Data and code from "Warming temperatures and shifting precipitation patterns may exacerbate pest damage in North American forests"

Here, we provide:(i) the list of 30 focal pest species used in our study (“focal_pest_species_30.csv”), which includes columns for the common name, scientific name, Damage Causing Agent (DCA) code and type, a simplified type category, nativity, and regional distribution;(ii) annotated R code (“run_linear_mixed_effects_models.R”) to analyze climate effects on forest pest damage using linear mixed effects models;(iii) RDS files of the processed pest damage and environmental data used as inputs for the R code (see “data” folder);(iv) RDS files of the linear mixed effects models produced by the R code (see “models” folder);(v) information about the top models ("list_of_top_models"), including AIC, temporal window, and sample sizes; and(vi) the full set of results from the linear mixed effects models for all models (“model_slope_coefficients_ALL_MODELS_FINAL_RESULTS.csv”) and the top models ("model_slope_coefficients_TOP_MODELS_RESULTS.csv") which comprises the mean value (“COEF”), lower confidence interval (“LCI”), upper confidence interval (“UCI”), significance, direction, and magnitude of the slope coefficients associated with each response variable (organized by variable group) in each model (organized by focal pest species, damage type, and response type).(vii) data and code to run the metanalysis on slope coefficients.In the CSV of final results, the climate variables are named as follows: “CA” = climate anomaly, “CW” = climate window, “2” = mean monthly temperature range, “5” = maximum temperature of the warmest month, “6” = minimum temperature of the coldest month, “13” = precipitation of the wettest month, and “14” = precipitation of the driest month. Note that a response type of “estimated-damage” is equivalent to the “refined damage” metric in our manuscript.We also include an alternative Fig. 3 from the manuscript, with data points on the marginal plots.