2006 Annual Report 4d.Progress report. This report serves to document research conducted under a Specific Cooperative Agreement between ARS and the University of Illinois to conduct research related to an Interagency Agreement (60-3625-4-0574) with the Environmental Protection Agency (National Risk Management Research Lab, Cincinnati, OH) and USDA-ARS. Additional details of research can be found in the report for the parent CRIS #3625-22000-016-00D, Ecologically-Based Management of Insect Pests of Corn. The purpose of this agreement is to contribute to a scientific information framework for managing insect resistance to transgenic crops. This information can provide enhanced support to the public in its deliberations about the use of transgenic plants. This will include an evaluation and integration of insect resistance models. Contributions also will be made by scientists from Brigham Young University, the University of Wisconsin, Pennsylvania State University, the University of Minnesota, Mississippi State University, the University of Arizona, and North Carolina State University. Two talks concerning the evaluation of IRM models were presented. The first, "Evaluation of models for insect resistance to transgenic crops" was presented in December 2005 at the Annual Meeting of the Entomological Society of America in Ft. Lauderdale, FL. The second, "Using Analytical Models of Evolutionary Theory to Extend the Application of Simulation Models for IRM" was presented in March 2006 at the North Central Branch meeting of the Entomological Society of America in Bloomington, IL. In conjunction with the North Central Branch meeting, participating modelers met to preview some of the evaluation standards and visualization software that have been developed. In June 2006, scientists met to review the theoretical standards, explore methods of using the visualization software, and discuss potential field evaluation techniques that could be developed from these models. Procedures and protocols were developed to verify that Insect Resistance Management models conform to the theoretical standards for evolution and population genetics. Verification is a necessary component of all modeling that has been neglected in developing resistance management models. The procedures are simple and independent of model structure (i.e., spatially explicit, stochastic, etc.). Models in this project will be tested against these theoretical standards. If a model does not conform to the theoretical standards, it would be incumbent upon the author to identify and justify the model structures that cause the model to deviate. Visualization software has been developed in Excel to better examine how the components of the model are affecting evolutionary risk. Sequential comparisons of population densities from the model provide methods of measuring various biological and ecological properties of the modeled system. For instance, the level of inbreeding in the modeled population can be measured by comparing genotypic population densities. This is a genetic characteristic of the population that could be measured in field samples of the pest populations, independent of the allele frequency for resistance. Similarly, genotypic and population fitness measurements of the modeled population can be assessed using density comparison techniques. Unlike many examples in the literature where fitness values are assumed to be constants, the fitness values derived from these models vary within and between growing seasons similar to what theoreticians have proposed. Fitness costs emerge in some of these models from the complex interactions between the allele frequency of resistance, the insects' dispersal behavior, and the configuration of the landscape. Many of these fitness values exhibit regular-cyclic patterns of change within growing seasons as the insects interact with the landscape configuration. These patterns also change as the allele frequency changes and the population evolves toward resistance in the model. The visualization software has proved useful in observing these patterns in fitness values. Quantifying the patterns of change in genotypic fitness and relating these patterns to the input parameters in the models (landscape usage, pest behavior, and pest biology) provides a more comprehensive method of assessing the risk of resistance evolution. Statistical analysis of these changing patterns of fitness should provide a theoretical basis for developing resistance management practices that are sustainable and that better identify management practices of greatest risk.