Data and scripts for "Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly"

The README explains how to reproduce the analyses presented in the paper "Joint species-trait distribution modelling: The role of intraspecific trait variation in community assembly" by Abrego et al. The input data for the script pipeline is the file “Kilpisjarvi_plant_data.csv”. This file includes the data on the plants and their traits in the long format. Hence, each row of the data matrix corresponds to measurements on one plant species in one study plot. The joint species-trait distribution modelling (JSTDM) pipeline that analyses these data consists of the following R-scripts. · S1_define_JSTDM_models.R. This script defines the JSDTM models (null model and environmental model) that include five response types for each species: the presence-absence, abundance conditional on presence, and the plot-level trait values of specific leaf area (SLA), leaf area (LA) and mean height (MH). The model is defined in the Hierarchical Modelling of Species Communities (HMSC) framework utilizing the R-package Hmsc. The models are saved in the file “unfitted_models.RData”. · S2_fit_models.R. This script loads the unfitted models and fits them using the posterior sampling methods implemented in the R-package Hmsc. The models are fitted with increasing thinning until thin=100, which value was used to generate the results of the paper. The fitted models are saved in the file "models_thin_100_samples_250_chains_4.Rdata". · S3_plot_Omega_matrices.R. This script loads the fitted models and plots the association matrices (Fig. 2 of the paper). The csv file containing the values used to construct Fig. 2 is also given (figure2Cdata.csv and figure2Ddata.csv). · S4_show_VP_Beta_Gamma.R. This script loads the fitted models and extracts information on the variance partitionings (VP; Figs. S3 and S4 of the paper), the relationships between response types and environmental predictors (beta; Fig. S2 of the paper), and the relationships between response types and species-level traits (gamma; Fig. S5 of the paper). The csv file containing the values used to construct Fig. S2 (figureS2Adata.csv and figureS2Bdata.csv), Fig. S3 (figureS3data.csv), Fig. S4 (figureS4data.csv) and Fig S5 (figureS5data.csv) are also given. · S5_conditional_cross_validation.R. This script performs 10-fold cross validation to the data to test the predictive power related to the modelled plant traits. The script performs both regular (unconditional) cross-validation where all data are masked for the test fold, and conditional cross-validation where only the trait data (but not the abundance data) are masked for the test fold. · S6_show_conditional_cross_validation_results.R. This script plots the results of cross-validation (Fig. 3 of the paper). The csv file containing the values used to construct Fig. 3 is also given (figure3Adata.csv and figure3Bdata.csv). · S7_scenario_predictions.R. This script performs the scenario simulations described and shown in Fig. 4 of the paper. The csv file containing the values used to construct Fig. 4 is also given (figure4Bdata.csv and figure4Cdata.csv).