1.1. Shapefile
Let’s assume we are interested in having a floristic knowledge of the
western part of the Mediterranean basin. For this purpose, we can simply
use a shape file of the region of interest and feed it to the
GIFT_checklists() function.
We do provide a shape file of this region in the GIFT R
package, which you can access using the
data("western_mediterranean") command.
data("western_mediterranean")
world <- ne_coastline(scale = "medium", returnclass = "sf")
world_countries <- ne_countries(scale = "medium", returnclass = "sf")
# Fixing polygons crossing dateline
world <- st_wrap_dateline(world)
world_countries <- st_wrap_dateline(world_countries)
# Eckert IV projection
eckertIV <-
"+proj=eck4 +lon_0=0 +x_0=0 +y_0=0 +ellps=WGS84 +datum=WGS84 +units=m +no_defs"
ggplot(world) +
geom_sf(color = "gray50") +
geom_sf(data = western_mediterranean, fill = "darkblue", color = "black",
alpha = 0.5, size = 1) +
labs(title = "Western Mediterranean basin") +
lims(x = c(-20, 20), y = c(24, 48)) +
theme_void()
Please note that shapes used in GIFT are unprojected
(Geographic Coordinate System WGS84), and that all shapefiles
provided should be in this CRS. You can check the coordinate
reference system of a sf object by using sf::st_crs().
1.2. Main arguments
Now that we have a shape for the region of interest, let’s call
GIFT_checklists(). This wrapper function has many
arguments, which we detail in this subsection.
First, the taxonomic
group of interest. We may be interested in a particular group of plants,
say only Angiosperms. In this case, we would set the
taxon_name argument like this
taxon_name = "Angiospermae". If we are interested in a
particular family of plants, let’s say orchids, then
taxon_name = "Orchidaceae".
To see all the options for
the taxon_name argument, you can run the
GIFT_taxonomy() function and look at the
taxon_name column of its output.
Along with this first
argument comes complete_taxon. This argument, set to TRUE
by default, determines whether only regions represented by checklists in
GIFT that completely cover the taxon of interest should
be retrieved. Figure 1 illustrates the principle.
In Figure 1, we want to retrieve checklists of Angiosperms. In
the first available region, region A, only one checklist is of interest.
This checklist is then always retrieved. In region B, there is only one
checklist of orchids, which is only a subset of Angiosperms. If
complete_taxon is set to TRUE, then this
checklist won’t be retrieved, otherwise yes. Finally, in region C, there
is a checklist for vascular plants and one for orchids. In both cases,
the checklist of vascular plants will be retrieved after filtering out
the non-angiosperm species. The checklist of Orchids is also retrieved
in both cases because it is not the only one available and because it
can complete the floristic knowledge for Angiosperms in this region.
The following arguments of GIFT_checklists()
refer to the floristic status of plant species. For example, we may be
interested only in endemic or naturalized species. The default value is
to get all native species.
Similarly, two arguments are needed in
the function. First, floristic_group defines the group of
interest. Second, complete_floristic indicates whether or
not to retrieve incomplete regions with respect to the selected
floristic group. The logic is detailed in Figure 2 and is similar to the
complete_taxon argument shown above
The next set of arguments relate to the spatial match between the
desired area and the GIFT database.
The main argument in this regard, when providing a shapefile or a set
of coordinates, is the overlap argument. This argument can
take 4 options, each of which produces different result, as shown in
Figure 3.
In Figure3, the GIFT polygons shown in orange either intersect, fall
inside or outside the provided shape file. The overlap argument below
each GIFT polygon illustrates in which situation a given GIFT polygon
will or will not be retrieved.
