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voluModel
contains several functions to generate
formatted maps of rasters and points automatically. This was to aid us
in cleanly visualizing large numbers of maps efficiently using
ggplot2
(Wickam, 2016) and lattice
(Sarkar,
2008) functionality. While there are quite a few features available in
these functions, they were designed with fairly specific use cases in
mind. You are welcome to gut the functions and rewrite them for your own
custom needs.
The data I will be using for the demos in this tutorial will be
loaded and analyzed first. The Luminous Hake occurrence points, which
you can see in the data
sampling tutorial, were downloaded via R (R Core Team, 2020) from
GBIF (Chamberlain et al., 2021; Chamberlain and Boettiger,
2017) and OBIS (Provoost and Bosch, 2019) via occCite
(Owens et al., 2021). The simple envelope model is based on two
environmental datasets from the World Ocean Atlas (Garcia et
al., 2019): temperature (Locarnini et al., 2018) and
apparent oxygen utilization (AOU; Garcia et al., 2019). For
more details on how to process environmental data, see the
raster data tutorial. For the workflow used to generate the niche
envelopes, refer to the
introduction vignette.
library(voluModel) # Because of course
library(ggplot2) # For fancy plotting
library(viridisLite) # For high-contrast plotting palettes
library(dplyr) # To filter data
library(terra) # Now being transitioned in
library(sf) # Now being transitioned in
# Load data
oxygenSmooth <- rast(system.file("extdata/oxygenSmooth.tif",
package='voluModel'))
occs <- read.csv(system.file("extdata/Steindachneria_argentea.csv",
package='voluModel'))
# Temperature
td <- tempdir()
unzip(system.file("extdata/woa18_decav_t00mn01_cropped.zip",
package = "voluModel"),
exdir = paste0(td, "/temperature"), junkpaths = T)
temperature <- vect(paste0(td, "/temperature/woa18_decav_t00mn01_cropped.shp"))
# Creating a SpatRaster vector
template <- centerPointRasterTemplate(temperature)
tempTerVal <- rasterize(x = temperature, y = template, field = names(temperature))
# Get names of depths
envtNames <- gsub("[d,M]", "", names(temperature))
envtNames[[1]] <- "0"
names(tempTerVal) <- envtNames
temperature <- tempTerVal
# Oxygen processing
names(oxygenSmooth) <- names(temperature)
# Clean points ----
occurrences <- occs %>% dplyr::select(decimalLongitude, decimalLatitude, depth) %>%
distinct() %>% filter(dplyr::between(depth, 1, 2000))
# Gets the layer index for each occurrence by matching to depth
layerNames <- as.numeric(names(temperature))
occurrences$index <- unlist(lapply(occurrences$depth,
FUN = function(x) which.min(abs(layerNames - x))))
indices <- unique(occurrences$index)
downsampledOccs <- data.frame()
for(i in indices){
tempPoints <- occurrences[occurrences$index==i,]
tempPoints <- downsample(tempPoints, temperature[[1]], verbose = FALSE)
tempPoints$depth <- rep(layerNames[[i]], times = nrow(tempPoints))
downsampledOccs <- rbind(downsampledOccs, tempPoints)
}
occsWdata <- downsampledOccs[,c("decimalLatitude", "decimalLongitude", "depth")]
# Extract data ----
occsWdata$temperature <- xyzSample(occs = occsWdata, temperature)
## Using decimalLongitude, decimalLatitude, and depth
## as x, y, and z coordinates, respectively.
## Using decimalLongitude, decimalLatitude, and depth
## as x, y, and z coordinates, respectively.
occsWdata <- occsWdata[complete.cases(occsWdata),]
# Land shapefile
land <- rnaturalearth::ne_countries(scale = "small", returnclass = "sf")[1]
# Study region
studyRegion <- marineBackground(occsWdata, buff = 1000000)
