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

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 C. elegans as a model
organism for studying insecticide 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.