DOI: 10.5061/dryad.7pvmcvdwc
title: Dataset for: Expanding risk predictions of pesticide resistance evolution in arthropod pests with a proxy for selection pressure

description: 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 used to guide proactive management strategies.
description: 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) <em>Evolutionary Applications</em> and Crossley et al.
 (2020) <em>Evolutionary Applications</em>.
 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.
description: # 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 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 used to guide proactive management strategies. ### Files and variables #### File: 01a\_Analyses\_Aus.zip **Description:** This ZIP files contains all the data need to run analyses of Australian pests. #### File: 01b\_Analyses\_USA.zip **Description:** This ZIP files contains all the data need to run analyses of USA pests. #### File: 01c\_Analyses\_Summarise.zip **Description:** This ZIP file contains all the data needed to run the summary of results produced from Australian and USA pests. #### File: Project\_Info.pdf (Zenodo) **Description:** This PDF file is the main place to go for all information related to running analyses, the data, and scripts, associated with this project. ## Code/software Download the respective datasets and use the *Project_Info.pdf* file to guide you through the analysis pipeline of this project. Note, in this study, all tabulated files (e.g., CSV or XLSX) have encoded missing data or no information with blank cells. ## Access information Pesticide registrations were mined from the APVMA (Australian Pesticide and Veterinary Medicines Authority) and EPA (Environmental Protection Agency) for Australian and USA pests, respectively, which are publicly available databases. 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*](https://doi.org/10.1111/eva.12579) and [Crossley et al. (2020) *Evolutionary Applications*](https://doi.org/10.1111/eva.13089), which is publicly available.