DOI: 10.6084/m9.figshare.27948915
title: <b>Plant functional trait maps for Australia</b>

description: <b>Plant functional trait maps for Australia</b>These data are associated with a manuscript by Samuel C. Andrew, Irene Martín-Forés, Greg R. Guerin, David Coleman, Daniel S. Falster, Elizabeth Wenk, Ian J. Wright, & Rachael V. Gallagher (<i>Mapping plant functional traits using gap-filled datasets to inform management and modelling, in review). </i>All included files can be used to run the Rmarkdown file "Biogeography_Rcode_241010.Rmd " for the main steps of data prep and analyses. Data objects are uploaded in the zip folder “Data_objects_1_to_9.zip”<b>Inputs</b>“Supplementary_methods.zip”: Data and scripts for using AusTraits data to calculate species mean values. The code for calculating mean species values with the method described in the paper is in the “1_build_Austraits_2.R” R code files. Please contact David Coleman (dave.r.coleman@gmail.com) for further information."1_gap_filled_traits_all_natives_species.csv": This file contains the species level trait data for native Australian species. Gap filling estimates were done by grouping varieties and subspecies to the species taxonomic level. The columns with the “_var” suffix have the variance for each estimated values from the trait gap filling workflow. Species with a value in the "_var" columns had estimated values for that trait and can have the trait values converted to NA for a ungap filled dataset (see output 7)."2_Grid_cell_cliamte_data.csv": Includes a list of equal area grid cells (10 x 10 km) for Australia. The latitude and longitude coordinates are given in the second and third columns. Equal areas coordinates are given in the fourth and fifth columns ("x", "y"). The remaining columns have climate data; "AnnMeanTemp" - Mean Annual Temperature (MAT), "AnnPrecip" – Mean Annual Precipitation (MAP), "maxTemp" – Average maximum temperature of the warmest month, "minTemp" - Average minimum temperature of the coldest month."3_species_cell_id_200306.rds": A R .rds file (loaded with the readRDS() function) that includes the stacked species distribution data. The species expected to occur in each grid cell are listed. The plant herbarium occurrence data from the Atlas of Living Australia (ALA) were used for species distribution modelling, see description in Andrew et al., (2021. <i>Journal of Vegetation Science</i>, <i>32</i>(2), e13018. https://doi.org/10.1111/jvs.13018). The ALA occurrence data were downloaded in December 2019 (see https://doi.org/10.6084/m9.figshare.24503893.v2 for data) but a fresh download of ALA occurrence data is probably recommended for future studies."4_Austraits_taxa_data_230627.csv": taxonomic data from AusTraits that was used to update species names in distribution data ("3_species_cell_id_200306.rds") to binomial names."5_Unweighted_ausplots_Trait_data.csv": Estimates of trait means and variance for AusPlots species inventories.“6_SDM_press_base_raster_10km.tif”: Raster mask of Australia used to plot grid cell values for maps. Projection "Lambert Azimuthal Equal Area", crs = "+proj=laea +lat_0=-25.2744 +lon_0=133.7751 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".<b>Outputs</b>"7_EA_SDM_10km_GC_Trait_layers.rds": This file can be loaded in R with the load() function and contains the outputs of the first chunk of Rmarkdown code in the “Biogeography_Rcode_241010.Rmd” Rmarkdown file. Includes a data frame for species level trait data (“Trait_data”) and a data frame for grid cell climate and community trait summaries (Aus_grid_cell_data). For the grid cell data each of the four traits have mean (“mean_” prefix), standard deviation(“SD_” prefix), variability (“var_” prefix), maximum (“max_” prefix), minimum (“min_” prefix), number of species with trait data (“species_” prefix), and the percentage of species with trait data ( “_PerCent” suffix). For estimates with gap filled trait data the column names have the “_gap” suffix."8_EA_SDM_10km_GC_models_240422.rds": GAM model outputs used to make plotting figures and variance partitioning analyses faster to run."9_Bootstrap_data_240422.rds": Data and outputs from running the models with 100 random subsets of 10% of the grid cells.<b>Results</b>“Biogeography_Rcode_241010.Rmd”: Rmarkdown file with scripts to use inputs and outputs 1 to 9 and the csv files from “Results_tables.zip”. The script should run as is with the data objects and csv files in the same directory as the Rmarkdown file.“Results_tables.zip”: includes the csv files for Tables 1, 2 and S1 ("Table_1_modes_240422.csv", "Table_2_partitioning_240422.csv", “Table_S1_plot_models_240422.csv”). Used to report model results neatly in the Knit file.“Biogeography_Knit_241010.html”: The output from the Rmarkdown file that shows the figures and tables.“Trait_Maps.zip”: zipped folder of main trait maps as raster layers. Projection "Lambert Azimuthal Equal Area", crs = "+proj=laea +lat_0=-25.2744 +lon_0=133.7751 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs".