Upscaling your data
funbiogeo provides an
easy way to upscale your site data to a coarser resolution. The idea is
that you have any type of data at the site level (diversity metrics,
environmental data, as well as site-species data) that you would like to
work or visualize at a coarser scale. The aggregation process can look
daunting at first and be quite difficult to run. We explain in this
vignette, how to do so through the fb_aggregate_site_data()
function. We’ll detail three use cases: the first through aggregating
arbitrary site-level data, the second focused on aggregating the site x
species object, and the last aggregating functional diversity
metrics.
Aggregating arbitrary site-data
Let’s import our site by locations object, which describes the geographical locations of sites.
data("woodiv_locations")
woodiv_locations
#> Simple feature collection with 5366 features and 2 fields
#> Geometry type: POLYGON
#> Dimension: XY
#> Bounding box: xmin: 2630000 ymin: 1380000 xmax: 5040000 ymax: 2550000
#> Projected CRS: ETRS89-extended / LAEA Europe
#> First 10 features:
#> site country geometry
#> 1 26351755 Portugal POLYGON ((2630000 1750000, ...
#> 2 26351765 Portugal POLYGON ((2630000 1760000, ...
#> 4 26351955 Portugal POLYGON ((2630000 1950000, ...
#> 5 26351965 Portugal POLYGON ((2630000 1960000, ...
#> 6 26451755 Portugal POLYGON ((2640000 1750000, ...
#> 7 26451765 Portugal POLYGON ((2640000 1760000, ...
#> 8 26451775 Portugal POLYGON ((2640000 1770000, ...
#> 10 26451955 Portugal POLYGON ((2640000 1950000, ...
#> 11 26451965 Portugal POLYGON ((2640000 1960000, ...
#> 12 26451975 Portugal POLYGON ((2640000 1970000, ...These sites are a collection of regular spatial polygons at a resolution of 10 x 10 km over Western Europe.
For each site, we want to compute the species richness.
# Import site x species data ----
data("woodiv_site_species")
# Compute species richness ----
species_richness <- fb_count_species_by_site(woodiv_site_species)
head(species_richness)
#> site n_species coverage
#> 1 40552295 12 0.5000000
#> 2 39252405 12 0.5000000
#> 3 37752305 12 0.5000000
#> 4 39852255 12 0.5000000
#> 5 39152415 12 0.5000000
#> 6 39552415 11 0.4583333Before going any further let’s map these original values with the
function fb_map_site_data().
Now, let’s say that our next analyses require to work at a coarser
resolution. We need to aggregate site data on a new spatial grid (object
SpatRaster from the terra
package). Let’s import this coarser raster.
# Import study area grid ----
coarser_grid <- system.file("extdata", "grid_area.tif", package = "funbiogeo")
coarser_grid <- terra::rast(coarser_grid)
coarser_grid
#> class : SpatRaster
#> dimensions : 29, 41, 1 (nrow, ncol, nlyr)
#> resolution : 0.8333333, 0.8333333 (x, y)
#> extent : -10.5, 23.66667, 35.83333, 60 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source : grid_area.tif
#> name : value
#> min value : 1
#> max value : 1We will aggregate the site data (resolution of 0.5°) to this new
coarser raster (resolution of 0.83°) with the function
fb_aggregate_site_data(). This function requires the
following arguments:
site_locations: the site x locations objectsite_data: amatrixordata.framecontaining values per sites to aggregate on the provided gridagg_grid. Can have one or several columns (variables to aggregate). The first column must contain sites names as provided in the example datasetsite_locationsagg_grid: aSpatRasterobject (packageterra). A raster of one single layer, that defines the grid along which to aggregatefun: the function used to aggregate sites values when there are multiple sites in one cell
Let’s aggregate our species richness values on this grid.
# Upscale to grid ----
upscaled_richness <- fb_aggregate_site_data(
site_locations = woodiv_locations[ , 1, drop = FALSE],
site_data = species_richness[ , 1:2],
agg_grid = coarser_grid,
fun = mean
)
upscaled_richness
#> class : SpatRaster
#> dimensions : 29, 41, 1 (nrow, ncol, nlyr)
#> resolution : 0.8333333, 0.8333333 (x, y)
#> extent : -10.5, 23.66667, 35.83333, 60 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> varname : grid_area
#> name : n_species
#> min value : 1
#> max value : 10The result of this function is a SpatRaster where the
cells contain the average values of species richness aggregated over the
coarser grid.
Aggregating site-species data to a coarser spatial scale
Now that we’ve learned how to aggregate arbitrary data at the site
scale over a spatial scale. We’re going to use our provided example
named site_species at a resolution of 10 x 10 km to get new
sites from a grid with pixels of 0.83° of resolution.
As shown in the part above, we’ll need three objects:
site_species, which describes the species present across
sites; site_locations, which gives the spatial locations of
sites; and agg_grid which is a SpatRaster
object defining the coarser grid.
We’ll use the previously defined object to run our example. To
aggregate the presence-absence of species within each pixel of the new
grid, we’ll use the max() function (as the fun
argument). As such, coarser pixels which contains a mix of presence and
absence of certain species, we’ll be considered as having the species
present.
site_species_agg <- fb_aggregate_site_data(
woodiv_locations[ , 1, drop = FALSE],
woodiv_site_species,
agg_grid = coarser_grid,
fun = max
)The return object is a SpatRaster as well but can be
transformed easily in a data.frame to follow back with the regular
analyses provided in funbiogeo. The new object contains one
layer for each aggregated variable, i.e. here, one per species.
site_species_agg
#> class : SpatRaster
#> dimensions : 29, 41, 24 (nrow, ncol, nlyr)
#> resolution : 0.8333333, 0.8333333 (x, y)
#> extent : -10.5, 23.66667, 35.83333, 60 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> varnames : grid_area
#> grid_area
#> grid_area
#> ...
