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An R package to improve the usability of datasets with predictors for species distribution modelling (SDM).
Installation:
install.packages("sdmpredictors")
# or for the latest dev version
devtools::install_github("lifewatch/sdmpredictors")Example 1: Create SDM for Dictyota diemensis in Australia Note that this requires the ZOON, ggplot2, cowplot and marinespeed packages to be installed.
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))Example 2: view marine datasets, layers and load a few of them by name
library(sdmpredictors)
# exploring the marine datasets
datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
View(datasets)
browseURL(datasets$url[1])
# exploring the layers
layers <- list_layers(datasets)
View(layers)
# download specific layers to the current directory
rasters <- load_layers(c("BO_calcite", "BO_chlomean", "MS_bathy_5m"), datadir = ".")Example 3: looking up statistics and correlations for marine annual layers:
datasets <- list_datasets(terrestrial = FALSE, marine = TRUE)
layers <- list_layers(datasets)
# filter out monthly layers
layers <- layers[is.na(layers$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)
}
}See the quickstart vignette for more information
vignette("quickstart", package = "sdmpredictors")These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.