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

<b>Abstract</b><br/><p>Insecticide resistance poses a major challenge to sustainable agriculture, yet studying its evolution in laboratory settings is notoriously difficult due to challenges related to maintaining large populations of pest species. While theoretical models offer valuable predictions, an experimental system for validating insecticide resistance management strategies remains lacking. Here, we explore <em>C. elegans</em> as a model organism for studying insecticide resistance evolution. We developed an <em>in silico</em> 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 <em>in silico</em> predictions generally resembled multigenerational <em>in vivo</em> resistance selection outcomes, demonstrating the feasibility of integrating <em>in vivo</em> and <em>in silico</em> 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.</p> <p>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.</p>