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Data and analysis for "Plant defense synergies and antagonisms affect performance of specialist herbivores of common milkweed"

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Version 2 2022-10-06, 17:45
Version 1 2022-10-05, 17:17
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posted on 2022-10-06, 17:45 authored by Collin EdwardsCollin Edwards, Stephen EllnerStephen Ellner, Anurag Agrawal

Directory for data and analysis of "Plant defense synergies and antagonisms affect performance of specialist herbivores of common milkweed." Here we analyze insect performance on plants to identify bilinear and non-bilinear synergies and antagonisms using regression tools and a novel (for ecology) application of Random Forests adapted from Hooker (2007).


See the associated publication for an overview of the data and methods. Here we provide data on common milkweed defense traits and the performance of two herbivores (monarch butterfly larvae and swamp milkweed beetle larvae) on those plants. The code and results used to carry out analysis and plotting for the associated publication, as well as a detailed tutorial for implementing the novel statistical approach we present in our publication.


Scripts were last run in R version 4.0.4, and the main analysis is in an Rmarkdown files (.Rmd). For those unfamiliar with the file format, it allows the combination of R code with document formatting using the Markdown language; there are considerable resources available online to introduce you to the use of Rmarkdown. Rmarkdown files are most easily viewed and edited in the free IDE Rstudio. Note that all code here is intended for use in an "R Project", an organizational tool associated with Rstudio; the .Rproj file in the main directory enables this. This allows the code to run out of the box on any computer, regardless of where the main directory is placed on your local hard drive.


See README.txt for more details and file structure.


Update: During final revisions, we have tweaked the figure-generation code slightly, including writing a script to generate the new Figure 1. Current upload incorporates these changes.

Funding

NSF-IOS 1907491

NSF-IOS 2209762

Cornell Mellon grant

NSF GRFP 14590

History