Data from: A proof-of-concept experimental-theoretical model to predict pesticide resistance evolution

We developed an in silico population genetics model and tested its predictive power in laboratory experiments, comparing the computational predictions to experimental resistance selection dynamics. Two compounds with distinct modes of action were tested to assess the generalizability of this system across different resistance mechanisms. Our results showed that in silico predictions generally resembled multigenerational in vivo resistance selection outcomes, demonstrating the feasibility of integrating in vivo and in silico modelling approaches in resistance research.

By bridging the gap between theoretical and empirical research, this framework paves the way for addressing a wide range of open questions in resistance management, permitting the development of better-informed and more effective resistance management strategies for the agricultural industry. The data archived here contains information relevant to the wild isolate chemical susceptibility screen, baseline dose-survival, and fitness data on PD4792, SR42, and JD608 strains, which were further characterised in this study, as well as the data on experimental microevolution of chemical resistance.