Another important spatial feature we provide is the possibility to
remove overlapping polygons. In fact, for many regions of the world,
there are several polygons in the GIFT database that cover them. If
overlapping polygons are not an issue for your case study, you can
simply set remove_overlap to FALSE (top right part of
Figure 4). However, if you want to have only one polygon per region, you
can set remove_overlap to TRUE. In this case,
the GIFT_checklists() will either retrieve the smaller or
the larger polygon. This depends on the values set for the
area_threshold_mainland argument as shown in Figure 4.
area_threshold_mainland takes a value in \(km^2\). If the area of the smaller polygon
is less than the threshold, then the larger overlapping polygon is
retrieved (lower left part in Figure 4). If the smaller polygon exceeds
the threshold, then it is retrieved (lower right part of Figure 4).
There is a similar argument for islands,
area_threshold_island, which is set to 0 \(km^2\) by default. This way the smaller
islands are always retrieved by default.
Note also that
polygons are considered to overlap if they exceed a certain percentage
of overlap. This percentage can be modified using the
overlap_threshold argument (Figure 5). This argument is set
by default to 10%.
1.3. GIFT_checklists()
Now that we have covered the main arguments of
GIFT_checklists(), we can retrieve plant checklists for the
Mediterranean region. GIFT_checklists() returns a list with
two elements. First the metadata of the checklists matching the
different criteria, named $lists. The second element is a
data.frame of all the checklists with the species
composition per checklist ($checklists).
If you only
want to retrieve the metadata, you can set the
list_set_only argument to TRUE.
ex_meta <- GIFT_checklists(taxon_name = "Angiospermae",
shp = western_mediterranean,
overlap = "centroid_inside",
list_set_only = TRUE)
And to retrieve the species composition:
medit <- GIFT_checklists(taxon_name = "Angiospermae",
complete_taxon = TRUE,
floristic_group = "native",
complete_floristic = TRUE,
geo_type = "All",
shp = western_mediterranean,
overlap = "centroid_inside",
remove_overlap = FALSE,
taxonomic_group = TRUE) # this argument adds two
# columns to the checklist: plant family and taxonomic group of each species
We can now have an estimation on the number of checklists with native
Angiosperm species in the western part of the Mediterranean basin, as
well as of the number of species.
# Number of references covered
length(unique(medit[[2]]$ref_ID))
# 22 references
# Number of checklists covered (one reference can have several lists inside)
length(unique(medit[[2]]$list_ID))
# 115 checklists
# Number of species
length(unique(medit[[2]]$work_species))
# 12840 plant species
You can now apply different values for the arguments detailed
above. As you can see, the number of checklists retrieved decreases as
you become stricter on some criteria. For example, when removing
overlapping regions:
medit_no_overlap <- GIFT_checklists(shp = western_mediterranean,
overlap = "centroid_inside",
taxon_name = "Angiospermae",
remove_overlap = TRUE)
# Number of references covered
length(unique(medit[[2]]$ref_ID)) # 23 references
length(unique(medit_no_overlap[[2]]$ref_ID)) # 22 references
Note that the function not only works with a shape file but can
accept a set of coordinates. The example below illustrates a case where
you want to retrieve GIFT checklists that intersect the coordinates of
Göttingen.
custom_point <- cbind(9.9, 51) # coordinates of Göttingen
got <- GIFT_checklists(coordinates = custom_point,
overlap = "extent_intersect",
taxon_name = "Angiospermae",
remove_overlap = TRUE,
list_set_only = TRUE)
To cite properly the references retrieved, you can run the function
GIFT_references() and look for the column
ref_long. The column geo_entity_ref associates
each reference to a name.
1.4. Species richness map
Once we have downloaded a set of checklists, it is possible to map
the species richness of the taxonomic group of interest. To do this, we
use a combination of two functions: GIFT_richness() which
returns either species richness or trait coverage per polygon, and
GIFT_shapes() which returns the shapefile of a list of GIFT
polygons.