#> names : AALB, ACEP, APIN, CLIB, CSEM, JCOM, ...
#> min values : 0, 0, 0, 0, 0, 0, ...
#> max values : 1, 1, 1, 0, 1, 1, ...We can visualize both maps for a single species to see the difference:
library("ggplot2")
single_species <- merge(
woodiv_locations, woodiv_site_species[, 1:2], by = "site", all = TRUE
)
finer_map <- ggplot(single_species) +
geom_sf(aes(fill = as.factor(AALB))) +
labs(fill = "Presence of AALB", title = "Original resolution (10 x 10 km)")
coarser_map <- fb_map_raster(site_species_agg[[1]]) +
scale_fill_binned(breaks = c(0, 0.5, 1)) +
labs(title = "Coarser resolution (0.83°)")
patchwork::wrap_plots(finer_map, coarser_map, nrow = 1)
Obtaining back a site x species data.frame
Now we obtained a raster of aggregated site-species presences.
However, the other functions of funbiogeo don’t play well
with raster data. They need data.frames to work well. We can do this
through the specific function as.data.frame() in
terra (make sure to check the dedicated help page that
specifies all the additional arguments with
?terra::as.data.frame).
# Use the 'cells = TRUE' argument to index results with a new cell column
# corresponding to the ID of the coarser grid pixels
site_species_agg_df <- terra::as.data.frame(site_species_agg, cells = TRUE)
site_species_agg_df[1:4, 1:4]
#> cell AALB ACEP APIN
#> 755 755 0 0 0
#> 757 757 0 0 1
#> 758 758 1 0 0
#> 759 759 1 0 0
colnames(site_species_agg_df)[1] <- "site"With this, we’re ready to reuse all of funbiogeo
functions to work on these coarser data. You can proceed similarly to
aggregate the ancillary site-related data, to use them in the rest of
the analyses.
Aggregating site-functional diversity data
Because funbiogeo focuses on the functional biogeography
workflow, we’ll explore in this section how to aggregate the results for
a functional biogeography function. First, we’ll show the example with
the CWM of body mass then we’ll show an example with the functional
diversity using the fundiversity package.
Coarser CWM of body mass
To compute the CWM we’ll use the internal function
fb_cwm().
site_cwm <- fb_cwm(woodiv_site_species, woodiv_traits[, 1:2])
head(site_cwm)
#> site trait cwm
#> 1 26351755 plant_height 12.31767
#> 2 26351765 plant_height 4.88150
#> 3 26351955 plant_height 12.31767
#> 4 26351965 plant_height 13.77575
#> 5 26451755 plant_height 4.88150
#> 6 26451765 plant_height 15.76845Now we can aggregate the CWM of body mass at coarser scale using
fb_aggregate_site_data() as done in the previous
section:
colnames(site_cwm)[3] <- "plant_height"
upscaled_cwm <- fb_aggregate_site_data(
woodiv_locations[ , 1, drop = FALSE],
site_cwm[, c(1, 3)],
coarser_grid
)
upscaled_cwm
#> class : SpatRaster
#> dimensions : 29, 41, 1 (nrow, ncol, nlyr)
#> resolution : 0.8333333, 0.8333333 (x, y)
#> extent : -10.5, 23.66667, 35.83333, 60 (xmin, xmax, ymin, ymax)
#> coord. ref. : lon/lat WGS 84 (EPSG:4326)
#> source(s) : memory
#> varname : grid_area
#> name : plant_height
#> min value : 4.88150
#> max value : 42.14307We can then map the CWM using the fb_map_raster()
function:
Coarser FRic through fundiversity
In a similar fashion as in the introduction
vignette to funbiogeo in this section we’ll compute the
Functional Richness using two traits across our example dataset.
# Get all species for which we have both adult body mass and litter size
subset_traits <- woodiv_traits[
, c("species", "plant_height", "seed_mass")
]
subset_traits <- subset(
subset_traits, !is.na(plant_height) & !is.na(seed_mass)
)
# Transform trait data
subset_traits[["plant_height"]] <- as.numeric(
scale(log10(subset_traits[["plant_height"]]))
)
subset_traits[["seed_mass"]] <- as.numeric(
scale(subset_traits[["seed_mass"]])
)
# Filter site for which we have trait information for than 80% of species
subset_site <- fb_filter_sites_by_trait_coverage(
woodiv_site_species, subset_traits, 0.8
)
subset_site <- subset_site[, c("site", subset_traits$species)]
# Remove first column and convert in rownames
rownames(subset_traits) <- subset_traits[["species"]]
subset_traits <- subset_traits[, -1]
rownames(subset_site) <- subset_site[["site"]]
subset_site <- subset_site[, -1]
# Compute FRic
site_fric <- fundiversity::fd_fric(
subset_traits, subset_site
)
#> Warning: Some sites had less species than traits so returned FRic is 'NA'
head(site_fric)
#> site FRic
#> 1 41152325 1.0964687
#> 2 40852325 1.1380385
#> 3 40852345 1.1523280
#> 4 42552105 0.8201878
#> 5 41152315 1.1380385
#> 6 37452365 NAWe can now follow a similar upscaling process as in the previous sections
agg_fric <- fb_aggregate_site_data(woodiv_locations[ , 1, drop = FALSE],
site_fric,
coarser_grid)
fb_map_raster(agg_fric)
#> Warning: Raster pixels are placed at uneven horizontal intervals and will be shifted
#> ℹ Consider using `geom_tile()` instead.