Dataset for: Expanding risk predictions of pesticide resistance evolution in arthropod pests with a proxy for selection pressure

This archive comprises the data and scripts required to reproduce the analyses presented in Thia et al., “Expanding risk predictions of pesticide resistance evolution in arthropod pests with a proxy for selection pressure”. A version of this manuscript exists on BioRXiv, https://www.biorxiv.org/content/10.1101/2022.04.18.488707v3, and the final published version is in the Journal of Pest Science, https://doi.org/10.1007/s10340-023-01593-w.
In this study, we explore the use of pesticide product registrations as a predictor of pesticide resistance status in agricultural arthropod pests from Australia and the USA. We find that pesticide product registrations are positively associated with resistance status. We suggest that pesticide product registrations may represent a proxy for chemical selection pressure acting on agricultural pests. Our work suggests that publicly available chemical databases can be mined to obtain important information about selection in agro-ecosystems that can be us..., Pesticide registrations were mined from the APVMA (Australian Pesticide and Veterinary Medicines Authority) and EPA (Environmental Protection Agency) for Australian and USA pests, respectively. Resistance records were obtained from the APRD (Arthropod Pesticide Resistance Database) with permission from David Mota-Sanchez and John Wise. Ecological traits (phagy and voltinism) were obtained from literature searches and from data reported in Hardy et al. (2018) Evolutionary Applications and Crossley et al. (2020) Evolutionary Applications.
We modelled pesticide resistance status as a Bernoulli response in Bayesian logistic regression models, with pesticide product registrations, phagy, and voltinism as fixed effects, and taxonomy and chemical mode of action as random effects. Cross-validation analyses were used to obtain area-under-the-curve scores, which were used to assess predictive power with 100 training-testing data partitions., , # Dataset for: Expanding risk predictions of pesticide resistance evolution in arthropod pests with a proxy for selection pressure

[https://doi.org/10.5061/dryad.7pvmcvdwc](https://doi.org/10.5061/dryad.7pvmcvdwc)

## Description of the data and file structure

This archive comprises the data and scripts required to reproduce the analyses presented in Thia et al., “Expanding risk predictions of pesticide resistance evolution in arthropod pests with a proxy for selection pressure”. A version of this manuscript exists on BioRXiv, [https://www.biorxiv.org/content/10.1101/2022.04.18.488707v3](https://www.biorxiv.org/content/10.1101/2022.04.18.488707v3), and the final published version is in the Journal of Pest Science, [https://doi.org/10.1007/s10340-023-01593-w](https://doi.org/10.1007/s10340-023-01593-w).

In this study, we explore the use of pesticide product registrations as a predictor of pesticide resistance status in agricultural arthropod pests from Australia and the USA. We find t...,