The next two chunks illustrate this for the Angiosperms
in the World and in the Western part of the Mediterranean basin.
gift_shapes <- GIFT_shapes() # retrieves all shapefiles by default
angio_rich <- GIFT_richness(taxon_name = "Angiospermae")
rich_map <- dplyr::left_join(gift_shapes, angio_rich, by = "entity_ID") %>%
dplyr::filter(stats::complete.cases(total))
ggplot(world) +
geom_sf(color = "gray50") +
geom_sf(data = rich_map, aes(fill = total + 1)) +
scale_fill_viridis_c("Species number\n(log-transformed)", trans = "log10",
labels = scales::number_format(accuracy = 1)) +
labs(title = "Angiosperms", subtitle = "Projection EckertIV") +
coord_sf(crs = eckertIV) +
theme_void()
By customizing the code above, you can also produce a nicer
map:
Below is the R code to produce the above map if
interested.
Fancier code
# Background box
xmin <- st_bbox(world)[["xmin"]]; xmax <- st_bbox(world)[["xmax"]]
ymin <- st_bbox(world)[["ymin"]]; ymax <- st_bbox(world)[["ymax"]]
bb <- sf::st_union(sf::st_make_grid(st_bbox(c(xmin = xmin,
xmax = xmax,
ymax = ymax,
ymin = ymin),
crs = st_crs(4326)),
n = 100))
# Equator line
equator <- st_linestring(matrix(c(-180, 0, 180, 0), ncol = 2, byrow = TRUE))
equator <- st_sfc(equator, crs = st_crs(world))
# Color code from Barthlott 2007
hexcode_barthlott2007 <- c("#fbf9ed", "#f3efcc", "#f6e39e", "#cbe784",
"#65c66a", "#0e8d4a", "#4a6fbf",
"#b877c2", "#f24dae", "#ed1c24")
ggplot(world) +
geom_sf(data = bb, fill = "aliceblue") +
geom_sf(data = equator, color = "gray50", linetype = "dashed",
linewidth = 0.1) +
geom_sf(data = world_countries, fill = "antiquewhite1", color = NA) +
geom_sf(color = "gray50", linewidth = 0.1) +
geom_sf(data = bb, fill = NA) +
geom_sf(data = rich_map,
aes(fill = ifelse(rich_map$entity_class %in%
c("Island/Mainland", "Mainland",
"Island Group", "Island Part"),
total + 1, NA)),
size = 0.1) +
geom_point(data = rich_map,
aes(color = ifelse(rich_map$entity_class %in%
c("Island"),
total + 1, NA),
geometry = geometry),
stat = "sf_coordinates", size = 1, stroke = 0.5) +
scale_color_gradientn(
"Species number", trans = "log10", limits = c(1, 40000),
colours = hexcode_barthlott2007,
breaks = c(1, 10, 100, 1000, 10000, 40000),
labels = c(1, 10, 100, 1000, 10000, 40000),
na.value = "transparent") +
scale_fill_gradientn(
"Species number", trans = "log10", limits = c(1, 40000),
colours = hexcode_barthlott2007,
breaks = c(1, 10, 100, 1000, 10000, 40000),
labels = c(1, 10, 100, 1000, 10000, 40000),
na.value = "transparent") +
labs(title = "Angiosperms", subtitle = "Projection EckertIV") +
coord_sf(crs = eckertIV) +
theme_void()
We can also produce maps of richness at intermediate scales. Here is
the code and the map of Angiosperms in the Western Mediterranean
basin.
med_shape <- gift_shapes[which(gift_shapes$entity_ID %in%
unique(medit[[2]]$entity_ID)), ]
med_rich <- angio_rich[which(angio_rich$entity_ID %in%
unique(medit[[2]]$entity_ID)), ]
med_map <- dplyr::left_join(med_shape, med_rich, by = "entity_ID") %>%
dplyr::filter(stats::complete.cases(total))
ggplot(world) +
geom_sf(color = "gray50") +
geom_sf(data = western_mediterranean,
fill = "darkblue", color = "black", alpha = 0.1, size = 1) +
geom_sf(data = med_map, aes(fill = total)) +
scale_fill_viridis_c("Species number") +
labs(title = "Angiosperms in the Western Mediterranean basin") +
lims(x = c(-20, 20), y = c(24, 48)) +
theme_void()
