DOI: 10.5061/dryad.cz8w9gjbm
title: Mean plant toxicity modulates the effects of plant defense variability

description: Plant trait variation is thought to suppress herbivore performance, but
 experiments are usually done with a single mean level of the trait. We
 manipulated the mean and variation of glucosinolate toxin concentration on
 Arabidopsis thaliana leaves and fed them to Trichoplusia ni in three field
 and greenhouse experiments. Plants painted with a greater mean
 glucosinolate concentration had higher fitness and resistance to
 herbivores; however, at high mean concentrations, variation reduced the
 defensive effect, while at lower mean concentrations, variation enhanced
 it. This reversal is consistent with models that include herbivore food
 selectivity, but our simulations revealed that the benefit of food
 selectivity to herbivores was minuscule. Instead, nonlinear averaging and
 physiological tracking effects likely drove patterns in plant fitness and
 resistance to herbivores. We suggest that high defense variation in plants
 may be a widespread convergent defensive phenotype, but for well defended
 plants, variation may inadvertently promote herbivore niche expansion.
description: # Mean plant toxicity modulates the effects of plant defense variability
 Vincent S. Pan, Kadeem J. Gilbert, William C. Wetzel
 [https://doi.org/10.5061/dryad.cz8w9gjbm](https://doi.org/10.5061/dryad.cz8w9gjbm) **02-Dec-2024 update: VSP made a mistake in inputting the name of the last author 'William Wetzel' as 'Kadeem Wetzel'. The mistake has been corrected.** ## Description of the data and file structure #### Data Files: * field_variability_experiment.csv * Field data on *Arabidopsis thaliana* fitness (experiment 1). Each row is a unique plant * greenhouse_variability_data.csv * Greenhouse data on *Trichoplusia ni* performance (experiment 2). Each row is a unique caterpillar. * dose_response_data.csv * Greenhouse data on *Trichoplusia ni* dose response curves (experiment 3). Each row is a unique caterpillar #### Code Files: * eff_part_functions_bayes.R * Custom functions for performing counterfactual simulations that partition the effects of variability * figures.R * Code for generating manuscript figures, working along side the `main_analyses_bayes.Rmd` script. * main_analyses_bayes.Rmd * Code for performing the analyses reported in the manuscript * main_analyses_bayes.html * Knitted version of the above .Rmd file #### Column definitions: * cage_ID: unique identifier for individiual cage * plant_ID: unique identifier for individual plant * cage_trt: the assigned cage level treatment (combining plant_mean_trt and plant_var_trt) * plot_mean_trt: the mean level of glucosinolate painted on leaves in the cage (h: high, l: low) * plot_var_trt: the variation treatment of the cage (const: constant, intra: intraindividual variation, inter: interindividual variation) * plant_trt: the dose level of the plant (mean: the cage mean, low: low dose of the cage, high: high dose of the cage, both: both high and low doses of the cage) * recruitment: the number of F1 A. thaliana plants counted in the same pot * block_ID: the block identifier * cat_ID: unique caterpillar identifier * cat_pre_wt: the pre treatment caterpillar mass in mg * plant_ID: unique plant identifier * cage_ID: the assigned cage level treatment (combining mean_trt and var_trt) * mean_trt: the mean cage dose level treatment (h: high, l: low) * var_trt: the variation treatment (intra: intraindivdiual variation, const: constant) * cat_ID: unique caterpillar identifier * tray_ID: unique tray (holding the 3X3 pots together) identifier * session: unique identifier for experimental session * container_ID unique identifier for where the caterpillar was sourced * cat_pre_wt: caterpillar pre experimental weight in mg * cat_post_wt: caterpillar post experimental weight in mg * leaf[1-11]: Proportion area removed on leaf n counting clockwise from a piece of wire marking the first high dose leaf * prop_herb: sum(leaf[1-11]) / leaf_num_pre (i.e. proportion total leaf area removed) * leaf_num_pre: number of leaves on the plant pre experiment * days_to_pupation: number of days the caterpillar took to pupate, counting from end of experiment to day of pupation * missing: whether the caterpillar went missing during the experiment (1: true, 0: false) * high_leaf_dose: nmol/mg of exogenuous glucosinolate added in the high treatment * low_leaf_dose: nmol/mg of exogenuous glucosinolate added in the low treatment * leaf_num_pre_log_scale: scale(log(leaf_num_pre)) * cat_pre_wt_log_Scale: scale(log(cat_pre_wt)) * cat_post_wt_log: log(cat_post_wt) * cat_retrieve_date: when the caterpillar was retrieved from the cage, ending the experiment (mm-dd-YYYY) * cat_pupation_date: when the caterpillar pupated (mm-dd-YYYY) * trt_painted_date: when the glucosinolate was painted (mm-dd-YY) * cat_deploy_date: when the caterpillar was put in the cage, starting the experiment (mm-dd-YYYY) * cat_death_date: when the caterpillar died (mm-dd-YYYY) * dead_when_retreived: did the caterpillar die before the end of the experiment? (1: true, 0: false) * plant_ID: unique plant identifier * cat_ID: unique caterpillar identifier * tray_ID: unique tray (holding the 3X3 pots together) identifier * container_ID unique identifier for where the caterpillar was sourced * cat_pre_wt: caterpillar pre experimental weight in mg * cat_post_wt: caterpillar post experimental weight in mg * dose: the dose applied to the plant in nmol/mg * dose_scale: scale(dose) * leaf[1-7]: Proportion area removed on leaf n counting clockwise from a piece of wire marking the first high dose leaf * prop_herb: sum(leaf[1-7]) / leaf_num_pre (i.e. proportion total leaf area removed) * leaf_num_pre: number of leaves on the plant pre experiment * days_to_pupation: number of days the caterpillar took to pupate, counting from end of experiment to day of pupation * leaf_num_pre_log_scale: scale(log(leaf_num_pre)) * cat_pre_wt_log_Scale: scale(log(cat_pre_wt)) * cat_retrieve_date: when the caterpillar was retrieved from the cage, ending the experiment (mm-dd-YYYY) * cat_pupation_date: when the caterpillar pupated (mm-dd-YYYY) * trt_painted_date: when the glucosinolate was painted (mm-dd-YY) * cat_deploy_date: when the caterpillar was put in the cage, starting the experiment (mm-dd-YYYY) * cat_death_date: when the caterpillar died (mm-dd-YYYY) * dead_when_retreived: did the caterpillar die before the end of the experiment? (1: true, 0: false) NA: Missing data. ## Sharing/Access information Use the data and code as you please.