DOI: 10.5061/dryad.p8cz8wb0h
title: Data from: Impacts of weather anomalies and climate on plant disease

description: Predicting effects of climate change on plant disease is critical for
 protecting ecosystems and food production. Here, we show how disease
 pressure responds to short-term weather, historical climate, and weather
 anomalies by compiling a global database (4339 plant–disease populations)
 of disease prevalence in both agricultural and wild plant systems. We
 hypothesized that weather and climate would play a larger role in disease
 in wild versus agricultural plant populations, which the results
 supported. In wild systems, disease prevalence peaked when temperature was
 2.7°C warmer than the historical average for the same time of year. We
 also found evidence of a negative interactive effect between weather
 anomalies and climate in wild systems, consistent with the idea that
 climate maladaptation can be an important driver of disease outbreaks.
 Temperature and precipitation had relatively little explanatory power in
 agricultural systems, though we observed a significant positive effect of
 current temperature. These results indicate that disease pressure in wild
 plants is sensitive to nonlinear effects of weather, weather anomalies,
 and their interaction with historical climate. In contrast, warmer
 temperatures drove risks for agricultural plant disease outbreaks within
 the temperature range examined regardless of historical climate,
 suggesting vulnerability to ongoing climate change.
description: # Impacts of weather anomalies and climate on plant disease
 [https://doi.org/10.5061/dryad.p8cz8wb0h](https://doi.org/10.5061/dryad.p8cz8wb0h) ## Description of the data and file structure Data was collected through a systematic literature review and by extracting relevant climatic data from available databases. ### Files and variables #### File: ELE\_-\_climate\_and\_plant\_disease\_code\_and\_data.zip **Description:** Files: * Kirk_et_al_ELE_README.docx * data folder       Contains two CSV files: Plant disease survey data from literature review, Climate and weather data associated with plant disease surveys * output folder Empty folder to store output from R code * Kirk_ELE_climate_and_plant_disease_code.R        R script to run analyses to run models, create tables, create figures, and create supplemental figures.  **Climate and weather data CSV details** -       obs is an observation column to link the data to the disease survey data -       bio01 represents the annual historic mean temperature -       bio12 represents the annual historic rainfall -       tavg represents the monthly historic temperature -       prec represents the monthly historic rainfall -       total_precipitation represents the contemporaneous precipitation -       temperature_2m represents the contemporaneous temperature -       start_date and end_date represent the study period that the data was captured for **Plant disease survey CSV details** -       Obs is an observation column to link the data to the disease survey data -       NUM_ID is a unique identifier for the study in the literature review -       Short reference is the first author and study year -       Natural_or_ag specifies what type of plant system (natural = “wild” in the manuscript) -       Location is the approximate named location in the study -       Parasite and host taxonomic data is specified to different levels, and as detailed as possible from each study -       Date information records how many months the study occurred for and in which month it began -       n is the number of plants surveyed in the population -       Infected is the number of those plants that were infected -       Incidence (referred to as “prevalence” in the manuscript) is Infected / n. ## Code/software System requirements: -       R (tested on version 4.3.2) Installation guide: [https://cran.r-project.org/doc/manuals/r-release/R-admin.html](https://cran.r-project.org/doc/manuals/r-release/R-admin.html) Typical install time &lt; 5 minutes. Instructions: -       R code is set-up to load in the two CSV files from the data folder, though user may need to change working directory. -       Expected output includes the figures and tables in the manuscript and summaries of the model(s). -       Expected run time &lt; 5 minutes.