Please use this identifier to cite or link to this item: http://hdl.handle.net/11667/243
Appears in Collections:University of Stirling Research Data
Title: Exploiting pathogen defence trade-offs to manage risks of crop pest evolution against biocontrol agents
Creator(s): Mangan, Rosie M
Tinsley, Matthew C
Ferrari, Ester
Polanczyk, Ricardo Antônio
Bussière, Luc F
Contact Email: mt18@stir.ac.uk
Keywords: Evolution
Parasite
Date Available: 28-Jan-2025
Citation: Mangan, RM; Tinsley, MC; Ferrari, E; Polanczyk, RA; Bussière, LF (2025): Exploiting pathogen defence trade-offs to manage risks of crop pest evolution against biocontrol agents. Version 1. University of Stirling, School of Natural Sciences. Dataset, Collection, Software. http://hdl.handle.net/11667/243
Publisher: University of Stirling, School of Natural Sciences
Dataset Description (Abstract): Pathogens often exert strong selection on host populations, yet considerable genetic variation for resistance persists. Environmental heterogeneity may cause fitness trade-offs that help prevent fixation of pathogen resistance alleles in wild host populations. Pathogens are extensively used in biocontrol, yet the risks of pest resistance evolution are frequently underappreciated, and the key drivers of fitness trade-offs for pathogen resistance remain unclear, both in natural and managed populations. We investigate whether pathogen identity or host diet has a stronger effect on the fitness of resistance alleles by quantifying genetic variation and covariation for pathogen resistance in an insect pest across distinct pathogen and plant diet combinations. We demonstrate substantial heritability, indicating considerable risks of biopesticide resistance. Contrary to conventional thinking in host-pathogen biology, we found no strong genetic trade-offs for surviving exposure to two different fungal pathogen species. However, changes in plant diet dramatically altered selection, revealing diet-mediated genetic trade-offs affecting pest survival. Our data suggest that trade-offs in traits not strictly related to pathogen defence could nevertheless maintain genetic variation in natural and agricultural landscapes.
Dataset Description (TOC): This submission contains (1) a csv file containing raw data: Quan_Gene_APRIL_12_2023_RM_LFB.csv (2) an R script file that will perform analysis: ManganetalGEIforrxiv.R (3) model objects derived from the script and data ManganBigModels.RData. These data were derived from experiments on larvae of the crop pest Helicoverpa armigera. The larvae were fed one of three leaf diets (Maize, Soybean, Tomato) and were exposed to one of three infection treatments (which simulated biopesticide exposure (Beauveria bassiana, Metarhizium anisopliae, Uninfected (oil) control). Larvae originated from different family lines with a know relatedness structure. This enables calculation of the genetic variance and covariances for survival in different diet-infection treatment groups.
Type: collection
dataset
software
Contract/Grant Title: Overcoming insecticide resistance using diverse fungal pathogens and variable agricultural landscapes
Enhancing Diversity to Overcome Resistance Evolution
Funder(s): BBSRC - Biotechnology and Biological Sciences Research Council
São Paulo Research Foundation (FAPESP), Brazil
Vetenskapsrådet, Sweden
Carl Trygger Foundation (20:63), Sweden
Contract/Grant Number: BB/R022674/1
BB/S018956/1
Worktribe Project ID: 351316
972970
URI: http://hdl.handle.net/11667/243
Rights: Rights covered by the standard CC-BY 4.0 licence: https://creativecommons.org/licenses/by/4.0/
Affiliation(s) of Dataset Creator(s): University of Stirling (BES)
Júlio de Mesquita Filho State University of São Paulo
The University of Gothenburg

Files in This Item:
File Description SizeFormat 
Quan_Gene_APRIL_12_2023_RM_LFB.csvRaw data file356.46 kBUnknownView/Open
ManganetalGEIforrxiv.RR script80.26 kBUnknownView/Open
ManganBigModels.RDataModel objects556.27 MBUnknownView/Open


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