levels of Bt crops despite the high mobility of the adult insects. Region-wide average gene frequencies depend on the region-wide level of Bt deployment, so incomplete technology adoption slows the rate of resistance evolution. However, on a local scale, H. zea populations in clusters of fields in which Bt use is high undergo far more rapid evolution than populations in neighboring clusters of fields in which Bt use is low. The model suggests that farm-level refuge requirements are important for managing the risk of resistance. The model can be used as an aid in designing plans for monitoring for resistance by suggesting the appropriate distribution of monitoring locations, which should focus on areas of highest Bt crop deployment. The findings need to be placed in the context of the input parameters, many of which are uncertain or highly variable in nature, and therefore, a thorough sensitivity analysis is warranted.
Storer
Nicholas P
NP
Department of Entomology, College of Agriculture and Life Sciences, North Carolina State University, Raleigh, NC 27695-7613, USA. nstorer@dow.com
Peck
Steven L
SL
Gould
Fred
F
Van Duyn
John W
JW
Kennedy
George G
GG
D000383:Agriculture; D000818:Animals; D001413:Bacillus thuringiensis; D000083722:Bacillus thuringiensis Toxins; D001426:Bacterial Proteins / Q000235:genetics; D001427:Bacterial Toxins / Q000235:genetics; D003198:Computer Simulation; D017753:Ecosystem; D004731:Endotoxins / Q000235:genetics; D019143:Evolution, Molecular; D015870:Gene Expression; D003368:Gossypium / Q000235:genetics*; D006460:Hemolysin Proteins; D007305:Insecticide Resistance / Q000235:genetics*; D009036:Moths / Q000235:genetics*; D010572:Pest Control, Biological; D030821:Plants, Genetically Modified; D003313:Zea mays / Q000235:genetics*
D000083722:Bacillus thuringiensis Toxins; D001426:Bacterial Proteins; D001427:Bacterial Toxins; D004731:Endotoxins; D006460:Hemolysin Proteins; C030956:insecticidal crystal protein, Bacillus Thuringiensis