sdmpredictors: a compilation of species distribution modelling predictors data

  install.packages("sdmpredictors")
 # or for the latest dev version
 devtools::install_github("lifewatch/sdmpredictors")

 library(sdmpredictors)
 library(zoon)
 
 # Inspect the available datasets and layers
 datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
 View(datasets)
 layers <- list_layers(datasets)
 View(layers)
 # Load equal area rasters and crop with the extent of the Baltic Sea
 layercodes <- c("MS_biogeo05_dist_shore_5m", "MS_bathy_5m", 
 "BO_sstrange", "BO_sstmean", "BO_salinity")
 env <- load_layers(layercodes, equalarea = TRUE)
 australia <- raster::crop(env, extent(106e5,154e5, -52e5, -13e5))
 plot(australia)
 # Compare statistics between the original and the Australian bathymetry
 View(rbind(layer_stats("MS_bathy_5m"),
 calculate_statistics("Bathymetry Australia", 
 raster(australia, layer = 2))))
 # Compare correlations between predictors, globally and for Australia
 prettynames <- list(BO_salinity="Salinity", BO_sstmean="SST (mean)", 
 BO_sstrange="SST (range)", MS_bathy_5m="Bathymetry",
 MS_biogeo05_dist_shore_5m = "Shore distance")
 p1 <- plot_corr(layers_correlation(layercodes), prettynames)
 australian_correlations <- pearson_correlation_matrix(australia)
 p2 <- plot_correlation(australian_correlations, prettynames)
 cowplot::plot_grid(p1, p2, labels=c("A", "B"), ncol = 2, nrow = 1)
 print(correlation_groups(australian_correlations))
 # Fetch occurrences and prepare for ZOON
 occ <- marinespeed::get_occurrences("Dictyota diemensis")
 points <- SpatialPoints(occ[,c("longitude", "latitude")],
 lonlatproj)
 points <- spTransform(points, equalareaproj)
 occfile <- tempfile(fileext = ".csv")
 write.csv(cbind(coordinates(points), value=1), occfile)
 # Create SDM with ZOON
 workflow(
 occurrence = LocalOccurrenceData(
 occfile, occurrenceType="presence",
 columns = c("longitude", "latitude", "value")), 
 covariate = LocalRaster(stack(australia)),
 process = OneHundredBackground(seed = 42),
 model = LogisticRegression,
 output = PrintMap)
 # Layer citations
 print(layer_citations(layercodes))

 library(sdmpredictors)
 
 # exploring the marine datasets
 datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
 View(datasets)
 browseURL(datasets$url[1])
 
 # exploring the layers
 layers <- list_layers(datasets)
 View(layers)
 
 # download specific layers to the current directory
 rasters <- load_layers(c("BO_calcite", "BO_chlomean", "MS_bathy_5m"), datadir = ".")

 datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
 layers <- list_layers(datasets)
 
 # filter out monthly layers
 layers <- layers[is.na(layers$month),]
 
 stats <- layer_stats(layers)
 View(stats)
 
 correlations <- layers_correlation(layers)
 View(correlations)
 
 # create groups of layers where no layers in one group 
 # have a correlation > 0.7 with a layer from another group
 groups <- correlation_groups(correlations, max_correlation=0.7)
 
 # inspect groups
 # heatmap plot for larger groups (if gplots library is installed)
 for(group in groups) {
 group_correlation <- as.matrix(correlations[group, group, drop=FALSE])
 if(require(gplots) && length(group) > 4){
 heatmap.2(abs(group_correlation)
 ,main = "Correlation"
 ,col = "rainbow" 
 ,notecol="black" # change font color of cell labels to black
 ,density.info="none" # turns off density plot inside color legend
 ,trace="none" # turns off trace lines inside the heat map
 ,margins = c(12,9) # widens margins around plot
 )
 } else {
 print(group_correlation)
 }
 }

 vignette("quickstart", package = "sdmpredictors")