## Using decimalLongitude and decimalLatitude
## as x and y coordinates, respectively.
# Get limits
tempLims <- quantile(occsWdata$temperature,c(0, 1))
aouLims <- quantile(occsWdata$AOU,c(0, 1))
# Reclassify environmental bricks to presence/absence
temperaturePresence <- classify(temperature,
rcl = matrix(c(-Inf,tempLims[[1]],0,
tempLims[[1]], tempLims[[2]], 1,
tempLims[[2]], Inf, 0),
ncol = 3, byrow = TRUE))
AOUpresence <- classify(oxygenSmooth,
rcl = matrix(c(-Inf, aouLims[[1]],0,
aouLims[[1]], aouLims[[2]], 1,
aouLims[[2]], Inf, 0),
ncol = 3, byrow = TRUE))
# Put it all together
envelopeModel3D <- temperaturePresence * AOUpresence
envelopeModel3D <- mask(crop(envelopeModel3D, studyRegion),
mask = studyRegion)
names(envelopeModel3D) <- names(temperature)
rm(AOUpresence, downsampledOccs, occurrences, temperaturePresence,
tempPoints, aouLims, envtNames, i, indices, layerNames, tempLims)
There are two functions in voluModel
for plotting
occurrence points. The first, pointMap()
, plots a single
set of horizontal geographic points. The title generated is the species
name the user supplies and a count of the number of points that have
been plotted.
pointMap(occs = occs, land = land, landCol = "black", spName = "Steindachneria argentea",
ptSize = 2, ptCol = "orange")
The second, pointCompMap()
plots a map that maps the
extents of two sets of horizontal geographic points in different colors.
This might be useful, for example, if you want to compare a raw dataset
to a cleaned dataset. If any points overlap, they can be plotted as a
third color.
pointCompMap(occs1 = occs, occs1Col = "red", occs1Name = "Raw",
occs2 = occsWdata, occs2Col = "orange", occs2Name = "Clean",
spName = "Steindachneria argentea", agreeCol = "purple",
land = land, landCol = "black", ptSize = 2, verbose = FALSE)
If you have a horizontal raster you would like to map that contains
continuous data, the function for you might be
oneRasterPlot()
. It is a wrapper around
spplot()
from sp
(Pebesma and Bivand, 2005;
Bivand et al. 2013) that implements the high-contrast,
colorblind-friendly color palettes offered in viridis()
from viridisLite
(Garnier et al., 2021). The
function optionally takes arguments to the spplot()
and
viridis()
functions (so, for example, you can choose a
palette other than the default).
oneRasterPlot(rast = temperature[[1]],
land = land, title = "Sea Surface Temperature, WOA 2018",
landCol = "black", n = 11, option = "mako",
varName = "Temperature")
Suppose you want to compare the extents of two presence-absence rasters–perhaps you are comparing two species distribution model method outputs, or comparing potential distributions at two different depths. We have a function for that, too.
rasterComp(rast1 = envelopeModel3D[[1]], rast1Name = "Surface",
rast2 = envelopeModel3D[[10]], rast2Name = "45m",
land = land, landCol = "black",
title = "Suitability of Habitat for Luminous Hake\nAt Two Different Depths")
Of course, 3D models such as the ones voluModel
is
designed to help you produce, are difficult to visualize. We have given
it a try, though. plotLayers()
plots a transparent layer of
suitable habitat for each depth layer. The redder the color, the
shallower the layer, the bluer, the deeper. The more saturated the
color, the more layers with suitable habitat. Here, I am plotting
suitability from 20 to 700 m, the depth range of occurrences used to
train the envelope model.
layerNames <- as.numeric(names(envelopeModel3D))
occsWdata$index <- unlist(lapply(occsWdata$depth, FUN = function(x) which.min(abs(layerNames - x))))
indices <- unique(occsWdata$index)
layerPlot <- plotLayers(envelopeModel3D[[min(indices):max(indices)]],
title = "Envelope Model of Luminous Hake,\n 20 to 700m",
land = land, landCol = "black")
Last, we need to close the temporary directory we opened when we opened the data.
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They may not be fully stable and should be used with caution. We make no claims about them.
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