1. Versions and metadata for checklists

In all the functions from the package, there is a version argument. This argument allows for retrieving different instances of the GIFT database and therefore make all previous studies using the GIFT database reproducible. For example, the version used in Weigelt et al. (2020) is "1.0". To get more information about the content of the different versions, you can go here and click on the tab Version Log.

To access all the available versions of the database, you can run the following function:

versions <- GIFT_versions()
kable(versions, "html") %>%
  kable_styling(full_width = FALSE)

The column version of this table is the one to use when you want to retrieve past versions of the GIFT database. By default, the argument used is GIFT_version = "latest" which leads to the current latest stable version of the database (“2.0” in October 2022).

The function GIFT_lists() can be run to retrieve metadata about the GIFT checklists. In the next chunk, we call it with different values for the GIFT_version argument.

list_latest <- GIFT_lists(GIFT_version = "latest") # default value
list_1 <- GIFT_lists(GIFT_version = "1.0")

The number of checklists available was 3122 in the version 1.0 and equals 4475 in the version 2.0.

2. References

When using GIFT database in a research article, it is a good practice to cite the references used, and list them in an Appendix. The following function retrieves the reference for each checklist, as well as some metadata. References are documented in the column ref_long.

ref <- GIFT_references()
ref <- ref[which(ref$ref_ID %in% c(22, 10333, 10649)),
           c("ref_ID", "ref_long", "geo_entity_ref")]

# 3 first rows of that table
kable(ref, "html") %>%
  kable_styling(full_width = FALSE)

3. Checklist data

The main wrapper function to retrieve checklists and their species composition is GIFT_checklists() but you also have the possibility to retrieve individual checklists using GIFT_checklists_raw(). You would need to know the identification number list_ID of the checklists you want to retrieve.
To quickly see all the list_ID available in the database, you can run GIFT_lists() as shown in Section 1.

When calling GIFT_checklists_raw(), you can set the argument namesmatched to TRUE in order to get extra columns informing about the taxonomic harmonization that was performed when uploading the list to GIFT database.

listID_1 <- GIFT_checklists_raw(list_ID = c(11926))
listID_1_tax <- GIFT_checklists_raw(list_ID = c(11926), namesmatched = TRUE)

ncol(listID_1) # 16 columns
ncol(listID_1_tax) # 33 columns
length(unique(listID_1$work_ID)); length(unique(listID_1_tax$orig_ID))

In the list we called, you can see that we “lost” some species after taxonomic harmonization as we went from 1331 in the source to 1106 after the taxonomic harmonization. This means that several species were considered as synonyms or unknown plant species in the taxonomic backbone used for harmonization.

Note: the service mainly used to taxonomically harmonize the species’ names was The Plant List up to version 2.0 and World checklist of Vascular Plants afterwards.

4. Spatial subset

In the main vignette, we illustrated how to retrieve checklists that were falling into a provided shapefile, using the western Mediterranean basin provided with the GIFT R package.

data("western_mediterranean")

We here provide more details on the different values the overlap argument can take, using the function GIFT_spatial(). The following figure illustrates how this argument works:

Figure 1. GIFT spatial

We now illustrate this by retrieving checklists falling in the western Mediterranean basin using the four options available.

med_centroid_inside  <- GIFT_spatial(shp = western_mediterranean,
                                     overlap = "centroid_inside")
med_extent_intersect <- GIFT_spatial(shp = western_mediterranean,
                                     overlap = "extent_intersect")
med_shape_intersect <- GIFT_spatial(shp = western_mediterranean,
                                    overlap = "shape_intersect")
med_shape_inside <- GIFT_spatial(shp = western_mediterranean,
                                 overlap = "shape_inside")

length(unique(med_extent_intersect$entity_ID))
length(unique(med_shape_intersect$entity_ID))
length(unique(med_centroid_inside$entity_ID))
length(unique(med_shape_inside$entity_ID))

We here see that we progressively lose lists as we apply more selective criterion on the spatial overlap. The most restrictive option being overlap = "shape_inside" with 72 regions, then overlap = "centroid_inside" with 84 regions, overlap = "shape_intersect" with 104 regions and finally the less restrictive one being overlap = "extent_intersect" with 108 regions.
Using the functions GIFT_shapes() and calling it for the entity_IDs retrieved in each instance, we can download the shape files for each region.

geodata_extent_intersect <- GIFT_shapes(med_extent_intersect$entity_ID)

geodata_shape_inside <-
  geodata_extent_intersect[which(geodata_extent_intersect$entity_ID %in%
                                   med_shape_inside$entity_ID), ]
geodata_centroid_inside <-
  geodata_extent_intersect[which(geodata_extent_intersect$entity_ID %in%
                                   med_centroid_inside$entity_ID), ]
geodata_shape_intersect <-
  geodata_extent_intersect[which(geodata_extent_intersect$entity_ID %in%
                                   med_shape_intersect$entity_ID), ]

And then make a map.

par_overlap <- par(mfrow = c(2, 2), mai = c(0, 0, 0.5, 0))
plot(sf::st_geometry(geodata_shape_inside),
     col = geodata_shape_inside$entity_ID,
     main = paste("shape inside\n",
                  length(unique(med_shape_inside$entity_ID)),
                  "polygons"))
plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)

plot(sf::st_geometry(geodata_centroid_inside),
     col = geodata_centroid_inside$entity_ID,
     main = paste("centroid inside\n",
                  length(unique(med_centroid_inside$entity_ID)),
                  "polygons"))
points(geodata_centroid_inside$point_x, geodata_centroid_inside$point_y)
plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)

plot(sf::st_geometry(geodata_shape_intersect),
     col = geodata_shape_intersect$entity_ID,
     main = paste("shape intersect\n",
                  length(unique(med_shape_intersect$entity_ID)),
                  "polygons"))
plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)

plot(sf::st_geometry(geodata_extent_intersect),
     col = geodata_extent_intersect$entity_ID,
     main = paste("extent intersect\n",
                  length(unique(med_extent_intersect$entity_ID)),
                  "polygons"))
plot(sf::st_geometry(western_mediterranean), lwd = 2, add = TRUE)
par(par_overlap)

