3.1 Examples with one species

We need to define both column genus and species as characters before we use them on the gbif function. They were pre-defined as factors.

Since we don’t need all the information extracted from gbif, here we will select only the species ,lat ,lon ,fullCountry variables

Hyracoidea$genus<-as.character(Hyracoidea$genus)
Hyracoidea$species<-as.character(Hyracoidea$species)

Hyracoidea_sp1<-gbif(Hyracoidea$genus[1],Hyracoidea$species[1])[c("species","lat","lon","fullCountry")]

3.2 Traditional For loops

gbif_records<-NULL ## 1. output

for (i in 1:length(Hyracoidea$Binomial)){   ## 2. Sequence
  
  ## 3. Body
  Hyracoidea_sp<-gbif(Hyracoidea$genus[i],Hyracoidea$species[i])[c("species","lat","lon","fullCountry")]
  gbif_records<-rbind(gbif_records,Hyracoidea_sp)
  
}

3.3 For loops using the function foreach

gbif_records <-
  foreach(i=1:length(Hyracoidea$Binomial),.combine = rbind)%do%{
    
    gbif(Hyracoidea$genus[i],Hyracoidea$species[i])[c("species","lat","lon","fullCountry")]
    
  }

Before merging our GBIF record into the life-history trait data frame, remember to clean your geographic data

## Cleaning records
gbif_records<-
  gbif_records %>% 
  dplyr::filter(!is.na(lon)&!is.na(lat))

## Get rid of duplicate occurrences
dups=duplicated(gbif_records[, c("lon", "lat")])
gbif_records <-gbif_records[!dups, ]


## Merging data frames
Hyracoidea_df<-merge(Hyracoidea,gbif_records, by.x="Binomial", by.y="species")

4.1 Download the WWF ecoregions data

To access these maps, you can either download the data from the WWF website or through the function WwfLoad() from the R package speciesgeocodeR

To use the WwfLoad() function, you only need to specify the directory path where you want the data to be store.

wwf_eco <-WwfLoad(x = "./data/spatial/")

The WWF ecoregions are stored in a vector format, which represent discrete geometric locations (x,y values) known as vertices that define the shape of the spatial objects (e.g., points, lines and polygons). Here, the ecoregions and biomes are represented by spatial polygons which are usually stored in shapefiles. There are several functions in R that allows you to read shapefiles. One is using the function shapefile from the raster package or the readOGR function from the rgdal package

require(raster)
wwf_eco<-shapefile("./data/spatial/WWF_ecoregions/official/wwf_terr_ecos.shp")

require(rgdal)
## readOGR("path","layerName") 
wwf_eco <- readOGR("./data/spatial/WWF_ecoregions/official/",
                            "wwf_terr_ecos")

## OGR data source with driver: ESRI Shapefile 
## Source: "/Users/echeverrialondono1/Desktop/Loops_shapefile/data/spatial/WWF_ecoregions/official", layer: "wwf_terr_ecos"
## with 14458 features
## It has 21 fields

ATTENTION: Please include the path of the directory where you store your spatial data into the .gitignore file to avoid problems with Github when you push your commits!

Like rasters, shapefiles are geospatial objects, and therefore they also include spatial data attributes such as Coordinate Reference System (CRS) and extent

class(wwf_eco)

## [1] "SpatialPolygonsDataFrame"
## attr(,"package")
## [1] "sp"

crs(wwf_eco)

## CRS arguments:
##  +proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0

extent(wwf_eco)

## class       : Extent 
## xmin        : -180 
## xmax        : 180 
## ymin        : -89.89197 
## ymax        : 83.62313

4.2 Extract information from the Spatial polygons

In this exercise, we want to extract the information about the realms and biomes that fall on each of our species occurrences. To do that, we first need to convert our occurrence data into a spatial data frame, and second, we need to make sure our spatial data frame has the same projection and coordinate system as the spatial polygons.

# 1. Convert to spatial data frame
Hyracoidea_geo<-Hyracoidea_df
coordinates(Hyracoidea_geo)<-~lon + lat

# 2. Make sure both files have the same projection
proj4string(Hyracoidea_geo)<-proj4string(wwf_eco)

Now we can extract the information using the function over from the sp r package. This function overlay the ecoregions and biomes polygons on our spatial data frame and then retrieves the attributes for each occurrence.

biome_data<-over(Hyracoidea_geo,wwf_eco)[c("REALM","BIOME","G200_REGIO")]

# Include these three columns into our original data frame
Hyracoidea_df<-cbind(Hyracoidea_df,biome_data)
Hyracoidea_df<-droplevels(Hyracoidea_df)

kable(head(Hyracoidea_df[,c("lat","lon","fullCountry","REALM","BIOME","G200_REGIO")]))

As you can see, the BIOMES levels are represented by numbers. To look at the numbers equivalence, we need to check the metadata of the WWF biomes. In our example, 7 represents Tropical & Subtropical Grasslands, Savannas & Shrublands. and 1 Tropical & Subtropical Moist Broadleaf Forests. In the metadata, you can also check the meaning of each REALM abbreviation (e.g., AT is equivalent to Afrotropics).