5. Remove overlapping regions

GIFT comprises many polygons and for some regions, there are several polygons overlapping. How to remove overlapping polygons and the associated parameters are two things detailed in the main vignette. We here provide further details:

length(med_shape_inside$entity_ID)

## [1] 72

length(GIFT_no_overlap(med_shape_inside$entity_ID, area_threshold_island = 0,
                       area_threshold_mainland = 100, overlap_threshold = 0.1))

## [1] 53

# The following polygons are overlapping:
GIFT_no_overlap(med_shape_inside$entity_ID, area_threshold_island = 0,
                area_threshold_mainland = 100, overlap_threshold = 0.1)

##  [1]   145   146   147   148   149   150   151   414   415   416   417   547
## [13]   548   549   550   551   552   586   591   592   736   738   739 10001
## [25] 10072 10104 10184 10303 10422 10430 10978 11029 11030 11031 11033 11035
## [37] 11038 11039 11042 11044 11045 11046 11434 11474 11477 11503 12231 12232
## [49] 12233 12632 12633 12634 12635

# Example of two overlapping polygons: Spain mainland and Andalusia
overlap_shape <- GIFT_shapes(entity_ID = c(10071, 12078))

par_overlap_shp <- par(mfrow = c(1, 1))
plot(sf::st_geometry(overlap_shape),
     col = c(rgb(red = 1, green = 0, blue = 0, alpha = 0.5),
             rgb(red = 0, green = 0, blue = 1, alpha = 0.3)),
     lwd = c(2, 1),
     main = "Overlapping polygons")

par(par_overlap_shp)

GIFT_no_overlap(c(10071, 12078), area_threshold_island = 0,
                area_threshold_mainland = 100, overlap_threshold = 0.1)

## [1] 12078

GIFT_no_overlap(c(10071, 12078), area_threshold_island = 0,
                area_threshold_mainland = 100000, overlap_threshold = 0.1)

## [1] 10071

ex <- GIFT_checklists(taxon_name = "Tracheophyta", by_ref_ID = FALSE,
                      list_set_only = TRUE)
ex2 <- GIFT_checklists(taxon_name = "Tracheophyta",
                       remove_overlap = TRUE, by_ref_ID = TRUE,
                       list_set_only = TRUE)
ex3 <- GIFT_checklists(taxon_name = "Tracheophyta",
                       remove_overlap = TRUE, by_ref_ID = FALSE,
                       list_set_only = TRUE)

length(unique(ex$lists$ref_ID))
length(unique(ex2$lists$ref_ID))
length(unique(ex3$lists$ref_ID))

unique(ex2$lists$ref_ID)[!(unique(ex2$lists$ref_ID) %in%
                             unique(ex3$lists$ref_ID))] # 25 references

# Pilbara region Australy and overlapping shapes
pilbara <- GIFT_shapes(entity_ID = c(10043, 12172, 11398, 11391, 10918))

ggplot(pilbara) +
  geom_sf(aes(fill = as.factor(entity_ID)), alpha = 0.5) +
  scale_fill_brewer("entity_ID", palette = "Set1")

6. Species

All the plant species present in GIFT database can be retrieved using GIFT_species().

species <- GIFT_species()

To add additional information, like their order or family, we can call GIFT_taxgroup().

# Add Family
species$Family <- GIFT_taxgroup(
  as.numeric(species$work_ID), taxon_lvl = "family", return_ID = FALSE, 
  species = species)

Order or higher levels can also be retrieved.

GIFT_taxgroup(as.numeric(species$work_ID[1:5]), taxon_lvl = "order",
              return_ID = FALSE)
GIFT_taxgroup(as.numeric(species$work_ID[1:5]),
              taxon_lvl = "higher_lvl", return_ID = FALSE,
              species = species)

As said above, plant species names can vary from the original sources they come from to the final work_species name they get, due to the taxonomic harmonization procedure. Looking-up for a species and the different steps of the taxonomic harmonization is possible with the function GIFT_species_lookup().

Fagus <- GIFT_species_lookup(genus = "Fagus", epithet = "sylvatica",
                             namesmatched = TRUE)

In this table, we can see that the first entry Fagus silvatica was later converted as the accepted name Fagus sylvatica.

7. Taxonomy

The taxonomy used in GIFT database can be downloaded using GIFT_taxonomy().

taxo <- GIFT_taxonomy()

8. Overlap_GloNAF tables (and others)

As other global databases of plant diversity exist and may rely on different polygons, we provide a function GIFT_overlap() than can look at the spatial overlap between GIFT polygons and polygons coming from other databases.
So far, only two resources are available: glonaf and gmba. glonaf stands for Global Naturalized Alien Flora and gmba for Global Mountain Biodiversity Assessment.

GIFT_overlap() returns the spatial overlap in percentage for each pairwise combination of polygons between GIFT and the other resource.

glonaf <- GIFT_overlap(resource = "glonaf")

kable(glonaf[1:5, ], "html") %>%
  kable_styling(full_width = FALSE)

gmba <- GIFT_overlap(resource = "gmba")

kable(gmba[1:5, ], "html") %>%
  kable_styling(full_width = FALSE)

References

Weigelt, P., König, C., & Kreft, H. (2020). GIFT – A Global Inventory of Floras and Traits for macroecology and biogeography. Journal of Biogeography, 47(1), 16–43.